The computer graphics interface

CERN Document Server

Steinbrugge Chauveau, Karla; Niles Reed, Theodore; Shepherd, B

2014-01-01

The Computer Graphics Interface provides a concise discussion of computer graphics interface (CGI) standards. The title is comprised of seven chapters that cover the concepts of the CGI standard. Figures and examples are also included. The first chapter provides a general overview of CGI; this chapter covers graphics standards, functional specifications, and syntactic interfaces. Next, the book discusses the basic concepts of CGI, such as inquiry, profiles, and registration. The third chapter covers the CGI concepts and functions, while the fourth chapter deals with the concept of graphic obje

Optimal design method for a digital human–computer interface based on human reliability in a nuclear power plant. Part 3: Optimization method for interface task layout

International Nuclear Information System (INIS)

Jiang, Jianjun; Wang, Yiqun; Zhang, Li; Xie, Tian; Li, Min; Peng, Yuyuan; Wu, Daqing; Li, Peiyao; Ma, Congmin; Shen, Mengxu; Wu, Xing; Weng, Mengyun; Wang, Shiwei; Xie, Cen

2016-01-01

Highlights: • The authors present an optimization algorithm for interface task layout. • The performing process of the proposed algorithm was depicted. • The performance evaluation method adopted neural network method. • The optimization layouts of an event interface tasks were obtained by experiments. - Abstract: This is the last in a series of papers describing the optimal design for a digital human–computer interface of a nuclear power plant (NPP) from three different points based on human reliability. The purpose of this series is to propose different optimization methods from varying perspectives to decrease human factor events that arise from the defects of a human–computer interface. The present paper mainly solves the optimization method as to how to effectively layout interface tasks into different screens. The purpose of this paper is to decrease human errors by reducing the distance that an operator moves among different screens in each operation. In order to resolve the problem, the authors propose an optimization process of interface task layout for digital human–computer interface of a NPP. As to how to automatically layout each interface task into one of screens in each operation, the paper presents a shortest moving path optimization algorithm with dynamic flag based on human reliability. To test the algorithm performance, the evaluation method uses neural network based on human reliability. The less the human error probabilities are, the better the interface task layouts among different screens are. Thus, by analyzing the performance of each interface task layout, the optimization result is obtained. Finally, the optimization layouts of spurious safety injection event interface tasks of the NPP are obtained by an experiment, the proposed methods has a good accuracy and stabilization.

A Conceptual Architecture for Adaptive Human-Computer Interface of a PT Operation Platform Based on Context-Awareness

Directory of Open Access Journals (Sweden)

Qing Xue

2014-01-01

Full Text Available We present a conceptual architecture for adaptive human-computer interface of a PT operation platform based on context-awareness. This architecture will form the basis of design for such an interface. This paper describes components, key technologies, and working principles of the architecture. The critical contents covered context information modeling, processing, relationship establishing between contexts and interface design knowledge by use of adaptive knowledge reasoning, and visualization implementing of adaptive interface with the aid of interface tools technology.

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

Science.gov (United States)

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Computational design of patterned interfaces using reduced order models

International Nuclear Information System (INIS)

Vattre, A.J.; Abdolrahim, N.; Kolluri, K.; Demkowicz, M.J.

2014-01-01

Patterning is a familiar approach for imparting novel functionalities to free surfaces. We extend the patterning paradigm to interfaces between crystalline solids. Many interfaces have non-uniform internal structures comprised of misfit dislocations, which in turn govern interface properties. We develop and validate a computational strategy for designing interfaces with controlled misfit dislocation patterns by tailoring interface crystallography and composition. Our approach relies on a novel method for predicting the internal structure of interfaces: rather than obtaining it from resource-intensive atomistic simulations, we compute it using an efficient reduced order model based on anisotropic elasticity theory. Moreover, our strategy incorporates interface synthesis as a constraint on the design process. As an illustration, we apply our approach to the design of interfaces with rapid, 1-D point defect diffusion. Patterned interfaces may be integrated into the microstructure of composite materials, markedly improving performance. (authors)

An efficient ERP-based brain-computer interface using random set presentation and face familiarity.

Directory of Open Access Journals (Sweden)

Seul-Ki Yeom

Full Text Available Event-related potential (ERP-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC-based paradigm with our approach that combines a random set presentation paradigm with (non- self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.

A cell-phone-based brain-computer interface for communication in daily life

Science.gov (United States)

Wang, Yu-Te; Wang, Yijun; Jung, Tzyy-Ping

2011-04-01

Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design.

Science.gov (United States)

Pevzner, Yuri; Frugier, Emilie; Schalk, Vinushka; Caflisch, Amedeo; Woodcock, H Lee

2014-09-22

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

Flexusi Interface Builder For Computer Based Accelerator Monitoring And Control System

CERN Document Server

Kurakin, V G; Kurakin, P V

2004-01-01

We have developed computer code for any desired graphics user interface designing for monitoring and control system at the executable level. This means that operator can build up measurement console consisting of virtual devices before or even during real experiment without recompiling source file. Such functionality results in number of advantages comparing with traditional programming. First of all any risk disappears to introduce bug into source code. Another important thing is the fact the both program developers and operator staff do not interface in developing ultimate product (measurement console). Thus, small team without detailed project can design even very complicated monitoring and control system. For the reason mentioned below, approach suggested is especially helpful for large complexes to be monitored and control, accelerator being among them. The program code consists of several modules, responsible for data acquisition, control and representation. Borland C++ Builder technologies based on VCL...

Quantum neural network-based EEG filtering for a brain-computer interface.

Science.gov (United States)

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

OpenAIRE

Parth Gargava; Krishna Asawa

2017-01-01

A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command naviga...

A Brain Computer Interface for Robust Wheelchair Control Application Based on Pseudorandom Code Modulated Visual Evoked Potential

DEFF Research Database (Denmark)

Mohebbi, Ali; Engelsholm, Signe K.D.; Puthusserypady, Sadasivan

2015-01-01

In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code...

Tutorial: Signal Processing in Brain-Computer Interfaces

NARCIS (Netherlands)

Garcia Molina, G.

2010-01-01

Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential

HCIDL: Human-computer interface description language for multi-target, multimodal, plastic user interfaces

Directory of Open Access Journals (Sweden)

Lamia Gaouar

2018-06-01

Full Text Available From the human-computer interface perspectives, the challenges to be faced are related to the consideration of new, multiple interactions, and the diversity of devices. The large panel of interactions (touching, shaking, voice dictation, positioning … and the diversification of interaction devices can be seen as a factor of flexibility albeit introducing incidental complexity. Our work is part of the field of user interface description languages. After an analysis of the scientific context of our work, this paper introduces HCIDL, a modelling language staged in a model-driven engineering approach. Among the properties related to human-computer interface, our proposition is intended for modelling multi-target, multimodal, plastic interaction interfaces using user interface description languages. By combining plasticity and multimodality, HCIDL improves usability of user interfaces through adaptive behaviour by providing end-users with an interaction-set adapted to input/output of terminals and, an optimum layout. Keywords: Model driven engineering, Human-computer interface, User interface description languages, Multimodal applications, Plastic user interfaces

The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.

Science.gov (United States)

Brumberg, Jonathan S; Lorenz, Sean D; Galbraith, Byron V; Guenther, Frank H

2012-01-01

In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software "app" development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50-60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.

APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO HUMAN-COMPUTER INTERFACES

DEFF Research Database (Denmark)

Sonnenwald, Diane H.

1988-01-01

A description is given of UIMS (User Interface Management System), a system using a variety of artificial intelligence techniques to build knowledge-based user interfaces combining functionality and information from a variety of computer systems that maintain, test, and configure customer telephone...... and data networks. Three artificial intelligence (AI) techniques used in UIMS are discussed, namely, frame representation, object-oriented programming languages, and rule-based systems. The UIMS architecture is presented, and the structure of the UIMS is explained in terms of the AI techniques....

Integrated computer network high-speed parallel interface

International Nuclear Information System (INIS)

Frank, R.B.

1979-03-01

As the number and variety of computers within Los Alamos Scientific Laboratory's Central Computer Facility grows, the need for a standard, high-speed intercomputer interface has become more apparent. This report details the development of a High-Speed Parallel Interface from conceptual through implementation stages to meet current and future needs for large-scle network computing within the Integrated Computer Network. 4 figures

A brain-computer interface controlled mail client.

Science.gov (United States)

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Wang, Cong

2013-01-01

In this paper, we propose a brain-computer interface (BCI) based mail client. This system is controlled by hybrid features extracted from scalp-recorded electroencephalographic (EEG). We emulate the computer mouse by the motor imagery-based mu rhythm and the P300 potential. Furthermore, an adaptive P300 speller is included to provide text input function. With this BCI mail client, users can receive, read, write mails, as well as attach files in mail writing. The system has been tested on 3 subjects. Experimental results show that mail communication with this system is feasible.

Brain-computer interface based on intermodulation frequency

Science.gov (United States)

Chen, Xiaogang; Chen, Zhikai; Gao, Shangkai; Gao, Xiaorong

2013-12-01

Objective. Most recent steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have used a single frequency for each target, so that a large number of targets require a large number of stimulus frequencies and therefore a wider frequency band. However, human beings show good SSVEP responses only in a limited range of frequencies. Furthermore, this issue is especially problematic if the SSVEP-based BCI takes a PC monitor as a stimulator, which is only capable of generating a limited range of frequencies. To mitigate this issue, this study presents an innovative coding method for SSVEP-based BCI by means of intermodulation frequencies. Approach. Simultaneous modulations of stimulus luminance and color at different frequencies were utilized to induce intermodulation frequencies. Luminance flickered at relatively large frequency (10, 12, 15 Hz), while color alternated at low frequency (0.5, 1 Hz). An attractive feature of the proposed method was that it would substantially increase the number of targets at a single flickering frequency by altering color modulated frequencies. Based on this method, the BCI system presented in this study realized eight targets merely using three flickering frequencies. Main results. The online results obtained from 15 subjects (14 healthy and 1 with stroke) revealed that an average classification accuracy of 93.83% and information transfer rate (ITR) of 33.80 bit min-1 were achieved using our proposed SSVEP-based BCI system. Specifically, 5 out of the 15 subjects exhibited an ITR of 40.00 bit min-1 with a classification accuracy of 100%. Significance. These results suggested that intermodulation frequencies could be adopted as steady responses in BCI, for which our system could be used as a practical BCI system.

[The P300-based brain-computer interface: presentation of the complex "flash + movement" stimuli].

Science.gov (United States)

Ganin, I P; Kaplan, A Ia

2014-01-01

The P300 based brain-computer interface requires the detection of P300 wave of brain event-related potentials. Most of its users learn the BCI control in several minutes and after the short classifier training they can type a text on the computer screen or assemble an image of separate fragments in simple BCI-based video games. Nevertheless, insufficient attractiveness for users and conservative stimuli organization in this BCI may restrict its integration into real information processes control. At the same time initial movement of object (motion-onset stimuli) may be an independent factor that induces P300 wave. In current work we checked the hypothesis that complex "flash + movement" stimuli together with drastic and compact stimuli organization on the computer screen may be much more attractive for user while operating in P300 BCI. In 20 subjects research we showed the effectiveness of our interface. Both accuracy and P300 amplitude were higher for flashing stimuli and complex "flash + movement" stimuli compared to motion-onset stimuli. N200 amplitude was maximal for flashing stimuli, while for "flash + movement" stimuli and motion-onset stimuli it was only a half of it. Similar BCI with complex stimuli may be embedded into compact control systems requiring high level of user attention under impact of negative external effects obstructing the BCI control.

A computational method for sharp interface advection

DEFF Research Database (Denmark)

Roenby, Johan; Bredmose, Henrik; Jasak, Hrvoje

2016-01-01

We devise a numerical method for passive advection of a surface, such as the interface between two incompressible fluids, across a computational mesh. The method is called isoAdvector, and is developed for general meshes consisting of arbitrary polyhedral cells. The algorithm is based on the volu...

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

OpenAIRE

Raza, H; Cecotti, H; Li, Y; Prasad, G

2015-01-01

A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous mo...

The mind-writing pupil : A human-computer interface based on decoding of covert attention through pupillometry

NARCIS (Netherlands)

Mathôt, Sebastiaan; Melmi, Jean Baptiste; Van Der Linden, Lotje; Van Der Stigchel, Stefan

2016-01-01

We present a new human-computer interface that is based on decoding of attention through pupillometry. Our method builds on the recent finding that covert visual attention affects the pupillary light response: Your pupil constricts when you covertly (without looking at it) attend to a bright,

Graphical User Interface Programming in Introductory Computer Science.

Science.gov (United States)

Skolnick, Michael M.; Spooner, David L.

Modern computing systems exploit graphical user interfaces for interaction with users; as a result, introductory computer science courses must begin to teach the principles underlying such interfaces. This paper presents an approach to graphical user interface (GUI) implementation that is simple enough for beginning students to understand, yet…

PHREEQCI; a graphical user interface for the geochemical computer program PHREEQC

Science.gov (United States)

Charlton, Scott R.; Macklin, Clifford L.; Parkhurst, David L.

1997-01-01

PhreeqcI is a Windows-based graphical user interface for the geochemical computer program PHREEQC. PhreeqcI provides the capability to generate and edit input data files, run simulations, and view text files containing simulation results, all within the framework of a single interface. PHREEQC is a multipurpose geochemical program that can perform speciation, inverse, reaction-path, and 1D advective reaction-transport modeling. Interactive access to all of the capabilities of PHREEQC is available with PhreeqcI. The interface is written in Visual Basic and will run on personal computers under the Windows(3.1), Windows95, and WindowsNT operating systems.

Workshops of the Sixth International Brain–Computer Interface Meeting : brain–computer interfaces past, present, and future

NARCIS (Netherlands)

Huggins, Jane E.; Guger, Christoph; Ziat, Mounia; Zander, Thorsten O.; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K.R.; Ramsey, Nick F.; Nijholt, Anton; Müller-Putz, Gernot R.; McFarland, Dennis J.; Mattia, Donatella; Lance, Brent J.; Kindermans, Pieter-Jan; Iturrate, Iñaki; Herff, Christian; Gupta, Disha; Do, An H.; Collinger, Jennifer L.; Chavarriaga, Ricardo; Chasey, Steven M.; Bleichner, Martin G.; Batista, Aaron; Anderson, Charles W.; Aarnoutse, Erik J.

2017-01-01

The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific

A distributed, graphical user interface based, computer control system for atomic physics experiments.

Science.gov (United States)

Keshet, Aviv; Ketterle, Wolfgang

2013-01-01

Atomic physics experiments often require a complex sequence of precisely timed computer controlled events. This paper describes a distributed graphical user interface-based control system designed with such experiments in mind, which makes use of off-the-shelf output hardware from National Instruments. The software makes use of a client-server separation between a user interface for sequence design and a set of output hardware servers. Output hardware servers are designed to use standard National Instruments output cards, but the client-server nature should allow this to be extended to other output hardware. Output sequences running on multiple servers and output cards can be synchronized using a shared clock. By using a field programmable gate array-generated variable frequency clock, redundant buffers can be dramatically shortened, and a time resolution of 100 ns achieved over effectively arbitrary sequence lengths.

A distributed, graphical user interface based, computer control system for atomic physics experiments

Science.gov (United States)

Keshet, Aviv; Ketterle, Wolfgang

2013-01-01

Atomic physics experiments often require a complex sequence of precisely timed computer controlled events. This paper describes a distributed graphical user interface-based control system designed with such experiments in mind, which makes use of off-the-shelf output hardware from National Instruments. The software makes use of a client-server separation between a user interface for sequence design and a set of output hardware servers. Output hardware servers are designed to use standard National Instruments output cards, but the client-server nature should allow this to be extended to other output hardware. Output sequences running on multiple servers and output cards can be synchronized using a shared clock. By using a field programmable gate array-generated variable frequency clock, redundant buffers can be dramatically shortened, and a time resolution of 100 ns achieved over effectively arbitrary sequence lengths.

[Research of controlling of smart home system based on P300 brain-computer interface].

Science.gov (United States)

Wang, Jinjia; Yang, Chengjie

2014-08-01

Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.

Guest editorial: Brain/neuronal computer games interfaces and interaction

OpenAIRE

Coyle, D.; Principe, J.; Lotte, F.; Nijholt, Antinus

2013-01-01

Nowadays brainwave or electroencephalogram (EEG) controlled games controllers are adding new options to satisfy the continual demand for new ways to interact with games, following trends such as the Nintendo® Wii, Microsoft® Kinect and Playstation® Move which are based on accelerometers and motion capture. EEG-based brain-computer games interaction are controlled through brain-computer interface (BCI) technology which requires sophisticated signal processing to produce a low communication ban...

Brain-machine and brain-computer interfaces.

Science.gov (United States)

Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

2004-11-01

The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

Science.gov (United States)

Abu-Alqumsan, Mohammad; Ebert, Felix; Peer, Angelika

2017-06-01

Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the

SSVEP and ANN based optimal speller design for Brain Computer Interface

Directory of Open Access Journals (Sweden)

Irshad Ahmad Ansari

2015-07-01

Full Text Available This work put forwards an optimal BCI (Brain Computer Interface speller design based on Steady State Visual Evoked Potentials (SSVEP and Artificial Neural Network (ANN in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of  BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.DOI: 10.15181/csat.v2i2.1059

Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

Science.gov (United States)

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-01-01

Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

Brain-computer interfacing under distraction: an evaluation study

DEFF Research Database (Denmark)

Brandl, Stephanie; Frølich, Laura; Höhne, Johannes

2016-01-01

Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach...

Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game.

Science.gov (United States)

Leite, Harlei Miguel de Arruda; de Carvalho, Sarah Negreiros; Costa, Thiago Bulhões da Silva; Attux, Romis; Hornung, Heiko Horst; Arantes, Dalton Soares

2018-01-01

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named "Get Coins," through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.

A New Human-Machine Interfaces of Computer-based Procedure System to Reduce the Team Errors in Nuclear Power Plants

International Nuclear Information System (INIS)

Kim, Sa Kil; Sim, Joo Hyun; Lee, Hyun Chul

2016-01-01

In this study, we identify the emerging types of team errors, especially, in digitalized control room of nuclear power plants such as the APR-1400 main control room of Korea. Most works in nuclear industry are to be performed by a team of more than two persons. Even though the individual errors can be detected and recovered by the qualified others and/or the well trained team, it is rather seldom that the errors by team could be easily detected and properly recovered by the team itself. Note that the team is defined as two or more people who are appropriately interacting with each other, and the team is a dependent aggregate, which accomplishes a valuable goal. Organizational errors sometimes increase the likelihood of operator errors through the active failure pathway and, at the same time, enhance the possibility of adverse outcomes through defensive weaknesses. We incorporate the crew resource management as a representative approach to deal with the team factors of the human errors. We suggest a set of crew resource management training procedures under the unsafe environments where human errors can have devastating effects. We are on the way to develop alternative interfaces against team error in a condition of using a computer-based procedure system in a digitalized main control room. The computer-based procedure system is a representative feature of digitalized control room. In this study, we will propose new interfaces of computer-based procedure system to reduce feasible team errors. We are on the way of effectiveness test to validate whether the new interface can reduce team errors during operating with a computer-based procedure system in a digitalized control room

A New Human-Machine Interfaces of Computer-based Procedure System to Reduce the Team Errors in Nuclear Power Plants

Energy Technology Data Exchange (ETDEWEB)

Kim, Sa Kil; Sim, Joo Hyun; Lee, Hyun Chul [Korea Atomic Research Institute, Daejeon (Korea, Republic of)

2016-10-15

In this study, we identify the emerging types of team errors, especially, in digitalized control room of nuclear power plants such as the APR-1400 main control room of Korea. Most works in nuclear industry are to be performed by a team of more than two persons. Even though the individual errors can be detected and recovered by the qualified others and/or the well trained team, it is rather seldom that the errors by team could be easily detected and properly recovered by the team itself. Note that the team is defined as two or more people who are appropriately interacting with each other, and the team is a dependent aggregate, which accomplishes a valuable goal. Organizational errors sometimes increase the likelihood of operator errors through the active failure pathway and, at the same time, enhance the possibility of adverse outcomes through defensive weaknesses. We incorporate the crew resource management as a representative approach to deal with the team factors of the human errors. We suggest a set of crew resource management training procedures under the unsafe environments where human errors can have devastating effects. We are on the way to develop alternative interfaces against team error in a condition of using a computer-based procedure system in a digitalized main control room. The computer-based procedure system is a representative feature of digitalized control room. In this study, we will propose new interfaces of computer-based procedure system to reduce feasible team errors. We are on the way of effectiveness test to validate whether the new interface can reduce team errors during operating with a computer-based procedure system in a digitalized control room.

Rapid prototyping of an EEG-based brain-computer interface (BCI).

Science.gov (United States)

Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

2001-03-01

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

The Self-Paced Graz Brain-Computer Interface: Methods and Applications

Directory of Open Access Journals (Sweden)

Reinhold Scherer

2007-01-01

Full Text Available We present the self-paced 3-class Graz brain-computer interface (BCI which is based on the detection of sensorimotor electroencephalogram (EEG rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control or not (non-control state. The presented system is able to automatically reduce electrooculogram (EOG artifacts, to detect electromyographic (EMG activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

Robust Brain-Computer Interfaces

NARCIS (Netherlands)

Reuderink, B.

2011-01-01

A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Current BCIs aimed at patients require that the user invests weeks, or even months, to learn the skill to intentionally modify their brain

Optimal design methods for a digital human-computer interface based on human reliability in a nuclear power plant

International Nuclear Information System (INIS)

Jiang, Jianjun; Zhang, Li; Xie, Tian; Wu, Daqing; Li, Min; Wang, Yiqun; Peng, Yuyuan; Peng, Jie; Zhang, Mengjia; Li, Peiyao; Ma, Congmin; Wu, Xing

2017-01-01

Highlights: • A complete optimization process is established for digital human-computer interfaces of Npps. • A quick convergence search method is proposed. • The authors propose an affinity error probability mapping function to test human reliability. - Abstract: This is the second in a series of papers describing the optimal design method for a digital human-computer interface of nuclear power plant (Npp) from three different points based on human reliability. The purpose of this series is to explore different optimization methods from varying perspectives. This present paper mainly discusses the optimal design method for quantity of components of the same factor. In monitoring process, quantity of components has brought heavy burden to operators, thus, human errors are easily triggered. To solve the problem, the authors propose an optimization process, a quick convergence search method and an affinity error probability mapping function. Two balanceable parameter values of the affinity error probability function are obtained by experiments. The experimental results show that the affinity error probability mapping function about human-computer interface has very good sensitivity and stability, and that quick convergence search method for fuzzy segments divided by component quantity has better performance than general algorithm.

Brain Computer Interfaces for Enhanced Interaction with Mobile Robot Agents

Science.gov (United States)

2016-07-27

SECURITY CLASSIFICATION OF: Brain Computer Interfaces (BCIs) show great potential in allowing humans to interact with computational environments in a...Distribution Unlimited UU UU UU UU 27-07-2016 17-Sep-2013 16-Sep-2014 Final Report: Brain Computer Interfaces for Enhanced Interactions with Mobile Robot...published in peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Brain Computer Interfaces for Enhanced

Vibrotactile Feedback for Brain-Computer Interface Operation

OpenAIRE

Cincotti, Febo; Kauhanen, Laura; Aloise, Fabio; Palomäki, Tapio; Caporusso, Nicholas; Jylänki, Pasi; Mattia, Donatella; Babiloni, Fabio; Vanacker, Gerolf; Nuttin, Marnix; Marciani, Maria Grazia; Millán, José del R.

2007-01-01

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (i...

Control of a nursing bed based on a hybrid brain-computer interface.

Science.gov (United States)

Nengneng Peng; Rui Zhang; Haihua Zeng; Fei Wang; Kai Li; Yuanqing Li; Xiaobin Zhuang

2016-08-01

In this paper, we propose an intelligent nursing bed system which is controlled by a hybrid brain-computer interface (BCI) involving steady-state visual evoked potential (SSVEP) and P300. Specifically, the hybrid BCI includes an asynchronous brain switch based on SSVEP and P300, and a P300-based BCI. The brain switch is used to turn on/off the control system of the electric nursing bed through idle/control state detection, whereas the P300-based BCI is for operating the nursing bed. At the beginning, the user may focus on one group of flashing buttons in the graphic user interface (GUI) of the brain switch, which can simultaneously evoke SSVEP and P300, to switch on the control system. Here, the combination of SSVEP and P300 is used for improving the performance of the brain switch. Next, the user can control the nursing bed using the P300-based BCI. The GUI of the P300-based BCI includes 10 flashing buttons, which correspond to 10 functional operations, namely, left-side up, left-side down, back up, back down, bedpan open, bedpan close, legs up, legs down, right-side up, and right-side down. For instance, he/she can focus on the flashing button "back up" in the GUI of the P300-based BCI to activate the corresponding control such that the nursing bed is adjusted up. Eight healthy subjects participated in our experiment, and obtained an average accuracy of 93.75% and an average false positive rate (FPR) of 0.15 event/min. The effectiveness of our system was thus demonstrated.

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

Directory of Open Access Journals (Sweden)

Parth Gargava

2017-08-01

Full Text Available A Brain Computer Interface (BCI is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent.

Human-Computer Interfaces for Wearable Computers: A Systematic Approach to Development and Evaluation

OpenAIRE

Witt, Hendrik

2007-01-01

The research presented in this thesis examines user interfaces for wearable computers.Wearable computers are a special kind of mobile computers that can be worn on the body. Furthermore, they integrate themselves even more seamlessly into different activities than a mobile phone or a personal digital assistant can.The thesis investigates the development and evaluation of user interfaces for wearable computers. In particular, it presents fundamental research results as well as supporting softw...

Cost-effective computations with boundary interface operators in elliptic problems

International Nuclear Information System (INIS)

Khoromskij, B.N.; Mazurkevich, G.E.; Nikonov, E.G.

1993-01-01

The numerical algorithm for fast computations with interface operators associated with the elliptic boundary value problems (BVP) defined on step-type domains is presented. The algorithm is based on the asymptotically almost optimal technique developed for treatment of the discrete Poincare-Steklov (PS) operators associated with the finite-difference Laplacian on rectangles when using the uniform grid with a 'displacement by h/2'. The approach can be regarded as an extension of the method proposed for the partial solution of the finite-difference Laplace equation to the case of displaced grids and mixed boundary conditions. It is shown that the action of the PS operator for the Dirichlet problem and mixed BVP can be computed with expenses of the order of O(Nlog 2 N) both for arithmetical operations and computer memory needs, where N is the number of unknowns on the rectangle boundary. The single domain algorithm is applied to solving the multidomain elliptic interface problems with piecewise constant coefficients. The numerical experiments presented confirm almost linear growth of the computational costs and memory needs with respect to the dimension of the discrete interface problem. 14 refs., 3 figs., 4 tabs

fNIRS-based brain-computer interfaces: a review

Directory of Open Access Journals (Sweden)

Noman eNaseer

2015-01-01

Full Text Available A brain-computer interface (BCI is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

Electrophysiological Brain Activity during the Control of a Motor Imagery-Based Brain–Computer Interface

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Aziatskaya, G.A.; Bobrov, P.D.; Luykmanov, R. Kh.; Fedotova, I.R.; Húsek, Dušan; Snášel, V.

2017-01-01

Roč. 43, č. 5 (2017), s. 501-511 ISSN 0362-1197 Institutional support: RVO:67985807 Keywords : brain–computer interface * neurointerface * EEG * motor imagery * EEG rhythm synchronization and desynchronization * independent component analysis * EEG inverse problem * neurorehabilitation Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Implementation of an Embedded Web Server Application for Wireless Control of Brain Computer Interface Based Home Environments.

Science.gov (United States)

Aydın, Eda Akman; Bay, Ömer Faruk; Güler, İnan

2016-01-01

Brain Computer Interface (BCI) based environment control systems could facilitate life of people with neuromuscular diseases, reduces dependence on their caregivers, and improves their quality of life. As well as easy usage, low-cost, and robust system performance, mobility is an important functionality expected from a practical BCI system in real life. In this study, in order to enhance users' mobility, we propose internet based wireless communication between BCI system and home environment. We designed and implemented a prototype of an embedded low-cost, low power, easy to use web server which is employed in internet based wireless control of a BCI based home environment. The embedded web server provides remote access to the environmental control module through BCI and web interfaces. While the proposed system offers to BCI users enhanced mobility, it also provides remote control of the home environment by caregivers as well as the individuals in initial stages of neuromuscular disease. The input of BCI system is P300 potentials. We used Region Based Paradigm (RBP) as stimulus interface. Performance of the BCI system is evaluated on data recorded from 8 non-disabled subjects. The experimental results indicate that the proposed web server enables internet based wireless control of electrical home appliances successfully through BCIs.

A subject-independent pattern-based Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Andreas Markus Ray

2015-10-01

Full Text Available While earlier Brain-Computer Interface (BCI studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e. happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to match their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.

Steady State Visual Evoked Potential Based Brain-Computer Interface for Cognitive Assessment

DEFF Research Database (Denmark)

Westergren, Nicolai; Bendtsen, Rasmus L.; Kjær, Troels W.

2016-01-01

decline is important. Cognitive decline may be detected using fullyautomated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer...... interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% ± 0%. The test...... subjects achieved an information transfer rate (ITR) of 14:64 bits/min ± 7:63 bits=min and a subject test performance of 47:22% ± 34:10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system...

Control of a mobile robot through brain computer interface

Directory of Open Access Journals (Sweden)

Robinson Jimenez Moreno

2015-07-01

Full Text Available This paper poses a control interface to command the movement of a mobile robot according to signals captured from the user's brain. These signals are acquired and interpreted by Emotiv EPOC device, a 14-electrode type sensor which captures electroencephalographic (EEG signals with high resolution, which, in turn, are sent to a computer for processing. One brain-computer interface (BCI was developed based on the Emotiv software and SDK in order to command the mobile robot from a distance. Functionality tests are performed with the sensor to discriminate shift intentions of a user group, as well as with a fuzzy controller to hold the direction in case of concentration loss. As conclusion, it was possible to obtain an efficient system for robot movements by brain commands.

Engineering brain-computer interfaces: past, present and future.

Science.gov (United States)

Hughes, M A

2014-06-01

Electricity governs the function of both nervous systems and computers. Whilst ions move in polar fluids to depolarize neuronal membranes, electrons move in the solid-state lattices of microelectronic semiconductors. Joining these two systems together, to create an iono-electric brain-computer interface, is an immense challenge. However, such interfaces offer (and in select clinical contexts have already delivered) a method of overcoming disability caused by neurological or musculoskeletal pathology. To fulfill their theoretical promise, several specific challenges demand consideration. Rate-limiting steps cover a diverse range of disciplines including microelectronics, neuro-informatics, engineering, and materials science. As those who work at the tangible interface between brain and outside world, neurosurgeons are well placed to contribute to, and inform, this cutting edge area of translational research. This article explores the historical background, status quo, and future of brain-computer interfaces; and outlines the challenges to progress and opportunities available to the clinical neurosciences community.

Evolution of the Brain Computing Interface (BCI and Proposed Electroencephalography (EEG Signals Based Authentication Model

Directory of Open Access Journals (Sweden)

Ramzan Qaseem

2018-01-01

Full Text Available With current advancements in the field of Brain Computer interface it is required to study how it will affect the other technologies currently in use. In this paper, the authors motivate the need of Brain Computing Interface in the era of IoT (Internet of Things, and analyze how BCI in the presence of IoT could have serious privacy breach if not protected by new kind of more secure protocols. Security breach and hacking has been around for a long time but now we are sensitive towards data as our lives depend on it. When everything is interconnected through IoT and considering that we control all interconnected things by means of our brain using BCI (Brain Computer Interface, the meaning of security breach becomes much more sensitive than in the past. This paper describes the old security methods being used for authentication and how they can be compromised. Considering the sensitivity of data in the era of IoT, a new form of authentication is required, which should incorporate BCI rather than usual authentication techniques.

MULTI - multifunctional interface of the IBM XT and AT type personal computers

International Nuclear Information System (INIS)

Gross, T.; Kalavski, D.; Rubin, D.; Tulaev, A.B.; Tumanov, A.V.

1988-01-01

MULTI multifunctional interface which enables to solve problems of personal computer connestion with physical equipment without application of intermediate buses is described. Parallel 32-digit bidirectional 1/10 register and buffered bus of personal computer represent MULTI base. Ways of MULTI application are described

Performance of Brain-computer Interfacing based on tactile selective sensation and motor imagery

DEFF Research Database (Denmark)

Yao, Lin; Sheng, Xinjun; Mrachacz-Kersting, Natalie

2018-01-01

We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile...

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

Science.gov (United States)

Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

2015-01-01

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Addition of visual noise boosts evoked potential-based brain-computer interface.

Science.gov (United States)

Xie, Jun; Xu, Guanghua; Wang, Jing; Zhang, Sicong; Zhang, Feng; Li, Yeping; Han, Chengcheng; Li, Lili

2014-05-14

Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7-36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs.

Navigation with a passive brain based interface

NARCIS (Netherlands)

Erp, J.B.F. van; Werkhoven, P.J.; Thurlings, M.E.; Brouwer, A.-M.

2009-01-01

In this paper, we describe a Brain Computer Interface (BCI) for navigation. The system is based on detecting brain signals that are elicited by tactile stimulation on the torso indicating the desired direction.

BRAIN-COMPUTER-INTERFACE – SUPPORTED MOTOR IMAGERY TRAININTG FOR PATIENTS WITH HEMIPARESIS

Directory of Open Access Journals (Sweden)

O. A. Mokienko

2013-01-01

Full Text Available The aim of study was to assess the feasibility of motor imagery supported brain-computer interface in patients with hemiparesis. 13 patients with central paresis of the hand and 15 healthy volunteers were learning to control EEG-based interface with feedback. No differences on interface control quality were found between patients and healthy subjects. The trainings were accompanied by the desynchronization of sensorimotor rhythm. In patients with cortical damage the source of EEG-activity was dislocated.

Gaze-independent brain-computer interfaces based on covert attention and feature attention

Science.gov (United States)

Treder, M. S.; Schmidt, N. M.; Blankertz, B.

2011-10-01

There is evidence that conventional visual brain-computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%-97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.

Selective sensation based brain-computer interface via mechanical vibrotactile stimulation.

Science.gov (United States)

Yao, Lin; Meng, Jianjun; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

2013-01-01

In this work, mechanical vibrotactile stimulation was applied to subjects' left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement ([Formula: see text]) from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system.

Selective Sensation Based Brain-Computer Interface via Mechanical Vibrotactile Stimulation

Science.gov (United States)

Yao, Lin; Meng, Jianjun; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

2013-01-01

In this work, mechanical vibrotactile stimulation was applied to subjects’ left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement () from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system. PMID:23762253

An optimal range of information quantity on computer-based procedure interface design in the advanced main control room

International Nuclear Information System (INIS)

Hsieh Minchih; Chiu Mingchuan; Hwang Sheueling

2015-01-01

The quantification of information in the interface design is a critical issue. Too much information on an interface can confuse a user while executing a task, and too little information may result in poor user performance. This study focused on the quantification of visible information on computer-based procedures (CBPs). Levels of information quantity and task complexity were considered in this experiment. Simulated CBPs were developed to consist of three levels: high (at least 10 events, i.e. 3.32 bits), medium (4–8 events, i.e. 2–3 bits), and low information quantity (1 or 2 events, i.e. 0 or 1 bits). Task complexity comprised two levels: complex tasks and simple tasks. The dependent variables include operation time, secondary task performance, and mental workload. Results suggested that medium information quantity of five to eight events has a remarkable advantage in supporting operator performance under both simple and complex tasks. This research not only suggested the appropriate range of information quantity on the CBP interface, but also complemented certain deficient results of previous CBP interface design studies. Additionally, based on results obtained by this study, the quantification of information on the CBP interface should be considered to ensure safe operation of nuclear power plants. (author)

Design of a Computer-Controlled, Random-Access Slide Projector Interface. Final Report (April 1974 - November 1974).

Science.gov (United States)

Kirby, Paul J.; And Others

The design, development, test, and evaluation of an electronic hardware device interfacing a commercially available slide projector with a plasma panel computer terminal is reported. The interface device allows an instructional computer program to select slides for viewing based upon the lesson student situation parameters of the instructional…

Connections that Count: Brain-Computer Interface Enables the Profoundly Paralyzed to Communicate

Science.gov (United States)

... Home Current Issue Past Issues Connections that Count: Brain-Computer Interface Enables the Profoundly Paralyzed to Communicate Past Issues / ... of this page please turn Javascript on. A brain-computer interface (BCI) system This brain-computer interface (BCI) system ...

Endpoint-based parallel data processing in a parallel active messaging interface of a parallel computer

Science.gov (United States)

Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.

2014-08-12

Endpoint-based parallel data processing in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective operation through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.

Version II of the users manual for the Tuff Data Base Interface

International Nuclear Information System (INIS)

Welch, E.P.; Satter, B.J.; Langkopf, B.S.; Zeuch, D.H.

1987-05-01

The Nevada Nuclear Waste Storage Investigations (NNWSI) Project, managed by the Nevada Operations Office of the US Department of Energy, is investigating the feasibility of locating a repository at Yucca Mountain on and adjacent to the Nevada Test Site (NTS) in southern Nevada. A part of this investigation includes obtaining physical properties from laboratory tests on samples from Yucca Mountain and from field tests at Yucca Mountain. A computerized data base has been developed to store this data in a centralized location. The data base is stored on the Cyber 170/855 computer at Sandia using the System 2000 Data Base Management software. A user-friendly interface, the Tuff Data Base Interface (the Interface), allows NNWSI participants to retrieve data from the Tuff Data Base. The Interface gives users flexibility to retrieve portions of the Data Base related to their interests. This report gives basic instructions on accessing the Sandia computing system and explains how to use the Interface. 18 figs., 5 tabs

A visual interface to computer programs for linkage analysis.

Science.gov (United States)

Chapman, C J

1990-06-01

This paper describes a visual approach to the input of information about human families into computer data bases, making use of the GEM graphic interface on the Atari ST. Similar approaches could be used on the Apple Macintosh or on the IBM PC AT (to which it has been transferred). For occasional users of pedigree analysis programs, this approach has considerable advantages in ease of use and accessibility. An example of such use might be the analysis of risk in families with Huntington disease using linked RFLPs. However, graphic interfaces do make much greater demands on the programmers of these systems.

Are we there yet? Evaluating commercial grade brain-computer interface for control of computer applications by individuals with cerebral palsy.

Science.gov (United States)

Taherian, Sarvnaz; Selitskiy, Dmitry; Pau, James; Claire Davies, T

2017-02-01

Using a commercial electroencephalography (EEG)-based brain-computer interface (BCI), the training and testing protocol for six individuals with spastic quadriplegic cerebral palsy (GMFCS and MACS IV and V) was evaluated. A customised, gamified training paradigm was employed. Over three weeks, the participants spent two sessions exploring the system, and up to six sessions playing the game which focussed on EEG feedback of left and right arm motor imagery. The participants showed variable inconclusive results in the ability to produce two distinct EEG patterns. Participant performance was influenced by physical illness, motivation, fatigue and concentration. The results from this case study highlight the infancy of BCIs as a form of assistive technology for people with cerebral palsy. Existing commercial BCIs are not designed according to the needs of end-users. Implications for Rehabilitation Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces are not designed for practical assistive technology use for people with cerebral palsy. Practical brain-computer interface assistive technologies may need to be flexible to suit individual needs.

Guidelines for the integration of audio cues into computer user interfaces

Energy Technology Data Exchange (ETDEWEB)

Sumikawa, D.A.

1985-06-01

Throughout the history of computers, vision has been the main channel through which information is conveyed to the computer user. As the complexities of man-machine interactions increase, more and more information must be transferred from the computer to the user and then successfully interpreted by the user. A logical next step in the evolution of the computer-user interface is the incorporation of sound and thereby using the sense of ''hearing'' in the computer experience. This allows our visual and auditory capabilities to work naturally together in unison leading to more effective and efficient interpretation of all information received by the user from the computer. This thesis presents an initial set of guidelines to assist interface developers in designing an effective sight and sound user interface. This study is a synthesis of various aspects of sound, human communication, computer-user interfaces, and psychoacoustics. We introduce the notion of an earcon. Earcons are audio cues used in the computer-user interface to provide information and feedback to the user about some computer object, operation, or interaction. A possible construction technique for earcons, the use of earcons in the interface, how earcons are learned and remembered, and the affects of earcons on their users are investigated. This study takes the point of view that earcons are a language and human/computer communication issue and are therefore analyzed according to the three dimensions of linguistics; syntactics, semantics, and pragmatics.

Spectrometer user interface to computer systems

International Nuclear Information System (INIS)

Salmon, L.; Davies, M.; Fry, F.A.; Venn, J.B.

1979-01-01

A computer system for use in radiation spectrometry should be designed around the needs and comprehension of the user and his operating environment. To this end, the functions of the system should be built in a modular and independent fashion such that they can be joined to the back end of an appropriate user interface. The point that this interface should be designed rather than just allowed to evolve is illustrated by reference to four related computer systems of differing complexity and function. The physical user interfaces in all cases are keyboard terminals, and the virtues and otherwise of these devices are discussed and compared with others. The language interface needs to satisfy a number of requirements, often conflicting. Among these, simplicity and speed of operation compete with flexibility and scope. Both experienced and novice users need to be considered, and any individual's needs may vary from naive to complex. To be efficient and resilient, the implementation must use an operating system, but the user needs to be protected from its complex and unfamiliar syntax. At the same time the interface must allow the user access to all services appropriate to his needs. The user must also receive an image of privacy in a multi-user system. The interface itself must be stable and exhibit continuity between implementations. Some of these conflicting needs have been overcome by the SABRE interface with languages operating at several levels. The foundation is a simple semimnemonic command language that activates indididual and independent functions. The commands can be used with positional parameters or in an interactive dialogue the precise nature of which depends upon the operating environment and the user's experience. A command procedure or macrolanguage allows combinations of commands with conditional branching and arithmetic features. Thus complex but repetitive operations are easily performed

Brain-computer interfaces for arts

NARCIS (Netherlands)

Gürkök, Hayrettin; Nijholt, Antinus; D' Mello, S.; Pantic, Maja

2013-01-01

We experience positive emotions when our hedonic needs, such as virtuosity or relatedness, are satisfied. Creating art is one way of satisfying these needs, so artistic computer applications can be considered as ‘affective’. Artistic braincomputer interfaces (BCIs), which allow people to create art

Experiencing Brain-Computer Interface Control

NARCIS (Netherlands)

van de Laar, B.L.A.

2016-01-01

Brain-Computer Interfaces (BCIs) are systems that extract information from the user’s brain activity and employ it in some way in an interactive system. While historically BCIs were mainly catered towards paralyzed or otherwise physically handicapped users, the last couple of years applications with

Interfacing the Paramesh Computational Libraries to the Cactus Computational Framework, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — We will design and implement an interface between the Paramesh computational libraries, developed and used by groups at NASA GSFC, and the Cactus computational...

Internet-based interface for STRMDEPL08

Science.gov (United States)

Reeves, Howard W.; Asher, A. Jeremiah

2010-01-01

The core of the computer program STRMDEPL08 that estimates streamflow depletion by a pumping well with one of four analytical solutions was re-written in the Javascript software language and made available through an internet-based interface (web page). In the internet-based interface, the user enters data for one of the four analytical solutions, Glover and Balmer (1954), Hantush (1965), Hunt (1999), and Hunt (2003), and the solution is run for constant pumping for a desired number of simulation days. Results are returned in tabular form to the user. For intermittent pumping, the interface allows the user to request that the header information for an input file for the stand-alone executable STRMDEPL08 be created. The user would add the pumping information to this header information and run the STRMDEPL08 executable that is available for download through the U.S. Geological Survey. Results for the internet-based and stand-alone versions of STRMDEPL08 are shown to match.

A computational method for sharp interface advection

Science.gov (United States)

Bredmose, Henrik; Jasak, Hrvoje

2016-01-01

We devise a numerical method for passive advection of a surface, such as the interface between two incompressible fluids, across a computational mesh. The method is called isoAdvector, and is developed for general meshes consisting of arbitrary polyhedral cells. The algorithm is based on the volume of fluid (VOF) idea of calculating the volume of one of the fluids transported across the mesh faces during a time step. The novelty of the isoAdvector concept consists of two parts. First, we exploit an isosurface concept for modelling the interface inside cells in a geometric surface reconstruction step. Second, from the reconstructed surface, we model the motion of the face–interface intersection line for a general polygonal face to obtain the time evolution within a time step of the submerged face area. Integrating this submerged area over the time step leads to an accurate estimate for the total volume of fluid transported across the face. The method was tested on simple two-dimensional and three-dimensional interface advection problems on both structured and unstructured meshes. The results are very satisfactory in terms of volume conservation, boundedness, surface sharpness and efficiency. The isoAdvector method was implemented as an OpenFOAM® extension and is published as open source. PMID:28018619

A computational method for sharp interface advection.

Science.gov (United States)

Roenby, Johan; Bredmose, Henrik; Jasak, Hrvoje

2016-11-01

We devise a numerical method for passive advection of a surface, such as the interface between two incompressible fluids, across a computational mesh. The method is called isoAdvector, and is developed for general meshes consisting of arbitrary polyhedral cells. The algorithm is based on the volume of fluid (VOF) idea of calculating the volume of one of the fluids transported across the mesh faces during a time step. The novelty of the isoAdvector concept consists of two parts. First, we exploit an isosurface concept for modelling the interface inside cells in a geometric surface reconstruction step. Second, from the reconstructed surface, we model the motion of the face-interface intersection line for a general polygonal face to obtain the time evolution within a time step of the submerged face area. Integrating this submerged area over the time step leads to an accurate estimate for the total volume of fluid transported across the face. The method was tested on simple two-dimensional and three-dimensional interface advection problems on both structured and unstructured meshes. The results are very satisfactory in terms of volume conservation, boundedness, surface sharpness and efficiency. The isoAdvector method was implemented as an OpenFOAM ® extension and is published as open source.

Knowledge-based control of an adaptive interface

Science.gov (United States)

Lachman, Roy

1989-01-01

The analysis, development strategy, and preliminary design for an intelligent, adaptive interface is reported. The design philosophy couples knowledge-based system technology with standard human factors approaches to interface development for computer workstations. An expert system has been designed to drive the interface for application software. The intelligent interface will be linked to application packages, one at a time, that are planned for multiple-application workstations aboard Space Station Freedom. Current requirements call for most Space Station activities to be conducted at the workstation consoles. One set of activities will consist of standard data management services (DMS). DMS software includes text processing, spreadsheets, data base management, etc. Text processing was selected for the first intelligent interface prototype because text-processing software can be developed initially as fully functional but limited with a small set of commands. The program's complexity then can be increased incrementally. The intelligent interface includes the operator's behavior and three types of instructions to the underlying application software are included in the rule base. A conventional expert-system inference engine searches the data base for antecedents to rules and sends the consequents of fired rules as commands to the underlying software. Plans for putting the expert system on top of a second application, a database management system, will be carried out following behavioral research on the first application. The intelligent interface design is suitable for use with ground-based workstations now common in government, industrial, and educational organizations.

Brain-computer interface

DEFF Research Database (Denmark)

2014-01-01

A computer-implemented method of providing an interface between a user and a processing unit, the method comprising : presenting one or more stimuli to a user, each stimulus varying at a respective stimulation frequency, each stimulation frequency being associated with a respective user......-selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the one or more stimuli the user attends to and selecting the user-selectable input associated with the stimulation frequency of the determined stimuli as being a user...

The Brain-Computer Interface Cycle

NARCIS (Netherlands)

Gerven, Marcel; Farquhar, Jason; Schaefer, Rebecca; Vlek, Rutger; Geuze, Jeroen; Nijholt, Antinus; Ramsay, Nick; Haselager, Pim; Vuurpijl, Louis; Gielen, Stan; Desain, Peter

2009-01-01

Brain–computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of

Human-computer interface glove using flexible piezoelectric sensors

Science.gov (United States)

Cha, Youngsu; Seo, Jeonggyu; Kim, Jun-Sik; Park, Jung-Min

2017-05-01

In this note, we propose a human-computer interface glove based on flexible piezoelectric sensors. We select polyvinylidene fluoride as the piezoelectric material for the sensors because of advantages such as a steady piezoelectric characteristic and good flexibility. The sensors are installed in a fabric glove by means of pockets and Velcro bands. We detect changes in the angles of the finger joints from the outputs of the sensors, and use them for controlling a virtual hand that is utilized in virtual object manipulation. To assess the sensing ability of the piezoelectric sensors, we compare the processed angles from the sensor outputs with the real angles from a camera recoding. With good agreement between the processed and real angles, we successfully demonstrate the user interaction system with the virtual hand and interface glove based on the flexible piezoelectric sensors, for four hand motions: fist clenching, pinching, touching, and grasping.

Brain computer interface for operating a robot

Science.gov (United States)

Nisar, Humaira; Balasubramaniam, Hari Chand; Malik, Aamir Saeed

2013-10-01

A Brain-Computer Interface (BCI) is a hardware/software based system that translates the Electroencephalogram (EEG) signals produced by the brain activity to control computers and other external devices. In this paper, we will present a non-invasive BCI system that reads the EEG signals from a trained brain activity using a neuro-signal acquisition headset and translates it into computer readable form; to control the motion of a robot. The robot performs the actions that are instructed to it in real time. We have used the cognitive states like Push, Pull to control the motion of the robot. The sensitivity and specificity of the system is above 90 percent. Subjective results show a mixed trend of the difficulty level of the training activities. The quantitative EEG data analysis complements the subjective results. This technology may become very useful for the rehabilitation of disabled and elderly people.

Digital quality control of the camera computer interface

International Nuclear Information System (INIS)

Todd-Pokropek, A.

1983-01-01

A brief description is given of how the gamma camera-computer interface works and what kind of errors can occur. Quality control tests of the interface are then described which include 1) tests of static performance e.g. uniformity, linearity, 2) tests of dynamic performance e.g. basic timing, interface count-rate, system count-rate, 3) tests of special functions e.g. gated acquisition, 4) tests of the gamma camera head, and 5) tests of the computer software. The tests described are mainly acceptance and routine tests. Many of the tests discussed are those recommended by an IAEA Advisory Group for inclusion in the IAEA control schedules for nuclear medicine instrumentation. (U.K.)

High Performance Computing - Power Application Programming Interface Specification.

Energy Technology Data Exchange (ETDEWEB)

Laros, James H.,; Kelly, Suzanne M.; Pedretti, Kevin; Grant, Ryan; Olivier, Stephen Lecler; Levenhagen, Michael J.; DeBonis, David

2014-08-01

Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area [13, 3, 5, 10, 4, 21, 19, 16, 7, 17, 20, 18, 11, 1, 6, 14, 12]. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.

Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness

Science.gov (United States)

Pichiorri, F.; De Vico Fallani, F.; Cincotti, F.; Babiloni, F.; Molinari, M.; Kleih, S. C.; Neuper, C.; Kübler, A.; Mattia, D.

2011-04-01

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

A USB 2.0 computer interface for the UCO/Lick CCD cameras

Science.gov (United States)

Wei, Mingzhi; Stover, Richard J.

2004-09-01

The new UCO/Lick Observatory CCD camera uses a 200 MHz fiber optic cable to transmit image data and an RS232 serial line for low speed bidirectional command and control. Increasingly RS232 is a legacy interface supported on fewer computers. The fiber optic cable requires either a custom interface board that is plugged into the mainboard of the image acquisition computer to accept the fiber directly or an interface converter that translates the fiber data onto a widely used standard interface. We present here a simple USB 2.0 interface for the UCO/Lick camera. A single USB cable connects to the image acquisition computer and the camera's RS232 serial and fiber optic cables plug into the USB interface. Since most computers now support USB 2.0 the Lick interface makes it possible to use the camera on essentially any modern computer that has the supporting software. No hardware modifications or additions to the computer are needed. The necessary device driver software has been written for the Linux operating system which is now widely used at Lick Observatory. The complete data acquisition software for the Lick CCD camera is running on a variety of PC style computers as well as an HP laptop.

Legal Aspects of Brain-Computer Interfaces

Czech Academy of Sciences Publication Activity Database

Krausová, Alžběta

2014-01-01

Roč. 8, č. 2 (2014) ISSN 1802-5951 Institutional support: RVO:68378122 Keywords : brain-computer interface * human rights * right to privacy, Subject RIV: AG - Legal Sciences http://mujlt.law.muni.cz/index.php

Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

Directory of Open Access Journals (Sweden)

Shivayogi V Hiremath

2015-06-01

Full Text Available A brain-computer interface (BCI system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Directory of Open Access Journals (Sweden)

Aitzol Astigarraga

2016-01-01

Full Text Available Brain-Computer Interfaces (BCIs have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG- based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

Computational analysis of RNA-protein interaction interfaces via the Voronoi diagram.

Science.gov (United States)

Mahdavi, Sedigheh; Mohades, Ali; Salehzadeh Yazdi, Ali; Jahandideh, Samad; Masoudi-Nejad, Ali

2012-01-21

Cellular functions are mediated by various biological processes including biomolecular interactions, such as protein-protein, DNA-protein and RNA-protein interactions in which RNA-Protein interactions are indispensable for many biological processes like cell development and viral replication. Unlike the protein-protein and protein-DNA interactions, accurate mechanisms and structures of the RNA-Protein complexes are not fully understood. A large amount of theoretical evidence have shown during the past several years that computational geometry is the first pace in understanding the binding profiles and plays a key role in the study of intricate biological structures, interactions and complexes. In this paper, RNA-Protein interaction interface surface is computed via the weighted Voronoi diagram of atoms. Using two filter operations provides a natural definition for interface atoms as classic methods. Unbounded parts of Voronoi facets that are far from the complex are trimmed using modified convex hull of atom centers. This algorithm is implemented to a database with different RNA-Protein complexes extracted from Protein Data Bank (PDB). Afterward, the features of interfaces have been computed and compared with classic method. The results show high correlation coefficients between interface size in the Voronoi model and the classical model based on solvent accessibility, as well as high accuracy and precision in comparison to classical model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Brain-muscle-computer interface: mobile-phone prototype development and testing.

Science.gov (United States)

Vernon, Scott; Joshi, Sanjay S

2011-07-01

We report prototype development and testing of a new mobile-phone-based brain-muscle-computer interface for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single surface electromyography (sEMG) signal. EMG activity on the surface of a single face muscle site (auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone and digitized via an internal A/D converter. The digital signal is split, and then simultaneously filtered with two band-pass filters to extract total power within two separate frequency bands. The user-modulated power in each frequency band serves as two separate control channels for machine control. After signal processing, the Android phone sends commands to external devices via a Bluetooth interface. Users are trained to use the device via visually based operant conditioning, with simple cursor-to-target activities on the phone screen. The mobile-phone prototype interface is formally evaluated on a single advanced Spinal Muscle Atrophy subject, who has successfully used the interface in his home in evaluation trials and for remote control of a television. Development of this new device will not only guide future interface design for community use, but will also serve as an information technology bridge for in situ data collection to quantify human sEMG manipulation abilities for a relevant population.

Mental workload during brain-computer interface training.

Science.gov (United States)

Felton, Elizabeth A; Williams, Justin C; Vanderheiden, Gregg C; Radwin, Robert G

2012-01-01

It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.

Design of Wireless GPIB Interface Module Based on Bluetooth

International Nuclear Information System (INIS)

Fu, P; Ma, W J; Huang, C J

2006-01-01

GPIB interface is widely used in the testing and control field. In this paper a wireless GPIB interface module based on Bluetooth is developed. Programming with Verilog HDL language on the hardware of ROK 101 008 and a FPGA chip, the complicated logical design of GPIB interface and the Bluetooth data processing unit are implemented. On basis of Bluetooth specifications, the software for the control computer is developed. In order to provide a standard software interface for users, a VISA library that is compatible with the VPP specifications is also designed

Design of Wireless GPIB Interface Module Based on Bluetooth

Energy Technology Data Exchange (ETDEWEB)

Fu, P [Department of Automatic Testing and Control, Harbin Institute of Technology, Harbin 150001 (China); Ma, W J [Department of Automatic Testing and Control, Harbin Institute of Technology, Harbin 150001 (China); Huang, C J [Department of Automatic Testing and Control, Harbin Institute of Technology, Harbin 150001 (China)

2006-10-15

GPIB interface is widely used in the testing and control field. In this paper a wireless GPIB interface module based on Bluetooth is developed. Programming with Verilog HDL language on the hardware of ROK 101 008 and a FPGA chip, the complicated logical design of GPIB interface and the Bluetooth data processing unit are implemented. On basis of Bluetooth specifications, the software for the control computer is developed. In order to provide a standard software interface for users, a VISA library that is compatible with the VPP specifications is also designed.

Flashing characters with famous faces improves ERP-based brain-computer interface performance

Science.gov (United States)

Kaufmann, T.; Schulz, S. M.; Grünzinger, C.; Kübler, A.

2011-10-01

Currently, the event-related potential (ERP)-based spelling device, often referred to as P300-Speller, is the most commonly used brain-computer interface (BCI) for enhancing communication of patients with impaired speech or motor function. Among numerous improvements, a most central feature has received little attention, namely optimizing the stimulus used for eliciting ERPs. Therefore we compared P300-Speller performance with the standard stimulus (flashing characters) against performance with stimuli known for eliciting particularly strong ERPs due to their psychological salience, i.e. flashing familiar faces transparently superimposed on characters. Our results not only indicate remarkably increased ERPs in response to familiar faces but also improved P300-Speller performance due to a significant reduction of stimulus sequences needed for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-Speller.

Version I of the users manual for the Tuff Data Base Interface

International Nuclear Information System (INIS)

Langkopf, B.S.; Satter, B.J.; Welch, E.P.

1985-04-01

The Nevada Nuclear Waste Storage Investigations (NNWSI) project, managed by the Nevada Operations Office of the US Department of Energy, is investigating the feasibility of locating a repository at Yucca Mountain on and adjacent to the Nevada Test Site (NTS) in southern Nevada. A part of this investigation includes obtaining physical properties from laboratory tests on samples from Yucca Mountain and field tests of the in situ tuffs at Yucca Mountain. A computerized data base has been developed to store this data in a centralized location. The data base is stored on the Cyber 170/855 computer at Sandia using the System 2000 Data Base Management software. A user-friendly interface, the Tuff Data Base Interface, is being developed to allow NNWSI participants to retrieve information from the Tuff Data Base directly. The Interface gives NNWSI users a great deal of flexibility in retrieving portions of the Data Base. This report is an interim users manual for the Tuff Data Base Interface, as of August 1984. It gives basic instructions on accessing the Sandia computing system and explains the Interface on a question-by-question basis

A user-friendly SSVEP-based brain-computer interface using a time-domain classifier.

Science.gov (United States)

Luo, An; Sullivan, Thomas J

2010-04-01

We introduce a user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. Single-channel EEG is recorded using a low-noise dry electrode. Compared to traditional gel-based multi-sensor EEG systems, a dry sensor proves to be more convenient, comfortable and cost effective. A hardware system was built that displays four LED light panels flashing at different frequencies and synchronizes with EEG acquisition. The visual stimuli have been carefully designed such that potential risk to photosensitive people is minimized. We describe a novel stimulus-locked inter-trace correlation (SLIC) method for SSVEP classification using EEG time-locked to stimulus onsets. We studied how the performance of the algorithm is affected by different selection of parameters. Using the SLIC method, the average light detection rate is 75.8% with very low error rates (an 8.4% false positive rate and a 1.3% misclassification rate). Compared to a traditional frequency-domain-based method, the SLIC method is more robust (resulting in less annoyance to the users) and is also suitable for irregular stimulus patterns.

A novel task-oriented optimal design for P300-based brain-computer interfaces.

Science.gov (United States)

Zhou, Zongtan; Yin, Erwei; Liu, Yang; Jiang, Jun; Hu, Dewen

2014-10-01

Objective. The number of items of a P300-based brain-computer interface (BCI) should be adjustable in accordance with the requirements of the specific tasks. To address this issue, we propose a novel task-oriented optimal approach aimed at increasing the performance of general P300 BCIs with different numbers of items. Approach. First, we proposed a stimulus presentation with variable dimensions (VD) paradigm as a generalization of the conventional single-character (SC) and row-column (RC) stimulus paradigms. Furthermore, an embedding design approach was employed for any given number of items. Finally, based on the score-P model of each subject, the VD flash pattern was selected by a linear interpolation approach for a certain task. Main results. The results indicate that the optimal BCI design consistently outperforms the conventional approaches, i.e., the SC and RC paradigms. Specifically, there is significant improvement in the practical information transfer rate for a large number of items. Significance. The results suggest that the proposed optimal approach would provide useful guidance in the practical design of general P300-based BCIs.

Design of a Mobile Brain Computer Interface-Based Smart Multimedia Controller

Science.gov (United States)

Tseng, Kevin C.; Lin, Bor-Shing; Wong, Alice May-Kuen; Lin, Bor-Shyh

2015-01-01

Music is a way of expressing our feelings and emotions. Suitable music can positively affect people. However, current multimedia control methods, such as manual selection or automatic random mechanisms, which are now applied broadly in MP3 and CD players, cannot adaptively select suitable music according to the user’s physiological state. In this study, a brain computer interface-based smart multimedia controller was proposed to select music in different situations according to the user’s physiological state. Here, a commercial mobile tablet was used as the multimedia platform, and a wireless multi-channel electroencephalograph (EEG) acquisition module was designed for real-time EEG monitoring. A smart multimedia control program built in the multimedia platform was developed to analyze the user’s EEG feature and select music according his/her state. The relationship between the user’s state and music sorted by listener’s preference was also examined in this study. The experimental results show that real-time music biofeedback according a user’s EEG feature may positively improve the user’s attention state. PMID:25756862

Design of a Mobile Brain Computer Interface-Based Smart Multimedia Controller

Directory of Open Access Journals (Sweden)

Kevin C. Tseng

2015-03-01

Full Text Available Music is a way of expressing our feelings and emotions. Suitable music can positively affect people. However, current multimedia control methods, such as manual selection or automatic random mechanisms, which are now applied broadly in MP3 and CD players, cannot adaptively select suitable music according to the user’s physiological state. In this study, a brain computer interface-based smart multimedia controller was proposed to select music in different situations according to the user’s physiological state. Here, a commercial mobile tablet was used as the multimedia platform, and a wireless multi-channel electroencephalograph (EEG acquisition module was designed for real-time EEG monitoring. A smart multimedia control program built in the multimedia platform was developed to analyze the user’s EEG feature and select music according his/her state. The relationship between the user’s state and music sorted by listener’s preference was also examined in this study. The experimental results show that real-time music biofeedback according a user’s EEG feature may positively improve the user’s attention state.

Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

International Nuclear Information System (INIS)

Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

2011-01-01

The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

A brain-computer interface to support functional recovery

DEFF Research Database (Denmark)

Kjaer, Troels W; Sørensen, Helge Bjarup Dissing

2013-01-01

Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features...... extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type...... of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating...

Brain-computer interfaces in neurological rehabilitation.

Science.gov (United States)

Daly, Janis J; Wolpaw, Jonathan R

2008-11-01

Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

TMS communications software. Volume 1: Computer interfaces

Science.gov (United States)

Brown, J. S.; Lenker, M. D.

1979-01-01

A prototype bus communications system, which is being used to support the Trend Monitoring System (TMS) as well as for evaluation of the bus concept is considered. Hardware and software interfaces to the MODCOMP and NOVA minicomputers are included. The system software required to drive the interfaces in each TMS computer is described. Documentation of other software for bus statistics monitoring and for transferring files across the bus is also included.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface

Science.gov (United States)

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-08-01

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s-1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

A Novel Mu Rhythm-based Brain Computer Interface Design that uses a Programmable System on Chip.

Science.gov (United States)

Joshi, Rohan; Saraswat, Prateek; Gajendran, Rudhram

2012-01-01

This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC, the use of an extra computer is eliminated, resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5.01 are employed in developing the software for the system, the hardware being custom designed. In order to test the usability of the BCI, preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min.

Interface between computational fluid dynamics (CFD) and plant analysis computer codes

International Nuclear Information System (INIS)

Coffield, R.D.; Dunckhorst, F.F.; Tomlinson, E.T.; Welch, J.W.

1993-01-01

Computational fluid dynamics (CFD) can provide valuable input to the development of advanced plant analysis computer codes. The types of interfacing discussed in this paper will directly contribute to modeling and accuracy improvements throughout the plant system and should result in significant reduction of design conservatisms that have been applied to such analyses in the past

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

Science.gov (United States)

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

The Impact of User Interface on Young Children’s Computational Thinking

Directory of Open Access Journals (Sweden)

Amanda Sullivan

2017-07-01

Full Text Available Aim/Purpose: Over the past few years, new approaches to introducing young children to computational thinking have grown in popularity. This paper examines the role that user interfaces have on children’s mastery of computational thinking concepts and positive interpersonal behaviors. Background: There is a growing pressure to begin teaching computational thinking at a young age. This study explores the affordances of two very different programming interfaces for teaching computational thinking: a graphical coding application on the iPad (ScratchJr and tangible programmable robotics kit (KIBO. Methodology\t: This study used a mixed-method approach to explore the learning experiences that young children have with tangible and graphical coding interfaces. A sample of children ages four to seven (N = 28 participated. Findings: Results suggest that type of user interface does have an impact on children’s learning, but is only one of many factors that affect positive academic and socio-emotional experiences. Tangible and graphical interfaces each have qualities that foster different types of learning

Performance improvement of ERP-based brain-computer interface via varied geometric patterns.

Science.gov (United States)

Ma, Zheng; Qiu, Tianshuang

2017-12-01

Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.

Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.

Science.gov (United States)

Zander, Thorsten O; Kothe, Christian

2011-04-01

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

Directory of Open Access Journals (Sweden)

J. Clark Powers

2015-08-01

Full Text Available Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use—i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1 alternative signal evocation methods within the oddball paradigm; (2 environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3 measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.

Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms.

Science.gov (United States)

Royer, Audrey S; He, Bin

2009-02-01

In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control, the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp-recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for seven out of eight subjects) and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.

Mechatronics Interface for Computer Assisted Prostate Surgery Training

Science.gov (United States)

Altamirano del Monte, Felipe; Padilla Castañeda, Miguel A.; Arámbula Cosío, Fernando

2006-09-01

In this work is presented the development of a mechatronics device to simulate the interaction of the surgeon with the surgical instrument (resectoscope) used during a Transurethral Resection of the Prostate (TURP). Our mechatronics interface is part of a computer assisted system for training in TURP, which is based on a 3D graphics model of the prostate which can be deformed and resected interactively by the user. The mechatronics interface, is the device that the urology residents will manipulate to simulate the movements performed during surgery. Our current prototype has five degrees of freedom, which are enough to have a realistic simulation of the surgery movements. Two of these degrees of freedom are linear, to determinate the linear displacement of the resecting loop and the other three are rotational to determinate three directions and amounts of rotation.

A tactile P300 brain-computer interface

NARCIS (Netherlands)

Brouwer, A.M.; Erp, J.B.F. van

2010-01-01

De werking van de eerste Brain-Computer-Interface gebaseerd op tactiele EEG response wordt gedemonstreerd en het effect van het aantal gebruikte vibro-tactiele tactoren en stimulus-timing parameters wordt onderzocht

A novel brain-computer interface based on the rapid serial visual presentation paradigm.

Science.gov (United States)

Acqualagna, Laura; Treder, Matthias Sebastian; Schreuder, Martijn; Blankertz, Benjamin

2010-01-01

Most present-day visual brain computer interfaces (BCIs) suffer from the fact that they rely on eye movements, are slow-paced, or feature a small vocabulary. As a potential remedy, we explored a novel BCI paradigm consisting of a central rapid serial visual presentation (RSVP) of the stimuli. It has a large vocabulary and realizes a BCI system based on covert non-spatial selective visual attention. In an offline study, eight participants were presented sequences of rapid bursts of symbols. Two different speeds and two different color conditions were investigated. Robust early visual and P300 components were elicited time-locked to the presentation of the target. Offline classification revealed a mean accuracy of up to 90% for selecting the correct symbol out of 30 possibilities. The results suggest that RSVP-BCI is a promising new paradigm, also for patients with oculomotor impairments.

Research of Digital Interface Layout Design based on Eye-tracking

OpenAIRE

Shao Jiang; Xue Chengqi; Wang Fang; Wang Haiyan; Tang Wencheng; Chen Mo; Kang Mingwu

2015-01-01

The aim of this paper is to improve the low service efficiency and unsmooth human-computer interaction caused by currently irrational layouts of digital interfaces for complex systems. Also, three common layout structures for digital interfaces are to be presented and five layout types appropriate for multilevel digital interfaces are to be summarized. Based on the eye tracking technology, an assessment was conducted in advantages and disadvantages of different layout types through subjects’ ...

RC Circuits: Some Computer-Interfaced Experiments.

Science.gov (United States)

Jolly, Pratibha; Verma, Mallika

1994-01-01

Describes a simple computer-interface experiment for recording the response of an RC network to an arbitrary input excitation. The setup is used to pose a variety of open-ended investigations in network modeling by varying the initial conditions, input signal waveform, and the circuit topology. (DDR)

Brain Computer Interfaces, a Review

Directory of Open Access Journals (Sweden)

Luis Fernando Nicolas-Alonso

2012-01-01

Full Text Available A brain-computer interface (BCI is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

Brain Computer Interfaces, a Review

Science.gov (United States)

Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

2012-01-01

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices. PMID:22438708

Operation of a P300-based brain-computer interface by patients with spinocerebellar ataxia

Directory of Open Access Journals (Sweden)

Yoji Okahara

Full Text Available Objective: We investigated the efficacy of a P300-based brain-computer interface (BCI for patients with spinocerebellar ataxia (SCA, which is often accompanied by cerebellar impairment. Methods: Eight patients with SCA and eight age- and gender-matched healthy controls were instructed to input Japanese hiragana characters using the P300-based BCI with green/blue flicker. All patients depended on some assistance in their daily lives (modified Rankin scale: mean 3.5. The chief symptom was cerebellar ataxia; no cognitive deterioration was present. A region-based, two-step P300-based BCI was used. During the P300 task, eight-channel EEG data were recorded, and a linear discriminant analysis distinguished the target from other nontarget regions of the matrix. Results: The mean online accuracy in BCI operation was 82.9% for patients with SCA and 83.2% for controls; no significant difference was detected. Conclusion: The P300-based BCI was operated successfully not only by healthy controls but also by individuals with SCA. Significance: These results suggest that the P300-based BCI may be applicable for patients with SCA. Keywords: BCI, BMI, P300, Visual stimuli, Spinocerebellar ataxia

Multi parametric card to personal computers interface based in ispLSI1016 circuits

International Nuclear Information System (INIS)

Osorio Deliz, J.F.; Toledo Acosta, R.B.; Arista Romeu, E.

1997-01-01

It is described the design and principal characteristic of the interface circuit for a 16 bit multi parametric add on card for IBM or compatible microcomputer which content two communication channels of direct memory access and bidirectional between the card and the computer, an interrupt controller, a programmable address register, a default add res register of the card, a four channels multiplexer, as well as the decoder logic of the 80C186 and computer. The circuit was designed with two programmable logic devices ispL1016, which allowed drastically to diminish the quantity of utilized components and get a more flexible design in less time better characteristics

Neuroanatomical correlates of brain-computer interface performance.

Science.gov (United States)

Kasahara, Kazumi; DaSalla, Charles Sayo; Honda, Manabu; Hanakawa, Takashi

2015-04-15

Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. Copyright © 2015 Elsevier Inc. All rights reserved.

Electrostatics with Computer-Interfaced Charge Sensors

Science.gov (United States)

Morse, Robert A.

2006-01-01

Computer interfaced electrostatic charge sensors allow both qualitative and quantitative measurements of electrostatic charge but are quite sensitive to charges accumulating on modern synthetic materials. They need to be used with care so that students can correctly interpret their measurements. This paper describes the operation of the sensors,…

User’s Emotions and Usability Study of a Brain-Computer Interface Applied to People with Cerebral Palsy

Directory of Open Access Journals (Sweden)

Alejandro Rafael García Ramírez

2018-02-01

Full Text Available People with motor and communication disorders face serious challenges in interacting with computers. To enhance this functionality, new human-computer interfaces are being studied. In this work, a brain-computer interface based on the Emotiv Epoc is used to analyze human-computer interactions in cases of cerebral palsy. The Phrase-Composer software was developed to interact with the brain-computer interface. A system usability evaluation was carried out with the participation of three specialists from The Fundação Catarinense de Educação especial (FCEE and four cerebral palsy volunteers. Even though the System Usability Scale (SUS score was acceptable, several challenges remain. Raw electroencephalography (EEG data were also analyzed in order to assess the user’s emotions during their interaction with the communication device. This study brings new evidences about human-computer interaction related to individuals with cerebral palsy.

Influence of P300 latency jitter on event related potential-based brain-computer interface performance

Science.gov (United States)

Aricò, P.; Aloise, F.; Schettini, F.; Salinari, S.; Mattia, D.; Cincotti, F.

2014-06-01

Objective. Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Mining multi-channel EEG for its information content: An ANN-based method for a brain-computer interface

DEFF Research Database (Denmark)

Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

1998-01-01

. This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically...

Interface design of VSOP'94 computer code for safety analysis

International Nuclear Information System (INIS)

Natsir, Khairina; Andiwijayakusuma, D.; Wahanani, Nursinta Adi; Yazid, Putranto Ilham

2014-01-01

Today, most software applications, also in the nuclear field, come with a graphical user interface. VSOP'94 (Very Superior Old Program), was designed to simplify the process of performing reactor simulation. VSOP is a integrated code system to simulate the life history of a nuclear reactor that is devoted in education and research. One advantage of VSOP program is its ability to calculate the neutron spectrum estimation, fuel cycle, 2-D diffusion, resonance integral, estimation of reactors fuel costs, and integrated thermal hydraulics. VSOP also can be used to comparative studies and simulation of reactor safety. However, existing VSOP is a conventional program, which was developed using Fortran 65 and have several problems in using it, for example, it is only operated on Dec Alpha mainframe platforms and provide text-based output, difficult to use, especially in data preparation and interpretation of results. We develop a GUI-VSOP, which is an interface program to facilitate the preparation of data, run the VSOP code and read the results in a more user friendly way and useable on the Personal 'Computer (PC). Modifications include the development of interfaces on preprocessing, processing and postprocessing. GUI-based interface for preprocessing aims to provide a convenience way in preparing data. Processing interface is intended to provide convenience in configuring input files and libraries and do compiling VSOP code. Postprocessing interface designed to visualized the VSOP output in table and graphic forms. GUI-VSOP expected to be useful to simplify and speed up the process and analysis of safety aspects

Interface design of VSOP'94 computer code for safety analysis

Science.gov (United States)

Natsir, Khairina; Yazid, Putranto Ilham; Andiwijayakusuma, D.; Wahanani, Nursinta Adi

2014-09-01

Today, most software applications, also in the nuclear field, come with a graphical user interface. VSOP'94 (Very Superior Old Program), was designed to simplify the process of performing reactor simulation. VSOP is a integrated code system to simulate the life history of a nuclear reactor that is devoted in education and research. One advantage of VSOP program is its ability to calculate the neutron spectrum estimation, fuel cycle, 2-D diffusion, resonance integral, estimation of reactors fuel costs, and integrated thermal hydraulics. VSOP also can be used to comparative studies and simulation of reactor safety. However, existing VSOP is a conventional program, which was developed using Fortran 65 and have several problems in using it, for example, it is only operated on Dec Alpha mainframe platforms and provide text-based output, difficult to use, especially in data preparation and interpretation of results. We develop a GUI-VSOP, which is an interface program to facilitate the preparation of data, run the VSOP code and read the results in a more user friendly way and useable on the Personal 'Computer (PC). Modifications include the development of interfaces on preprocessing, processing and postprocessing. GUI-based interface for preprocessing aims to provide a convenience way in preparing data. Processing interface is intended to provide convenience in configuring input files and libraries and do compiling VSOP code. Postprocessing interface designed to visualized the VSOP output in table and graphic forms. GUI-VSOP expected to be useful to simplify and speed up the process and analysis of safety aspects.

Brain-computer interfaces increase whole-brain signal to noise.

Science.gov (United States)

Papageorgiou, T Dorina; Lisinski, Jonathan M; McHenry, Monica A; White, Jason P; LaConte, Stephen M

2013-08-13

Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

Brain-Computer Interfacing Embedded in Intelligent and Affective Systems

NARCIS (Netherlands)

Nijholt, Antinus

In this talk we survey recent research views on non-traditional brain-computer interfaces (BCI). That is, interfaces that can process brain activity input, but that are designed for the ‘general population’, rather than for clinical purposes. Control of applications can be made more robust by fusing

Human-computer interfaces applied to numerical solution of the Plateau problem

Science.gov (United States)

Elias Fabris, Antonio; Soares Bandeira, Ivana; Ramos Batista, Valério

2015-09-01

In this work we present a code in Matlab to solve the Problem of Plateau numerically, and the code will include human-computer interface. The Problem of Plateau has applications in areas of knowledge like, for instance, Computer Graphics. The solution method will be the same one of the Surface Evolver, but the difference will be a complete graphical interface with the user. This will enable us to implement other kinds of interface like ocular mouse, voice, touch, etc. To date, Evolver does not include any graphical interface, which restricts its use by the scientific community. Specially, its use is practically impossible for most of the Physically Challenged People.

Principles of interfacing computers to medical equipment.

Science.gov (United States)

Francis, J L; Martin, T R

1990-12-01

Table 3 shows a comparison of the interface standards considered. RS232 has the advantages of availability, flexibility and low cost. Variants on the standard overcome its limitations in data-rate and distance. The Centronics parallel standard is available on most personal computers and is particularly suitable for high data-rates over short distances. Other PC standards such as SCSI are special-purpose interfaces and therefore more difficult to use. GPIB is a robust and well-specified standard often used for the control of laboratory instruments.

Calibrating EEG-based motor imagery brain-computer interface from passive movement.

Science.gov (United States)

Ang, Kai Keng; Guan, Cuntai; Wang, Chuanchu; Phua, Kok Soon; Tan, Adrian Hock Guan; Chin, Zheng Yang

2011-01-01

EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable, induces Event-Related Synchronization patterns that are similar to those induced from motor imagery. Hence, this paper investigates the feasibility of calibrating EEG-based motor imagery BCI from passive movement. EEG data of 12 healthy subjects were collected during motor imagery and passive movement of the hand by a haptic knob robot. The calibration models using the Filter Bank Common Spatial Pattern algorithm on the EEG data from motor imagery were compared against using the EEG data from passive movement. The performances were compared based on the 10×10-fold cross-validation accuracies of the calibration data, and off-line session-to-session transfer kappa values to other sessions of motor imagery performed on another day. The results showed that the calibration performed using passive movement yielded higher model accuracy and off-line session-to-session transfer (73.6% and 0.354) than the calibration performed using motor imagery (71.3% and 0.311), and no significant differences were observed between the two groups (p=0.20, 0.23). Hence, this study shows that it is feasible to calibrate EEG-based motor imagery BCI from passive movement.

Computer organization and design the hardware/software interface

CERN Document Server

Hennessy, John L

1994-01-01

Computer Organization and Design: The Hardware/Software Interface presents the interaction between hardware and software at a variety of levels, which offers a framework for understanding the fundamentals of computing. This book focuses on the concepts that are the basis for computers.Organized into nine chapters, this book begins with an overview of the computer revolution. This text then explains the concepts and algorithms used in modern computer arithmetic. Other chapters consider the abstractions and concepts in memory hierarchies by starting with the simplest possible cache. This book di

Research of Digital Interface Layout Design based on Eye-tracking

Directory of Open Access Journals (Sweden)

Shao Jiang

2015-01-01

Full Text Available The aim of this paper is to improve the low service efficiency and unsmooth human-computer interaction caused by currently irrational layouts of digital interfaces for complex systems. Also, three common layout structures for digital interfaces are to be presented and five layout types appropriate for multilevel digital interfaces are to be summarized. Based on the eye tracking technology, an assessment was conducted in advantages and disadvantages of different layout types through subjects’ search efficiency. Based on data and results, this study constructed a matching model which is appropriate for multilevel digital interface layout and verified the fact that the task element is a significant and important aspect of layout design. A scientific experimental model of research on digital interfaces for complex systems is provided. Both data and conclusions of the eye movement experiment provide a reference for layout designs of interfaces for complex systems with different task characteristics.

Digital interface for bi-directional communication between a computer and a peripheral device

Science.gov (United States)

Bond, H. H., Jr. (Inventor); Franklin, C. R.

1984-01-01

For transmission of data from the computer to the peripheral, the computer initially clears a flipflop which provides a select signal to a multiplexer. A data available signal or data strobe signal is produced while tht data is being provided to the interface. Setting of the flipflop causes a gate to provide to the peripherial a signal indicating that the interface has data available for transmission. The peripheral provides an acknowledge or strobe signal to transfer the data to the peripheral. For transmission of data from the peripheral to the computer, the computer presents the initially cleared flipflop. A data request signal from the peripheral indicates that the peripheral has data available for transmission to the computer. An acknowledge signal indicates that the interface is ready to receive data from the peripheral and to strobe that data into the interface.

Development of a graphical interface computer code for reactor fuel reloading optimization

International Nuclear Information System (INIS)

Do Quang Binh; Nguyen Phuoc Lan; Bui Xuan Huy

2007-01-01

This report represents the results of the project performed in 2007. The aim of this project is to develop a graphical interface computer code that allows refueling engineers to design fuel reloading patterns for research reactor using simulated graphical model of reactor core. Besides, this code can perform refueling optimization calculations based on genetic algorithms as well as simulated annealing. The computer code was verified based on a sample problem, which relies on operational and experimental data of Dalat research reactor. This code can play a significant role in in-core fuel management practice at nuclear research reactor centers and in training. (author)

Dynamic Distribution and Layouting of Model-Based User Interfaces in Smart Environments

Science.gov (United States)

Roscher, Dirk; Lehmann, Grzegorz; Schwartze, Veit; Blumendorf, Marco; Albayrak, Sahin

The developments in computer technology in the last decade change the ways of computer utilization. The emerging smart environments make it possible to build ubiquitous applications that assist users during their everyday life, at any time, in any context. But the variety of contexts-of-use (user, platform and environment) makes the development of such ubiquitous applications for smart environments and especially its user interfaces a challenging and time-consuming task. We propose a model-based approach, which allows adapting the user interface at runtime to numerous (also unknown) contexts-of-use. Based on a user interface modelling language, defining the fundamentals and constraints of the user interface, a runtime architecture exploits the description to adapt the user interface to the current context-of-use. The architecture provides automatic distribution and layout algorithms for adapting the applications also to contexts unforeseen at design time. Designers do not specify predefined adaptations for each specific situation, but adaptation constraints and guidelines. Furthermore, users are provided with a meta user interface to influence the adaptations according to their needs. A smart home energy management system serves as running example to illustrate the approach.

Preface (to: Towards Practical Brain-Computer Interfaces)

NARCIS (Netherlands)

Allison, Brendan Z.; Dunne, Stephen; Leeb, Robert; Millán, Jose del R.; Allison, Brendan Z.; Dunne, Stephen; Leeb, Robert; Millán, Jose del R.; Nijholt, Antinus

2012-01-01

Brain–computer interface (BCI) research is advancing rapidly. The last few years have seen a dramatic rise in journal publications, academic workshops and conferences, books, new products aimed at both healthy and disabled users, research funding from different sources, and media attention. This

Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface.

Science.gov (United States)

Ludwig, Simone A; Kong, Jun

2017-12-01

Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs.

A FPGA-based Network Interface Card with GPUDirect enabling realtime GPU computing in HEP experiments

CERN Document Server

Lonardo, Alessandro; Ammendola, Roberto; Biagioni, Andrea; Cotta Ramusino, Angelo; Fiorini, Massimiliano; Frezza, Ottorino; Lamanna, Gianluca; Lo Cicero, Francesca; Martinelli, Michele; Neri, Ilaria; Paolucci, Pier Stanislao; Pastorelli, Elena; Pontisso, Luca; Rossetti, Davide; Simeone, Francesco; Simula, Francesco; Sozzi, Marco; Tosoratto, Laura; Vicini, Piero

2015-01-01

The capability of processing high bandwidth data streams in real-time is a computational requirement common to many High Energy Physics experiments. Keeping the latency of the data transport tasks under control is essential in order to meet this requirement. We present NaNet, a FPGA-based PCIe Network Interface Card design featuring Remote Direct Memory Access towards CPU and GPU memories plus a transport protocol offload module characterized by cycle-accurate upper-bound handling. The combination of these two features allows to relieve almost entirely the OS and the application from data tranfer management, minimizing the unavoidable jitter effects associated to OS process scheduling. The design currently supports one GbE (1000Base-T) and three custom 34 Gbps APElink I/O channels, but four-channels 10GbE (10Base-R) and 2.5 Gbps deterministic latency KM3link versions are being implemented. Two use cases of NaNet will be discussed: the GPU-based low level trigger for the RICH detector in the NA62 experiment an...

Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.

Science.gov (United States)

Riechmann, Hannes; Finke, Andrea; Ritter, Helge

2016-06-01

Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use.

A multi-purpose brain-computer interface output device.

Science.gov (United States)

Thompson, David E; Huggins, Jane E

2011-10-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.

A Multi-purpose Brain-Computer Interface Output Device

Science.gov (United States)

Thompson, David E; Huggins, Jane E

2012-01-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as standalone communication and control systems, rather than as interfaces to existing systems built for these purposes. While an individual communication and control system may be powerful or flexible, no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCIs could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e. without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems. PMID:22208120

Investigation and evaluation into the usability of human-computer interfaces using a typical CAD system

Energy Technology Data Exchange (ETDEWEB)

Rickett, J D

1987-01-01

This research program covers three topics relating to the human-computer interface namely, voice recognition, tools and techniques for evaluation, and user and interface modeling. An investigation into the implementation of voice-recognition technologies examines how voice recognizers may be evaluated in commercial software. A prototype system was developed with the collaboration of FEMVIEW Ltd. (marketing a CAD package). A theoretical approach to evaluation leads to the hypothesis that human-computer interaction is affected by personality, influencing types of dialogue, preferred methods for providing helps, etc. A user model based on personality traits, or habitual-behavior patterns (HBP) is presented. Finally, a practical framework is provided for the evaluation of human-computer interfaces. It suggests that evaluation is an integral part of design and that the iterative use of evaluation techniques throughout the conceptualization, design, implementation and post-implementation stages will ensure systems that satisfy the needs of the users and fulfill the goal of usability.

Optimizing the Usability of Brain-Computer Interfaces.

Science.gov (United States)

Zhang, Yin; Chase, Steve M

2018-03-22

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Designing a hands-on brain computer interface laboratory course.

Science.gov (United States)

Khalighinejad, Bahar; Long, Laura Kathleen; Mesgarani, Nima

2016-08-01

Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students. The course provides an effective introduction to the experimental design, neuroscience concepts, data analysis techniques, and technical skills required in the field of BCI.

[A wireless smart home system based on brain-computer interface of steady state visual evoked potential].

Science.gov (United States)

Zhao, Li; Xing, Xiao; Guo, Xuhong; Liu, Zehua; He, Yang

2014-10-01

Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.

Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery

Czech Academy of Sciences Publication Activity Database

Frolov, A.; Húsek, Dušan; Bobrov, P.; Korshakov, A.V.; Chernikova, L.; Konovalov, R.; Mokienko, O.

2012-01-01

Roč. 22, č. 1 (2012), s. 21-37 ISSN 1210-0552 R&D Projects: GA ČR GAP202/10/0262 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070 Program:ED Institutional research plan: CEZ:AV0Z10300504 Keywords : brain-computer interface * independent component analysis * pattern classification * motor imagery * inverse problem * fMRI * EEG Subject RIV: IN - Informatics, Computer Science Impact factor: 0.362, year: 2012

High Performance Computing - Power Application Programming Interface Specification Version 2.0.

Energy Technology Data Exchange (ETDEWEB)

Laros, James H. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Levenhagen, Michael J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Olivier, Stephen Lecler [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Pedretti, Kevin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ward, H. Lee [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Younge, Andrew J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-03-01

Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.

Brain-Computer Interface Games: Towards a Framework.

NARCIS (Netherlands)

Gürkök, Hayrettin; Nijholt, Antinus; Poel, Mannes; Herrlich, Marc; Malaka, Rainer; Masuch, Maic

2012-01-01

The brain-computer interface (BCI) community started to consider games as potential applications while the games community started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. In this paper, we propose a preliminary BCI

Man-system interface based on automatic speech recognition: integration to a virtual control desk

Energy Technology Data Exchange (ETDEWEB)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

2009-07-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

Man-system interface based on automatic speech recognition: integration to a virtual control desk

International Nuclear Information System (INIS)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C.; Nomiya, Diogo V.

2009-01-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

User interface support

Science.gov (United States)

Lewis, Clayton; Wilde, Nick

1989-01-01

Space construction will require heavy investment in the development of a wide variety of user interfaces for the computer-based tools that will be involved at every stage of construction operations. Using today's technology, user interface development is very expensive for two reasons: (1) specialized and scarce programming skills are required to implement the necessary graphical representations and complex control regimes for high-quality interfaces; (2) iteration on prototypes is required to meet user and task requirements, since these are difficult to anticipate with current (and foreseeable) design knowledge. We are attacking this problem by building a user interface development tool based on extensions to the spreadsheet model of computation. The tool provides high-level support for graphical user interfaces and permits dynamic modification of interfaces, without requiring conventional programming concepts and skills.

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface.

Science.gov (United States)

Kondo, Toshiyuki; Saeki, Midori; Hayashi, Yoshikatsu; Nakayashiki, Kosei; Takata, Yohei

2015-10-01

Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI neurofeedback training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills. Copyright © 2014 Elsevier B.V. All rights reserved.

Two ions coupled to an optical cavity : from an enhanced quantum computer interface towards distributed quantum computing

International Nuclear Information System (INIS)

Casabone, B.

2015-01-01

Distributed quantum computing, an approach to scale up the computational power of quantum computers, requires entanglement between nodes of a quantum network. In our research group, two building blocks of schemes to entangle two ion-based quantum computers using cavity-based quantum interfaces have recently been demonstrated: ion-photon entanglement and ion-photon state mapping. In this thesis work, we extend the first building block in order to entangle two ions located in the same optical cavity. The entanglement generated by this protocol is efficient and heralded, and as it does not rely on the fact that ions interact with the same cavity, our results are a stepping stone towards the efficient generation of entanglement of remote ion-based quantum computers. In the second part of this thesis, we discuss how collective effects can be used to improve the performance of a cavity-based quantum interface. We show that by using two ions in the so-called superradiant state, the coupling strength between the two ions and the optical cavity is effectively increased compared to the single-ion case. As a complementary result, the creation of a state of two ions that exhibits a reduced coupling strength to the optical cavity, i.e., a subradiant state, is shown. Finally, we demonstrate a direct application of the increased coupling strength that the superradiant state exhibits by showing an enhanced version of the ion-photon state mapping process. By using the current setup and a second one that is being assembled, we intend to build a quantum network. The heralded ion-ion entanglement protocol presented in this thesis work will be used to entangle ions located in both setups, an experiment that requires photons generated in both apparatuses to be indistinguishable. Collective effects then can be used to modify the waveform of photons exiting the cavity in order to effect the desired photon indistinguishability. (author) [de

Interface design of VSOP'94 computer code for safety analysis

Energy Technology Data Exchange (ETDEWEB)

Natsir, Khairina, E-mail: yenny@batan.go.id; Andiwijayakusuma, D.; Wahanani, Nursinta Adi [Center for Development of Nuclear Informatics - National Nuclear Energy Agency, PUSPIPTEK, Serpong, Tangerang, Banten (Indonesia); Yazid, Putranto Ilham [Center for Nuclear Technology, Material and Radiometry- National Nuclear Energy Agency, Jl. Tamansari No.71, Bandung 40132 (Indonesia)

2014-09-30

Today, most software applications, also in the nuclear field, come with a graphical user interface. VSOP'94 (Very Superior Old Program), was designed to simplify the process of performing reactor simulation. VSOP is a integrated code system to simulate the life history of a nuclear reactor that is devoted in education and research. One advantage of VSOP program is its ability to calculate the neutron spectrum estimation, fuel cycle, 2-D diffusion, resonance integral, estimation of reactors fuel costs, and integrated thermal hydraulics. VSOP also can be used to comparative studies and simulation of reactor safety. However, existing VSOP is a conventional program, which was developed using Fortran 65 and have several problems in using it, for example, it is only operated on Dec Alpha mainframe platforms and provide text-based output, difficult to use, especially in data preparation and interpretation of results. We develop a GUI-VSOP, which is an interface program to facilitate the preparation of data, run the VSOP code and read the results in a more user friendly way and useable on the Personal 'Computer (PC). Modifications include the development of interfaces on preprocessing, processing and postprocessing. GUI-based interface for preprocessing aims to provide a convenience way in preparing data. Processing interface is intended to provide convenience in configuring input files and libraries and do compiling VSOP code. Postprocessing interface designed to visualized the VSOP output in table and graphic forms. GUI-VSOP expected to be useful to simplify and speed up the process and analysis of safety aspects.

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

Directory of Open Access Journals (Sweden)

Markus A Wenzel

Full Text Available Brain-computer interfaces (BCIs that are based on event-related potentials (ERPs can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG. Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI, because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG.The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

Brain-Computer Interface Games: Towards a Framework

NARCIS (Netherlands)

Gürkök, Hayrettin; Nijholt, Antinus; Poel, Mannes; Nakatsu, Ryohei; Rauterberg, Matthias; Ciancarini, Paolo

2015-01-01

The brain-computer interface (BCI) community has started to consider games as potential applications, while the game community has started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. This not only adds to the workload

The graphics-based human interface to the DISYS diagnostic/control guidance system at EBR-2

International Nuclear Information System (INIS)

Edwards, R.M.; Chavez, C.; Kamarthi, S.; Dharap, S.; Lindsay, R.W.; Staffon, J.

1990-01-01

An initial graphics based interface to the real-time DISYS diagnostic system has been developed using the multi-tasking capabilities of the UNIX operating system and X-Windows 11 Xlib graphics library. This system is interfaced to live plant data at the Experimental Breeder Reactor (EBR-2) for the Argon Cooling System of fuel handling operations and the steam plant. The interface includes an intelligent process schematic which highlights problematic components and sensors based on the results of the diagnostic computations. If further explanation of a faulted component is required, the user can call up a display of the diagnostic computations presented in a tree-like diagram. Numerical data on the process schematic and optional diagnostic tree are updated as new real-time data becomes available. The initial X-Windows 11 based interface will be further enhanced using VI Corporation DATAVIEWS graphical data base software. 5 refs., 6 figs

Brain-computer interface: changes in performance using virtual reality techniques.

Science.gov (United States)

Ron-Angevin, Ricardo; Díaz-Estrella, Antonio

2009-01-09

The ability to control electroencephalographic (EEG) signals when different mental tasks are carried out would provide a method of communication for people with serious motor function problems. This system is known as a brain-computer interface (BCI). Due to the difficulty of controlling one's own EEG signals, a suitable training protocol is required to motivate subjects, as it is necessary to provide some type of visual feedback allowing subjects to see their progress. Conventional systems of feedback are based on simple visual presentations, such as a horizontal bar extension. However, virtual reality is a powerful tool with graphical possibilities to improve BCI-feedback presentation. The objective of the study is to explore the advantages of the use of feedback based on virtual reality techniques compared to conventional systems of feedback. Sixteen untrained subjects, divided into two groups, participated in the experiment. A group of subjects was trained using a BCI system, which uses conventional feedback (bar extension), and another group was trained using a BCI system, which submits subjects to a more familiar environment, such as controlling a car to avoid obstacles. The obtained results suggest that EEG behaviour can be modified via feedback presentation. Significant differences in classification error rates between both interfaces were obtained during the feedback period, confirming that an interface based on virtual reality techniques can improve the feedback control, specifically for untrained subjects.

An embedded implementation based on adaptive filter bank for brain-computer interface systems.

Science.gov (United States)

Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui

2018-07-15

Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates. Copyright © 2018 Elsevier B.V. All rights reserved.

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

Science.gov (United States)

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate

Developing A Web-based User Interface for Semantic Information Retrieval

Science.gov (United States)

Berrios, Daniel C.; Keller, Richard M.

2003-01-01

While there are now a number of languages and frameworks that enable computer-based systems to search stored data semantically, the optimal design for effective user interfaces for such systems is still uncle ar. Such interfaces should mask unnecessary query detail from users, yet still allow them to build queries of arbitrary complexity without significant restrictions. We developed a user interface supporting s emantic query generation for Semanticorganizer, a tool used by scient ists and engineers at NASA to construct networks of knowledge and dat a. Through this interface users can select node types, node attribute s and node links to build ad-hoc semantic queries for searching the S emanticOrganizer network.

Usability of Three Electroencephalogram Headsets for Brain-Computer Interfaces: A Within Subject Comparison

NARCIS (Netherlands)

Gamboa, H.; Nijboer, Femke; van de Laar, B.L.A.; Plácido da Silva, H.; Gilleade, K.; Gerritsen, Steven; Nijholt, Antinus; Bermúdez i Badia, S.; Poel, Mannes; Fairclough, S.

Currently the field of brain–computer interfacing is increasingly focused on developing usable brain–computer interfaces (BCIs) to better ensure technology transfer and acceptance. Many studies have investigated the usability of BCI applications as a whole. Here we aim to investigate one specific

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Science.gov (United States)

Huggins, Jane E.; Guger, Christoph; Ziat, Mounia; Zander, Thorsten O.; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K. R.; Ramsey, Nick F.; Nijholt, Anton; Müller-Putz, Gernot; McFarland, Dennis J.; Mattia, Donatella; Lance, Brent J.; Kindermans, Pieter-Jan; Iturrate, Iñaki; Herff, Christian; Gupta, Disha; Do, An H.; Collinger, Jennifer L.; Chavarriaga, Ricardo; Chase, Steven M.; Bleichner, Martin G.; Batista, Aaron; Anderson, Charles W.; Aarnoutse, Erik J.

2017-01-01

The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development. PMID:29152523

Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.

Science.gov (United States)

Huggins, Jane E; Guger, Christoph; Ziat, Mounia; Zander, Thorsten O; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K R; Ramsey, Nick F; Nijholt, Anton; Müller-Putz, Gernot; McFarland, Dennis J; Mattia, Donatella; Lance, Brent J; Kindermans, Pieter-Jan; Iturrate, Iñaki; Herff, Christian; Gupta, Disha; Do, An H; Collinger, Jennifer L; Chavarriaga, Ricardo; Chase, Steven M; Bleichner, Martin G; Batista, Aaron; Anderson, Charles W; Aarnoutse, Erik J

2017-01-01

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Encoder-decoder optimization for brain-computer interfaces.

Science.gov (United States)

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Encoder-decoder optimization for brain-computer interfaces.

Directory of Open Access Journals (Sweden)

Josh Merel

2015-06-01

Full Text Available Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model" and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Training to use a commercial brain-computer interface as access technology: a case study.

Science.gov (United States)

Taherian, Sarvnaz; Selitskiy, Dmitry; Pau, James; Davies, T Claire; Owens, R Glynn

2016-01-01

This case study describes how an individual with spastic quadriplegic cerebral palsy was trained over a period of four weeks to use a commercial electroencephalography (EEG)-based brain-computer interface (BCI). The participant spent three sessions exploring the system, and seven sessions playing a game focused on EEG feedback training of left and right arm motor imagery and a customised, training game paradigm was employed. The participant showed improvement in the production of two distinct EEG patterns. The participant's performance was influenced by motivation, fatigue and concentration. Six weeks post-training the participant could still control the BCI and used this to type a sentence using an augmentative and alternative communication application on a wirelessly linked device. The results from this case study highlight the importance of creating a dynamic, relevant and engaging training environment for BCIs. Implications for Rehabilitation Customising a training paradigm to suit the users' interests can influence adherence to assistive technology training. Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces, which require little set up time, may be used as access technology for individuals with severe disabilities.

Human-computer interface incorporating personal and application domains

Science.gov (United States)

Anderson, Thomas G [Albuquerque, NM

2011-03-29

The present invention provides a human-computer interface. The interface includes provision of an application domain, for example corresponding to a three-dimensional application. The user is allowed to navigate and interact with the application domain. The interface also includes a personal domain, offering the user controls and interaction distinct from the application domain. The separation into two domains allows the most suitable interface methods in each: for example, three-dimensional navigation in the application domain, and two- or three-dimensional controls in the personal domain. Transitions between the application domain and the personal domain are under control of the user, and the transition method is substantially independent of the navigation in the application domain. For example, the user can fly through a three-dimensional application domain, and always move to the personal domain by moving a cursor near one extreme of the display.

Nanostructured interfaces for enhancing mechanical properties of composites: Computational micromechanical studies

DEFF Research Database (Denmark)

Mishnaevsky, Leon

2015-01-01

Computational micromechanical studies of the effect of nanostructuring and nanoengineering of interfaces, phase and grain boundaries of materials on the mechanical properties and strength of materials and the potential of interface nanostructuring to enhance the materials properties are reviewed....

Brain-Computer Interfaces Revolutionizing Human-Computer Interaction

CERN Document Server

Graimann, Bernhard; Allison, Brendan

2010-01-01

A brain-computer interface (BCI) establishes a direct output channel between the human brain and external devices. BCIs infer user intent via direct measures of brain activity and thus enable communication and control without movement. This book, authored by experts in the field, provides an accessible introduction to the neurophysiological and signal-processing background required for BCI, presents state-of-the-art non-invasive and invasive approaches, gives an overview of current hardware and software solutions, and reviews the most interesting as well as new, emerging BCI applications. The book is intended not only for students and young researchers, but also for newcomers and other readers from diverse backgrounds keen to learn about this vital scientific endeavour.

Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface.

Science.gov (United States)

Yu, Yang; Zhou, Zongtan; Yin, Erwei; Jiang, Jun; Tang, Jingsheng; Liu, Yadong; Hu, Dewen

2016-10-01

This study presented a paradigm for controlling a car using an asynchronous electroencephalogram (EEG)-based brain-computer interface (BCI) and presented the experimental results of a simulation performed in an experimental environment outside the laboratory. This paradigm uses two distinct MI tasks, imaginary left- and right-hand movements, to generate a multi-task car control strategy consisting of starting the engine, moving forward, turning left, turning right, moving backward, and stopping the engine. Five healthy subjects participated in the online car control experiment, and all successfully controlled the car by following a previously outlined route. Subject S1 exhibited the most satisfactory BCI-based performance, which was comparable to the manual control-based performance. We hypothesize that the proposed self-paced car control paradigm based on EEG signals could potentially be used in car control applications, and we provide a complementary or alternative way for individuals with locked-in disorders to achieve more mobility in the future, as well as providing a supplementary car-driving strategy to assist healthy people in driving a car. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain-Computer Interface.

Science.gov (United States)

Horschig, Jörn M; Oosterheert, Wouter; Oostenveld, Robert; Jensen, Ole

2015-11-01

Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain-computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing.

Third Workshop on Affective Brain-Computer Interfaces: introduction

NARCIS (Netherlands)

Mühl, C.; Chanel, G.; Allison, B.; Nijholt, Antinus

2013-01-01

Following the first and second workshop on affective brain-computer interfaces, held in conjunction with ACII in Amsterdam (2009) and Memphis (2011), the third workshop explores the advantages and limitations of using neurophysiological signals for the automatic recognition of affective and

High Performance Computing - Power Application Programming Interface Specification Version 1.4

Energy Technology Data Exchange (ETDEWEB)

Laros III, James H. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); DeBonis, David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kelly, Suzanne M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Levenhagen, Michael J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Olivier, Stephen Lecler [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Pedretti, Kevin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-10-01

Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area [13, 3, 5, 10, 4, 21, 19, 16, 7, 17, 20, 18, 11, 1, 6, 14, 12]. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.

A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities

Science.gov (United States)

Moghimi, Saba; Kushki, Azadeh; Guerguerian, Anne Marie; Chau, Tom

2013-01-01

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals.…

Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

Science.gov (United States)

Mahmoudi, Babak; Erfanian, Abbas

2006-11-01

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Performance monitoring for brain-computer-interface actions.

Science.gov (United States)

Schurger, Aaron; Gale, Steven; Gozel, Olivia; Blanke, Olaf

2017-02-01

When presented with a difficult perceptual decision, human observers are able to make metacognitive judgements of subjective certainty. Such judgements can be made independently of and prior to any overt response to a sensory stimulus, presumably via internal monitoring. Retrospective judgements about one's own task performance, on the other hand, require first that the subject perform a task and thus could potentially be made based on motor processes, proprioceptive, and other sensory feedback rather than internal monitoring. With this dichotomy in mind, we set out to study performance monitoring using a brain-computer interface (BCI), with which subjects could voluntarily perform an action - moving a cursor on a computer screen - without any movement of the body, and thus without somatosensory feedback. Real-time visual feedback was available to subjects during training, but not during the experiment where the true final position of the cursor was only revealed after the subject had estimated where s/he thought it had ended up after 6s of BCI-based cursor control. During the first half of the experiment subjects based their assessments primarily on the prior probability of the end position of the cursor on previous trials. However, during the second half of the experiment subjects' judgements moved significantly closer to the true end position of the cursor, and away from the prior. This suggests that subjects can monitor task performance when the task is performed without overt movement of the body. Copyright © 2016 Elsevier Inc. All rights reserved.

A brain-computer interface to support functional recovery.

Science.gov (United States)

Kjaer, Troels W; Sørensen, Helge B

2013-01-01

Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period. Copyright © 2013 S. Karger AG, Basel.

Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

Science.gov (United States)

Spüler, Martin; Rosenstiel, Wolfgang; Bogdan, Martin

2012-01-01

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

A binary motor imagery tasks based brain-computer interface for two-dimensional movement control

Science.gov (United States)

Xia, Bin; Cao, Lei; Maysam, Oladazimi; Li, Jie; Xie, Hong; Su, Caixia; Birbaumer, Niels

2017-12-01

Objective. Two-dimensional movement control is a popular issue in brain-computer interface (BCI) research and has many applications in the real world. In this paper, we introduce a combined control strategy to a binary class-based BCI system that allows the user to move a cursor in a two-dimensional (2D) plane. Users focus on a single moving vector to control 2D movement instead of controlling vertical and horizontal movement separately. Approach. Five participants took part in a fixed-target experiment and random-target experiment to verify the effectiveness of the combination control strategy under the fixed and random routine conditions. Both experiments were performed in a virtual 2D dimensional environment and visual feedback was provided on the screen. Main results. The five participants achieved an average hit rate of 98.9% and 99.4% for the fixed-target experiment and the random-target experiment, respectively. Significance. The results demonstrate that participants could move the cursor in the 2D plane effectively. The proposed control strategy is based only on a basic two-motor imagery BCI, which enables more people to use it in real-life applications.

Brain-computer interface for alertness estimation and improving

Science.gov (United States)

Hramov, Alexander; Maksimenko, Vladimir; Hramova, Marina

2018-02-01

Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.

CAMAPPLE: CAMAC interface to the Apple computer

International Nuclear Information System (INIS)

Oxoby, G.J.; Trang, Q.H.; Williams, S.H.

1981-04-01

The advent of the personal microcomputer provides a new tool for the debugging, calibration and monitoring of small scale physics apparatus, e.g., a single detector being developed for a larger physics apparatus. With an appropriate interface these microcomputer systems provide a low cost (1/3 the cost of a comparable minicomputer system), convenient, dedicated, portable system which can be used in a fashion similar to that of portable oscilloscopes. Here, an interface between the Apple computer and CAMAC which is now being used to study the detector for a Cerenkov ring-imaging device is described. The Apple is particularly well-suited to this application because of its ease of use, hi-resolution graphics, peripheral bus and documentation support

Student teaching and research laboratory focusing on brain-computer interface paradigms--A creative environment for computer science students.

Science.gov (United States)

Rutkowski, Tomasz M

2015-08-01

This paper presents an applied concept of a brain-computer interface (BCI) student research laboratory (BCI-LAB) at the Life Science Center of TARA, University of Tsukuba, Japan. Several successful case studies of the student projects are reviewed together with the BCI Research Award 2014 winner case. The BCI-LAB design and project-based teaching philosophy is also explained. Future teaching and research directions summarize the review.

Interfacing computers and the internet with your allergy practice.

Science.gov (United States)

Bernstein, Jonathan A

2004-10-01

Computers and the internet have begun to play a prominent role in the medical profession and, in particular, the allergy specialty. Computer technology is being used more frequently for patient and physician education, asthma management in children and adults, including environmental control, generating patient databases for research and clinical practice and in marketing and e-commerce. This article will review how computers and the internet have begun to interface with the allergy subspecialty practice in these various areas.

The web-based user interface for EAST plasma control system

Energy Technology Data Exchange (ETDEWEB)

Zhang, R.R., E-mail: rrzhang@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Xiao, B.J. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); School of Nuclear Science and Technology, University of Science and Technology of China, Anhui (China); Yuan, Q.P. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Yang, F. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Department of Computer Science, Anhui Medical University, Anhui (China); Zhang, Y. [Institute of Plasma Physics, Chinese Academy of Sciences, Anhui (China); Johnson, R.D.; Penaflor, B.G. [General Atomics, DIII-D National Fusion Facility, San Diego, CA (United States)

2014-05-15

The plasma control system (PCS) plays a vital role at EAST for fusion science experiments. Its software application consists of two main parts: an IDL graphical user interface for setting a large number of plasma parameters to specify each discharge, several programs for performing the real-time feedback control and managing the whole control system. The PCS user interface can be used from any X11 Windows client with privileged access to the PCS computer system. However, remote access to the PCS system via the IDL user interface becomes an extreme inconvenience due to the high network latency to draw or operate the interfaces. In order to realize lower latency for remote access to the PCS system, a web-based system has been developed for EAST recently. The setup data are retrieved from the PCS system and client-side JavaScript draws the interfaces into the user's browser. The user settings are also sent back to the PCS system for controlling discharges. These technologies allow the web-based user interface to be viewed by authorized users with a web browser and have it communicate with PCS server processes directly. It works together with the IDL interface and provides a new way to aid remote participation.

The web-based user interface for EAST plasma control system

International Nuclear Information System (INIS)

Zhang, R.R.; Xiao, B.J.; Yuan, Q.P.; Yang, F.; Zhang, Y.; Johnson, R.D.; Penaflor, B.G.

2014-01-01

The plasma control system (PCS) plays a vital role at EAST for fusion science experiments. Its software application consists of two main parts: an IDL graphical user interface for setting a large number of plasma parameters to specify each discharge, several programs for performing the real-time feedback control and managing the whole control system. The PCS user interface can be used from any X11 Windows client with privileged access to the PCS computer system. However, remote access to the PCS system via the IDL user interface becomes an extreme inconvenience due to the high network latency to draw or operate the interfaces. In order to realize lower latency for remote access to the PCS system, a web-based system has been developed for EAST recently. The setup data are retrieved from the PCS system and client-side JavaScript draws the interfaces into the user's browser. The user settings are also sent back to the PCS system for controlling discharges. These technologies allow the web-based user interface to be viewed by authorized users with a web browser and have it communicate with PCS server processes directly. It works together with the IDL interface and provides a new way to aid remote participation

Personal computer interface for temmperature measuring in the cutting process with turning

International Nuclear Information System (INIS)

Trajchevski, Neven; Filipovski, Velimir; Kuzinonovski, Mikolaj

2004-01-01

The computer development aided reserch systems in the investigations of the characteristics of the surface layar forms conditions for decreasing of the measuring uncertainty. Especially important is the fact that the usage of open and self made measuring systems accomplishes the demands for a total control of the research process. This paper describes an original personal computer interface which is used in the newly built computer aided reserrch system for temperatute measuring in the machining with turning. This interface consists of optically-coupled linear isolation amplifier and an analog to digital (A/D) converter. It is designed for measuring of the themo- voltage that is a generated from the natural thermocouple workpiece-cutting tool. That is achived by digitalizing the value of the thermo-voltage in data which is transmitted to the personal computer. The interface realization is a result of the research activity of the faculty of Mechanical Engineering and the Faculty of Electrical Engineering in Skopje.

High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

Energy Technology Data Exchange (ETDEWEB)

Zhang, Feng; Zhang, Xin; Xie, Jun; Li, Yeping; Han, Chengcheng; Lili, Li; Wang, Jing [School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049 (China); Xu, Guang-Hua [School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049 (China); State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710054 (China)

2015-03-10

This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method; Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces n{sup n} with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method.

High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

International Nuclear Information System (INIS)

Zhang, Feng; Zhang, Xin; Xie, Jun; Li, Yeping; Han, Chengcheng; Lili, Li; Wang, Jing; Xu, Guang-Hua

2015-01-01

This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method; Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces n n with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method

Real-time brain computer interface using imaginary movements

DEFF Research Database (Denmark)

El-Madani, Ahmad; Sørensen, Helge Bjarup Dissing; Kjær, Troels W.

2015-01-01

Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper...

Mind the Sheep! User Experience Evaluation & Brain-Computer Interface Games

NARCIS (Netherlands)

Gürkök, Hayrettin

2012-01-01

A brain-computer interface (BCI) infers our actions (e.g. a movement), intentions (e.g. preparation for a movement) and psychological states (e.g. emotion, attention) by interpreting our brain signals. It uses the inferences it makes to manipulate a computer. Although BCIs have long been used

Effects of muscle fatigue on the usability of a myoelectric human-computer interface.

Science.gov (United States)

Barszap, Alexander G; Skavhaug, Ida-Maria; Joshi, Sanjay S

2016-10-01

Electromyography-based human-computer interface development is an active field of research. However, knowledge on the effects of muscle fatigue for specific devices is limited. We have developed a novel myoelectric human-computer interface in which subjects continuously navigate a cursor to targets by manipulating a single surface electromyography (sEMG) signal. Two-dimensional control is achieved through simultaneous adjustments of power in two frequency bands through a series of dynamic low-level muscle contractions. Here, we investigate the potential effects of muscle fatigue during the use of our interface. In the first session, eight subjects completed 300 cursor-to-target trials without breaks; four using a wrist muscle and four using a head muscle. The wrist subjects returned for a second session in which a static fatiguing exercise took place at regular intervals in-between cursor-to-target trials. In the first session we observed no declines in performance as a function of use, even after the long period of use. In the second session, we observed clear changes in cursor trajectories, paired with a target-specific decrease in hit rates. Copyright © 2016 Elsevier B.V. All rights reserved.

Si Interface Barrier Modification on Memristor for Brain-Inspired Computing

Science.gov (United States)

Wu, Wei; Wu, Huaqiang; Gao, Bin; Qian, He

2017-06-01

Memristor is an emerging technology aimed at implementing neuromorphic computing in hardware system. Resistive random access memory (RRAM) is a kind of memristor with excellent performance, but abrupt switching in the set process influences the efficiency of neuromorphic system. In this study, we present an interface switching memristor device based on TiN/Si/TaOx/TiN stack and CMOS compatible fabrication process to achieve gradually resistive switching both in set and reset processes. The devices show a more than 10 switching window. The related switching mechanism is discussed.

Asynchronous P300-based brain-computer interface to control a virtual environment: initial tests on end users.

Science.gov (United States)

Aloise, Fabio; Schettini, Francesca; Aricò, Pietro; Salinari, Serenella; Guger, Christoph; Rinsma, Johanna; Aiello, Marco; Mattia, Donatella; Cincotti, Febo

2011-10-01

Motor disability and/or ageing can prevent individuals from fully enjoying home facilities, thus worsening their quality of life. Advances in the field of accessible user interfaces for domotic appliances can represent a valuable way to improve the independence of these persons. An asynchronous P300-based Brain-Computer Interface (BCI) system was recently validated with the participation of healthy young volunteers for environmental control. In this study, the asynchronous P300-based BCI for the interaction with a virtual home environment was tested with the participation of potential end-users (clients of a Frisian home care organization) with limited autonomy due to ageing and/or motor disabilities. System testing revealed that the minimum number of stimulation sequences needed to achieve correct classification had a higher intra-subject variability in potential end-users with respect to what was previously observed in young controls. Here we show that the asynchronous modality performed significantly better as compared to the synchronous mode in continuously adapting its speed to the users' state. Furthermore, the asynchronous system modality confirmed its reliability in avoiding misclassifications and false positives, as previously shown in young healthy subjects. The asynchronous modality may contribute to filling the usability gap between BCI systems and traditional input devices, representing an important step towards their use in the activities of daily living.

The Impact of User Interface on Young Children's Computational Thinking

Science.gov (United States)

Pugnali, Alex; Sullivan, Amanda; Bers, Marina Umaschi

2017-01-01

Aim/Purpose: Over the past few years, new approaches to introducing young children to computational thinking have grown in popularity. This paper examines the role that user interfaces have on children's mastery of computational thinking concepts and positive interpersonal behaviors. Background: There is a growing pressure to begin teaching…

The Future of Brain-Computer Interfacing (keynote paper)

NARCIS (Netherlands)

Nijholt, Antinus

In this paper we survey some early applications and research on brain-computer interfacing. We emphasize and revalue the role the views on artistic and playful applications have played. In previous years various road maps for BCI research appeared. The interest in medical applications has guided BCI

Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

Directory of Open Access Journals (Sweden)

Burke R

2005-01-01

Full Text Available This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.

Interdisciplinary research and education at the biology-engineering-computer science interface: a perspective.

Science.gov (United States)

Tadmor, Brigitta; Tidor, Bruce

2005-09-01

Progress in the life sciences, including genome sequencing and high-throughput experimentation, offers an opportunity for understanding biology and medicine from a systems perspective. This 'new view', which complements the more traditional component-based approach, involves the integration of biological research with approaches from engineering disciplines and computer science. The result is more than a new set of technologies. Rather, it promises a fundamental reconceptualization of the life sciences based on the development of quantitative and predictive models to describe crucial processes. To achieve this change, learning communities are being formed at the interface of the life sciences, engineering and computer science. Through these communities, research and education will be integrated across disciplines and the challenges associated with multidisciplinary team-based science will be addressed.

On the tip of the tongue: learning typing and pointing with an intra-oral computer interface.

Science.gov (United States)

Caltenco, Héctor A; Breidegard, Björn; Struijk, Lotte N S Andreasen

2014-07-01

To evaluate typing and pointing performance and improvement over time of four able-bodied participants using an intra-oral tongue-computer interface for computer control. A physically disabled individual may lack the ability to efficiently control standard computer input devices. There have been several efforts to produce and evaluate interfaces that provide individuals with physical disabilities the possibility to control personal computers. Training with the intra-oral tongue-computer interface was performed by playing games over 18 sessions. Skill improvement was measured through typing and pointing exercises at the end of each training session. Typing throughput improved from averages of 2.36 to 5.43 correct words per minute. Pointing throughput improved from averages of 0.47 to 0.85 bits/s. Target tracking performance, measured as relative time on target, improved from averages of 36% to 47%. Path following throughput improved from averages of 0.31 to 0.83 bits/s and decreased to 0.53 bits/s with more difficult tasks. Learning curves support the notion that the tongue can rapidly learn novel motor tasks. Typing and pointing performance of the tongue-computer interface is comparable to performances of other proficient assistive devices, which makes the tongue a feasible input organ for computer control. Intra-oral computer interfaces could provide individuals with severe upper-limb mobility impairments the opportunity to control computers and automatic equipment. Typing and pointing performance of the tongue-computer interface is comparable to performances of other proficient assistive devices, but does not cause fatigue easily and might be invisible to other people, which is highly prioritized by assistive device users. Combination of visual and auditory feedback is vital for a good performance of an intra-oral computer interface and helps to reduce involuntary or erroneous activations.

Exploring methodological frameworks for a mental task-based near-infrared spectroscopy brain-computer interface.

Science.gov (United States)

Weyand, Sabine; Takehara-Nishiuchi, Kaori; Chau, Tom

2015-10-30

Near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs) enable users to interact with their environment using only cognitive activities. This paper presents the results of a comparison of four methodological frameworks used to select a pair of tasks to control a binary NIRS-BCI; specifically, three novel personalized task paradigms and the state-of-the-art prescribed task framework were explored. Three types of personalized task selection approaches were compared, including: user-selected mental tasks using weighted slope scores (WS-scores), user-selected mental tasks using pair-wise accuracy rankings (PWAR), and researcher-selected mental tasks using PWAR. These paradigms, along with the state-of-the-art prescribed mental task framework, where mental tasks are selected based on the most commonly used tasks in literature, were tested by ten able-bodied participants who took part in five NIRS-BCI sessions. The frameworks were compared in terms of their accuracy, perceived ease-of-use, computational time, user preference, and length of training. Most notably, researcher-selected personalized tasks resulted in significantly higher accuracies, while user-selected personalized tasks resulted in significantly higher perceived ease-of-use. It was also concluded that PWAR minimized the amount of data that needed to be collected; while, WS-scores maximized user satisfaction and minimized computational time. In comparison to the state-of-the-art prescribed mental tasks, our findings show that overall, personalized tasks appear to be superior to prescribed tasks with respect to accuracy and perceived ease-of-use. The deployment of personalized rather than prescribed mental tasks ought to be considered and further investigated in future NIRS-BCI studies. Copyright © 2015 Elsevier B.V. All rights reserved.

Camapple: CAMAC interface to the Apple computer

International Nuclear Information System (INIS)

Oxoby, G.J.; Trang, Q.H.; Williams, S.H.

1981-01-01

The advent of the 'personal' microcomputer provides a new tool for the debugging, calibration and monitoring of small scale physics apparatus, e.g., a single detector being developed for a larger physics apparatus. With an appropriate interface these microcomputer systems provide a low cost (1/3 the cost of a comparable minicomputer system), convenient, dedicated, portable system which can be used in a fashion similar to that of portable oscilliscopes. Here we describe an interface between the Apple computer and CAMAC which is now being used to study the detector for a Cerenkov ring-imaging device. The Apple is particularly well-suited to this application because of its ease of use, hi-resolution graphics, peripheral bus and documentation support. (orig.)

Competing and collaborating brains: multi-brain computer interfacing

NARCIS (Netherlands)

Nijholt, Antinus; Hassanieu, Aboul Ella; Azar, Ahmad Taher

2015-01-01

In this chapter we survey the possibilities of brain-computer interface applications that assume two or more users, where at least one of the users’ brain activity is used as input to the application. Such ‘applications’ were already explored by artists who introduced artistic EEG applications in

Design guidelines for the use of audio cues in computer interfaces

Energy Technology Data Exchange (ETDEWEB)

Sumikawa, D.A.; Blattner, M.M.; Joy, K.I.; Greenberg, R.M.

1985-07-01

A logical next step in the evolution of the computer-user interface is the incorporation of sound thereby using our senses of ''hearing'' in our communication with the computer. This allows our visual and auditory capacities to work in unison leading to a more effective and efficient interpretation of information received from the computer than by sight alone. In this paper we examine earcons, which are audio cues, used in the computer-user interface to provide information and feedback to the user about computer entities (these include messages and functions, as well as states and labels). The material in this paper is part of a larger study that recommends guidelines for the design and use of audio cues in the computer-user interface. The complete work examines the disciplines of music, psychology, communication theory, advertising, and psychoacoustics to discover how sound is utilized and analyzed in those areas. The resulting information is organized according to the theory of semiotics, the theory of signs, into the syntax, semantics, and pragmatics of communication by sound. Here we present design guidelines for the syntax of earcons. Earcons are constructed from motives, short sequences of notes with a specific rhythm and pitch, embellished by timbre, dynamics, and register. Compound earcons and family earcons are introduced. These are related motives that serve to identify a family of related cues. Examples of earcons are given.

Facial pressure zones of an oronasal interface for noninvasive ventilation: a computer model analysis

Directory of Open Access Journals (Sweden)

Luana Souto Barros

2014-12-01

Full Text Available OBJECTIVE: To study the effects of an oronasal interface (OI for noninvasive ventilation, using a three-dimensional (3D computational model with the ability to simulate and evaluate the main pressure zones (PZs of the OI on the human face. METHODS: We used a 3D digital model of the human face, based on a pre-established geometric model. The model simulated soft tissues, skull, and nasal cartilage. The geometric model was obtained by 3D laser scanning and post-processed for use in the model created, with the objective of separating the cushion from the frame. A computer simulation was performed to determine the pressure required in order to create the facial PZs. We obtained descriptive graphical images of the PZs and their intensity. RESULTS: For the graphical analyses of each face-OI model pair and their respective evaluations, we ran 21 simulations. The computer model identified several high-impact PZs in the nasal bridge and paranasal regions. The variation in soft tissue depth had a direct impact on the amount of pressure applied (438-724 cmH2O. CONCLUSIONS: The computer simulation results indicate that, in patients submitted to noninvasive ventilation with an OI, the probability of skin lesion is higher in the nasal bridge and paranasal regions. This methodology could increase the applicability of biomechanical research on noninvasive ventilation interfaces, providing the information needed in order to choose the interface that best minimizes the risk of skin lesion.

Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.

Science.gov (United States)

Zhang, Shen; Zheng, Yanchun; Wang, Daifa; Wang, Ling; Ma, Jianai; Zhang, Jing; Xu, Weihao; Li, Deyu; Zhang, Dan

2017-08-10

Motor imagery is one of the most investigated paradigms in the field of brain-computer interfaces (BCIs). The present study explored the feasibility of applying a common spatial pattern (CSP)-based algorithm for a functional near-infrared spectroscopy (fNIRS)-based motor imagery BCI. Ten participants performed kinesthetic imagery of their left- and right-hand movements while 20-channel fNIRS signals were recorded over the motor cortex. The CSP method was implemented to obtain the spatial filters specific for both imagery tasks. The mean, slope, and variance of the CSP filtered signals were taken as features for BCI classification. Results showed that the CSP-based algorithm outperformed two representative channel-wise methods for classifying the two imagery statuses using either data from all channels or averaged data from imagery responsive channels only (oxygenated hemoglobin: CSP-based: 75.3±13.1%; all-channel: 52.3±5.3%; averaged: 64.8±13.2%; deoxygenated hemoglobin: CSP-based: 72.3±13.0%; all-channel: 48.8±8.2%; averaged: 63.3±13.3%). Furthermore, the effectiveness of the CSP method was also observed for the motor execution data to a lesser extent. A partial correlation analysis revealed significant independent contributions from all three types of features, including the often-ignored variance feature. To our knowledge, this is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs. Copyright © 2017 Elsevier B.V. All rights reserved.

Ecological Interface Design for Computer Network Defense.

Science.gov (United States)

Bennett, Kevin B; Bryant, Adam; Sushereba, Christen

2018-05-01

A prototype ecological interface for computer network defense (CND) was developed. Concerns about CND run high. Although there is a vast literature on CND, there is some indication that this research is not being translated into operational contexts. Part of the reason may be that CND has historically been treated as a strictly technical problem, rather than as a socio-technical problem. The cognitive systems engineering (CSE)/ecological interface design (EID) framework was used in the analysis and design of the prototype interface. A brief overview of CSE/EID is provided. EID principles of design (i.e., direct perception, direct manipulation and visual momentum) are described and illustrated through concrete examples from the ecological interface. Key features of the ecological interface include (a) a wide variety of alternative visual displays, (b) controls that allow easy, dynamic reconfiguration of these displays, (c) visual highlighting of functionally related information across displays, (d) control mechanisms to selectively filter massive data sets, and (e) the capability for easy expansion. Cyber attacks from a well-known data set are illustrated through screen shots. CND support needs to be developed with a triadic focus (i.e., humans interacting with technology to accomplish work) if it is to be effective. Iterative design and formal evaluation is also required. The discipline of human factors has a long tradition of success on both counts; it is time that HF became fully involved in CND. Direct application in supporting cyber analysts.

A Research Roadmap for Computation-Based Human Reliability Analysis

Energy Technology Data Exchange (ETDEWEB)

Boring, Ronald [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Joe, Jeffrey [Idaho National Lab. (INL), Idaho Falls, ID (United States); Smith, Curtis [Idaho National Lab. (INL), Idaho Falls, ID (United States); Groth, Katrina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-08-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is often secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.

A Research Roadmap for Computation-Based Human Reliability Analysis

International Nuclear Information System (INIS)

Boring, Ronald; Mandelli, Diego; Joe, Jeffrey; Smith, Curtis; Groth, Katrina

2015-01-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is often secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.

Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

Science.gov (United States)

Brumberg, Jonathan S; Nguyen, Anh; Pitt, Kevin M; Lorenz, Sean D

2018-01-31

We investigated how overt visual attention and oculomotor control influence successful use of a visual feedback brain-computer interface (BCI) for accessing augmentative and alternative communication (AAC) devices in a heterogeneous population of individuals with profound neuromotor impairments. BCIs are often tested within a single patient population limiting generalization of results. This study focuses on examining individual sensory abilities with an eye toward possible interface adaptations to improve device performance. Five individuals with a range of neuromotor disorders participated in four-choice BCI control task involving the steady state visually evoked potential. The BCI graphical interface was designed to simulate a commercial AAC device to examine whether an integrated device could be used successfully by individuals with neuromotor impairment. All participants were able to interact with the BCI and highest performance was found for participants able to employ an overt visual attention strategy. For participants with visual deficits to due to impaired oculomotor control, effective performance increased after accounting for mismatches between the graphical layout and participant visual capabilities. As BCIs are translated from research environments to clinical applications, the assessment of BCI-related skills will help facilitate proper device selection and provide individuals who use BCI the greatest likelihood of immediate and long term communicative success. Overall, our results indicate that adaptations can be an effective strategy to reduce barriers and increase access to BCI technology. These efforts should be directed by comprehensive assessments for matching individuals to the most appropriate device to support their complex communication needs. Implications for Rehabilitation Brain computer interfaces using the steady state visually evoked potential can be integrated with an augmentative and alternative communication device to provide access

A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder.

Directory of Open Access Journals (Sweden)

Choon Guan Lim

Full Text Available Attention deficit hyperactivity disorder (ADHD symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females with significant inattentive symptoms (combined and inattentive ADHD subtypes. This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was -4.6 (5.9 and -4.7 (5.6 respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01. Cohen's d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters. Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD.ClinicalTrials.gov NCT01344044.

Brain-computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: Proof of concept study.

Science.gov (United States)

Kasashima-Shindo, Yuko; Fujiwara, Toshiyuki; Ushiba, Junichi; Matsushika, Yayoi; Kamatani, Daiki; Oto, Misa; Ono, Takashi; Nishimoto, Atsuko; Shindo, Keiichiro; Kawakami, Michiyuki; Tsuji, Tetsuya; Liu, Meigen

2015-04-01

Brain-computer interface technology has been applied to stroke patients to improve their motor function. Event-related desynchronization during motor imagery, which is used as a brain-computer interface trigger, is sometimes difficult to detect in stroke patients. Anodal transcranial direct current stimulation (tDCS) is known to increase event-related desynchronization. This study investigated the adjunctive effect of anodal tDCS for brain-computer interface training in patients with severe hemiparesis. Eighteen patients with chronic stroke. A non-randomized controlled study. Subjects were divided between a brain-computer interface group and a tDCS- brain-computer interface group and participated in a 10-day brain-computer interface training. Event-related desynchronization was detected in the affected hemisphere during motor imagery of the affected fingers. The tDCS-brain-computer interface group received anodal tDCS before brain-computer interface training. Event-related desynchronization was evaluated before and after the intervention. The Fugl-Meyer Assessment upper extremity motor score (FM-U) was assessed before, immediately after, and 3 months after, the intervention. Event-related desynchronization was significantly increased in the tDCS- brain-computer interface group. The FM-U was significantly increased in both groups. The FM-U improvement was maintained at 3 months in the tDCS-brain-computer interface group. Anodal tDCS can be a conditioning tool for brain-computer interface training in patients with severe hemiparetic stroke.

Interfacing of home-made photoacoustic spectrometer to computer

International Nuclear Information System (INIS)

Dhobale, A.R.; Chaturvedi, T.P.; Venkiteswaran, S.; Sastry, M.D.

1996-01-01

This report describes the interfacing of Photo Acoustic Spectrometer (PAS) fabricated in-house to a personal computer. This work was carried out to make a state of the art computer based spectrometer with provision for automatic background correction and also which gives hard copy of the spectrum. This report includes the development of software necessary to acquire data and for further processing of the signal. The monochromator used was modified for obtaining a +5 V pulse for each wavelength position. This pulse was further used for controlling the data acquisition and automatic increment of wavelength. Software program was developed in Quick Basic ver. 4.5 environment for acquisition, storage, display and analysis of the spectrum. The program displays on-line spectrum building up on the monitor. Another program converts the acquired spectrum into a normalized spectrum comparing with carbon spectrum stored already in addition to the S/N ratio improvement. The photo acoustic cell and chopper unit were also modified for improving the performance of the PAS unit. (author). 11 refs., 13 figs., 2 tabs

A structural approach to constructing perspective efficient and reliable human-computer interfaces

International Nuclear Information System (INIS)

Balint, L.

1989-01-01

The principles of human-computer interface (HCI) realizations are investigated with the aim of getting closer to a general framework and thus, to a more or less solid background of constructing perspective efficient, reliable and cost-effective human-computer interfaces. On the basis of characterizing and classifying the different HCI solutions, the fundamental problems of interface construction are pointed out especially with respect to human error occurrence possibilities. The evolution of HCI realizations is illustrated by summarizing the main properties of past, present and foreseeable future interface generations. HCI modeling is pointed out to be a crucial problem in theoretical and practical investigations. Suggestions concerning HCI structure (hierarchy and modularity), HCI functional dynamics (mapping from input to output information), minimization of human error caused system failures (error-tolerance, error-recovery and error-correcting) as well as cost-effective HCI design and realization methodology (universal and application-oriented vs. application-specific solutions) are presented. The concept of RISC-based and SCAMP-type HCI components is introduced with the aim of having a reduced interaction scheme in communication and a well defined architecture in HCI components' internal structure. HCI efficiency and reliability are dealt with, by taking into account complexity and flexibility. The application of fast computerized prototyping is also briefly investigated as an experimental device of achieving simple, parametrized, invariant HCI models. Finally, a concise outline of an approach of how to construct ideal HCI's is also suggested by emphasizing the open questions and the need of future work related to the proposals, as well. (author). 14 refs, 6 figs

Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.

Science.gov (United States)

Guger, C; Schlögl, A; Walterspacher, D; Pfurtscheller, G

1999-01-01

An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.

[The P300 based brain-computer interface: effect of stimulus position in a stimulus train].

Science.gov (United States)

Ganin, I P; Shishkin, S L; Kochetova, A G; Kaplan, A Ia

2012-01-01

The P300 brain-computer interface (BCI) is currently the most efficient BCI. This interface is based on detection of the P300 wave of the brain potentials evoked when a symbol related to the intended input is highlighted. To increase operation speed of the P300 BCI, reduction of the number of stimuli repetitions is needed. This reduction leads to increase of the relative contribution to the input symbol detection from the reaction to the first target stimulus. It is known that the event-related potentials (ERP) to the first stimulus presentations can be different from the ERP to stimuli presented latter. In particular, the amplitude of responses to the first stimulus presentations is often increased, which is beneficial for their recognition by the BCI. However, this effect was not studied within the BCI framework. The current study examined the ERP obtained from healthy participants (n = 14) in the standard P300 BCI paradigm using 10 trials, as well as in the modified P300 BCI with stimuli presented on moving objects in triple-trial (n = 6) and single-trial (n = 6) stimulation modes. Increased ERP amplitude was observed in response to the first target stimuli in both conditions, as well as in the single-trial mode comparing to triple-trial. We discuss the prospects of using the specific features of the ERP to first stimuli and the single-trial ERP for optimizing the high-speed modes in the P300 BCIs.

Improvement of computer complex and interface system for compact nuclear simulator

International Nuclear Information System (INIS)

Lee, D. Y.; Park, W. M.; Cha, K. H.; Jung, C. H.; Park, J. C.

1999-01-01

CNS(Compact Nuclear Simulator) was developed at the end of 1980s, and have been used as training simulator for staffs of KAERI during 10 years. The operator panel interface cards and the graphic interface cards were designed with special purpose only for CNS. As these interface cards were worn out for 10 years, it was very difficult to get spare parts and to repair them. And the interface cards were damaged by over current happened by shortage of lamp in the operator panel. To solve these problem, the project 'Improvement of Compact Nuclear Simulator' was started from 1997. This paper only introduces about the improvement of computer complex and interface system

Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

Science.gov (United States)

Wei, Qingguo; Wei, Zhonghai

2015-01-01

A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

Eye-gaze independent EEG-based brain-computer interfaces for communication

Science.gov (United States)

Riccio, A.; Mattia, D.; Simione, L.; Olivetti, M.; Cincotti, F.

2012-08-01

The present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain-computer interfaces (BCIs) for communication. BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users’ requirements in a real-life scenario.

Z-score linear discriminant analysis for EEG based brain-computer interfaces.

Directory of Open Access Journals (Sweden)

Rui Zhang

Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

A P300 brain-computer interface based on a modification of the mismatch negativity paradigm.

Science.gov (United States)

Jin, Jing; Sellers, Eric W; Zhou, Sijie; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

2015-05-01

The P300-based brain-computer interface (BCI) is an extension of the oddball paradigm, and can facilitate communication for people with severe neuromuscular disorders. It has been shown that, in addition to the P300, other event-related potential (ERP) components have been shown to contribute to successful operation of the P300 BCI. Incorporating these components into the classification algorithm can improve the classification accuracy and information transfer rate (ITR). In this paper, a single character presentation paradigm was compared to a presentation paradigm that is based on the visual mismatch negativity. The mismatch negativity paradigm showed significantly higher classification accuracy and ITRs than a single character presentation paradigm. In addition, the mismatch paradigm elicited larger N200 and N400 components than the single character paradigm. The components elicited by the presentation method were consistent with what would be expected from a mismatch paradigm and a typical P300 was also observed. The results show that increasing the signal-to-noise ratio by increasing the amplitude of ERP components can significantly improve BCI speed and accuracy. The mismatch presentation paradigm may be considered a viable option to the traditional P300 BCI paradigm.

A CAMAC-system crate interface based on EUR 6100 standards

International Nuclear Information System (INIS)

Wolstenholme, P.; Verelst, H.; Parker, C.R.C.B.

1980-01-01

The particle beams in the Intersecting Storage Rings of the CERN accelerator are controlled by means of a CAMAC Branch Highway and three CAMAC Serial Highway Systems. The highway controllers, housed in system crates, are driven by crate controllers interfaced to the control computers. The crate controllers are implemented as auxiliary controllers and are PROM based to allow N, A and F to be generated from a small zone of computer addresses and are largely transparent to the host computer. The concept has enabled standard modules and techniques taken from the Serial Highway to be adopted for use in system crates. (Auth.)

Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials

Science.gov (United States)

2014-01-01

Background People with severe disabilities, e.g. due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g. recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system. Methods Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N = 15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm. Results Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed. Conclusion We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control. PMID:24428900

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

Directory of Open Access Journals (Sweden)

Alonso-Valerdi Luz María

2017-01-01

Full Text Available Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI. Those cognitive processes take place while a user navigates and explores a virtual environment (VE and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI. BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1 set out working environmental conditions, (2 maximize the efficiency of BCI control panels, (3 implement navigation systems based not only on user intentions but also on user emotions, and (4 regulate user mental state to increase the differentiation between control and noncontrol modalities.

A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials.

Science.gov (United States)

Jin, Jing; Allison, Brendan Z; Wang, Xingyu; Neuper, Christa

2012-04-15

Brain-computer interfaces (BCIs) allow users to communicate via brain activity alone. Many BCIs rely on the P300 and other event-related potentials (ERPs) that are elicited when target stimuli flash. Although there have been considerable research exploring ways to improve P300 BCIs, surprisingly little work has focused on new ways to change visual stimuli to elicit more recognizable ERPs. In this paper, we introduce a "combined" BCI based on P300 potentials and motion-onset visual evoked potentials (M-VEPs) and compare it with BCIs based on each simple approach (P300 and M-VEP). Offline data suggested that performance would be best in the combined paradigm. Online tests with adaptive BCIs confirmed that our combined approach is practical in an online BCI, and yielded better performance than the other two approaches (P<0.05) without annoying or overburdening the subject. The highest mean classification accuracy (96%) and practical bit rate (26.7bit/s) were obtained from the combined condition. Copyright © 2012 Elsevier B.V. All rights reserved.

User-customized brain computer interfaces using Bayesian optimization.

Science.gov (United States)

Bashashati, Hossein; Ward, Rabab K; Bashashati, Ali

2016-04-01

The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject's brain characteristics. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Curvature computation in volume-of-fluid method based on point-cloud sampling

Science.gov (United States)

Kassar, Bruno B. M.; Carneiro, João N. E.; Nieckele, Angela O.

2018-01-01

This work proposes a novel approach to compute interface curvature in multiphase flow simulation based on Volume of Fluid (VOF) method. It is well documented in the literature that curvature and normal vector computation in VOF may lack accuracy mainly due to abrupt changes in the volume fraction field across the interfaces. This may cause deterioration on the interface tension forces estimates, often resulting in inaccurate results for interface tension dominated flows. Many techniques have been presented over the last years in order to enhance accuracy in normal vectors and curvature estimates including height functions, parabolic fitting of the volume fraction, reconstructing distance functions, coupling Level Set method with VOF, convolving the volume fraction field with smoothing kernels among others. We propose a novel technique based on a representation of the interface by a cloud of points. The curvatures and the interface normal vectors are computed geometrically at each point of the cloud and projected onto the Eulerian grid in a Front-Tracking manner. Results are compared to benchmark data and significant reduction on spurious currents as well as improvement in the pressure jump are observed. The method was developed in the open source suite OpenFOAM® extending its standard VOF implementation, the interFoam solver.

Brain-computer interface on the basis of EEG system Encephalan

Science.gov (United States)

Maksimenko, Vladimir; Badarin, Artem; Nedaivozov, Vladimir; Kirsanov, Daniil; Hramov, Alexander

2018-04-01

We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.

Detection of User Independent Single Trial ERPs in Brain Computer Interfaces: An Adaptive Spatial Filtering Approach

DEFF Research Database (Denmark)

Leza, Cristina; Puthusserypady, Sadasivan

2017-01-01

Brain Computer Interfaces (BCIs) use brain signals to communicate with the external world. The main challenges to address are speed, accuracy and adaptability. Here, a novel algorithm for P300 based BCI spelling system is presented, specifically suited for single-trial detection of Event...

Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke.

Science.gov (United States)

Mukaino, Masahiko; Ono, Takashi; Shindo, Keiichiro; Fujiwara, Toshiyuki; Ota, Tetsuo; Kimura, Akio; Liu, Meigen; Ushiba, Junichi

2014-04-01

Brain computer interface technology is of great interest to researchers as a potential therapeutic measure for people with severe neurological disorders. The aim of this study was to examine the efficacy of brain computer interface, by comparing conventional neuromuscular electrical stimulation and brain computer interface-driven neuromuscular electrical stimulation, using an A-B-A-B withdrawal single-subject design. A 38-year-old male with severe hemiplegia due to a putaminal haemorrhage participated in this study. The design involved 2 epochs. In epoch A, the patient attempted to open his fingers during the application of neuromuscular electrical stimulation, irrespective of his actual brain activity. In epoch B, neuromuscular electrical stimulation was applied only when a significant motor-related cortical potential was observed in the electroencephalogram. The subject initially showed diffuse functional magnetic resonance imaging activation and small electro-encephalogram responses while attempting finger movement. Epoch A was associated with few neurological or clinical signs of improvement. Epoch B, with a brain computer interface, was associated with marked lateralization of electroencephalogram (EEG) and blood oxygenation level dependent responses. Voluntary electromyogram (EMG) activity, with significant EEG-EMG coherence, was also prompted. Clinical improvement in upper-extremity function and muscle tone was observed. These results indicate that self-directed training with a brain computer interface may induce activity- dependent cortical plasticity and promote functional recovery. This preliminary clinical investigation encourages further research using a controlled design.

Effects of mobile phone-based app learning compared to computer-based web learning on nursing students: pilot randomized controlled trial.

Science.gov (United States)

Lee, Myung Kyung

2015-04-01

This study aimed to determine the effect of mobile-based discussion versus computer-based discussion on self-directed learning readiness, academic motivation, learner-interface interaction, and flow state. This randomized controlled trial was conducted at one university. Eighty-six nursing students who were able to use a computer, had home Internet access, and used a mobile phone were recruited. Participants were randomly assigned to either the mobile phone app-based discussion group (n = 45) or a computer web-based discussion group (n = 41). The effect was measured at before and after an online discussion via self-reported surveys that addressed academic motivation, self-directed learning readiness, time distortion, learner-learner interaction, learner-interface interaction, and flow state. The change in extrinsic motivation on identified regulation in the academic motivation (p = 0.011) as well as independence and ability to use basic study (p = 0.047) and positive orientation to the future in self-directed learning readiness (p = 0.021) from pre-intervention to post-intervention was significantly more positive in the mobile phone app-based group compared to the computer web-based discussion group. Interaction between learner and interface (p = 0.002), having clear goals (p = 0.012), and giving and receiving unambiguous feedback (p = 0.049) in flow state was significantly higher in the mobile phone app-based discussion group than it was in the computer web-based discussion group at post-test. The mobile phone might offer more valuable learning opportunities for discussion teaching and learning methods in terms of self-directed learning readiness, academic motivation, learner-interface interaction, and the flow state of the learning process compared to the computer.

Comparison of four classification methods for brain-computer interface

Czech Academy of Sciences Publication Activity Database

Frolov, A.; Húsek, Dušan; Bobrov, P.

2011-01-01

Roč. 21, č. 2 (2011), s. 101-115 ISSN 1210-0552 R&D Projects: GA MŠk(CZ) 1M0567; GA ČR GA201/05/0079; GA ČR GAP202/10/0262 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * motor imagery * visual imagery * EEG pattern classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science Impact factor: 0.646, year: 2011

The Emotiv EPOC interface paradigm in Human-Computer Interaction

OpenAIRE

Ancău Dorina; Roman Nicolae-Marius; Ancău Mircea

2017-01-01

Numerous studies have suggested the use of decoded error potentials in the brain to improve human-computer communication. Together with state-of-the-art scientific equipment, experiments have also tested instruments with more limited performance for the time being, such as Emotiv EPOC. This study presents a review of these trials and a summary of the results obtained. However, the level of these results indicates a promising prospect for using this headset as a human-computer interface for er...

Social Interaction in a Cooperative Brain-computer Interface Game

NARCIS (Netherlands)

Obbink, Michel; Gürkök, Hayrettin; Plass - Oude Bos, D.; Hakvoort, Gido; Poel, Mannes; Nijholt, Antinus; Camurri, Antonio; Costa, Cristina

Does using a brain-computer interface (BCI) influence the social interaction between people when playing a cooperative game? By measuring the amount of speech, utterances, instrumental gestures and empathic gestures during a cooperative game where two participants had to reach a certain goal, and

Remote media vision-based computer input device

Science.gov (United States)

Arabnia, Hamid R.; Chen, Ching-Yi

1991-11-01

In this paper, we introduce a vision-based computer input device which has been built at the University of Georgia. The user of this system gives commands to the computer without touching any physical device. The system receives input through a CCD camera; it is PC- based and is built on top of the DOS operating system. The major components of the input device are: a monitor, an image capturing board, a CCD camera, and some software (developed by use). These are interfaced with a standard PC running under the DOS operating system.

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface

Science.gov (United States)

Ng, Kian B.; Bradley, Andrew P.; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.

Science.gov (United States)

Huggins, Jane E; Guger, Christoph; Allison, Brendan; Anderson, Charles W; Batista, Aaron; Brouwer, Anne-Marie A-M; Brunner, Clemens; Chavarriaga, Ricardo; Fried-Oken, Melanie; Gunduz, Aysegul; Gupta, Disha; Kübler, Andrea; Leeb, Robert; Lotte, Fabien; Miller, Lee E; Müller-Putz, Gernot; Rutkowski, Tomasz; Tangermann, Michael; Thompson, David Edward

2014-01-01

The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7 th , 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.

An associative Brain-Computer-Interface for acute stroke patients

DEFF Research Database (Denmark)

Mrachacz-Kersting, Natalie; Stevenson, Andrew James Thomas; Aliakbaryhosseinabadi, Susan

2016-01-01

An efficient innovative Brain-Computer-Interface system that empowers chronic stroke patients to control an artificial activation of their lower limb muscle through task specific motor intent has been tested in the past. In the current study it was applied to acute stroke patients. The system...

NOSTOS: a paper-based ubiquitous computing healthcare environment to support data capture and collaboration.

Science.gov (United States)

Bång, Magnus; Larsson, Anders; Eriksson, Henrik

2003-01-01

In this paper, we present a new approach to clinical workplace computerization that departs from the window-based user interface paradigm. NOSTOS is an experimental computer-augmented work environment designed to support data capture and teamwork in an emergency room. NOSTOS combines multiple technologies, such as digital pens, walk-up displays, headsets, a smart desk, and sensors to enhance an existing paper-based practice with computer power. The physical interfaces allow clinicians to retain mobile paper-based collaborative routines and still benefit from computer technology. The requirements for the system were elicited from situated workplace studies. We discuss the advantages and disadvantages of augmenting a paper-based clinical work environment.

Open-Box Muscle-Computer Interface: Introduction to Human-Computer Interactions in Bioengineering, Physiology, and Neuroscience Courses

Science.gov (United States)

Landa-Jiménez, M. A.; González-Gaspar, P.; Pérez-Estudillo, C.; López-Meraz, M. L.; Morgado-Valle, C.; Beltran-Parrazal, L.

2016-01-01

A Muscle-Computer Interface (muCI) is a human-machine system that uses electromyographic (EMG) signals to communicate with a computer. Surface EMG (sEMG) signals are currently used to command robotic devices, such as robotic arms and hands, and mobile robots, such as wheelchairs. These signals reflect the motor intention of a user before the…

A Development of Lightweight Grid Interface

International Nuclear Information System (INIS)

Iwai, G; Kawai, Y; Sasaki, T; Watase, Y

2011-01-01

In order to help a rapid development of Grid/Cloud aware applications, we have developed API to abstract the distributed computing infrastructures based on SAGA (A Simple API for Grid Applications). SAGA, which is standardized in the OGF (Open Grid Forum), defines API specifications to access distributed computing infrastructures, such as Grid, Cloud and local computing resources. The Universal Grid API (UGAPI), which is a set of command line interfaces (CLI) and APIs, aims to offer simpler API to combine several SAGA interfaces with richer functionalities. These CLIs of the UGAPI offer typical functionalities required by end users for job management and file access to the different distributed computing infrastructures as well as local computing resources. We have also built a web interface for the particle therapy simulation and demonstrated the large scale calculation using the different infrastructures at the same time. In this paper, we would like to present how the web interface based on UGAPI and SAGA achieve more efficient utilization of computing resources over the different infrastructures with technical details and practical experiences.

Brain Computer Interface on Track to Home

OpenAIRE

Miralles, Felip; Vargiu, Eloisa; Dauwalder, Stefan; Solà, Marc; Müller-Putz, Gernot; Wriessnegger, Selina C.; Pinegger, Andreas; Kübler, Andrea; Halder, Sebastian; Käthner, Ivo; Martin, Suzanne; Daly, Jean; Armstrong, Elaine; Guger, Christoph; Hintermüller, Christoph

2015-01-01

The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users' home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within ...

New generation of 3D desktop computer interfaces

Science.gov (United States)

Skerjanc, Robert; Pastoor, Siegmund

1997-05-01

Today's computer interfaces use 2-D displays showing windows, icons and menus and support mouse interactions for handling programs and data files. The interface metaphor is that of a writing desk with (partly) overlapping sheets of documents placed on its top. Recent advances in the development of 3-D display technology give the opportunity to take the interface concept a radical stage further by breaking the design limits of the desktop metaphor. The major advantage of the envisioned 'application space' is, that it offers an additional, immediately perceptible dimension to clearly and constantly visualize the structure and current state of interrelations between documents, videos, application programs and networked systems. In this context, we describe the development of a visual operating system (VOS). Under VOS, applications appear as objects in 3-D space. Users can (graphically connect selected objects to enable communication between the respective applications. VOS includes a general concept of visual and object oriented programming for tasks ranging from, e.g., low-level programming up to high-level application configuration. In order to enable practical operation in an office or at home for many hours, the system should be very comfortable to use. Since typical 3-D equipment used, e.g., in virtual-reality applications (head-mounted displays, data gloves) is rather cumbersome and straining, we suggest to use off-head displays and contact-free interaction techniques. In this article, we introduce an autostereoscopic 3-D display and connected video based interaction techniques which allow viewpoint-depending imaging (by head tracking) and visually controlled modification of data objects and links (by gaze tracking, e.g., to pick, 3-D objects just by looking at them).

The Emotiv EPOC interface paradigm in Human-Computer Interaction

Directory of Open Access Journals (Sweden)

Ancău Dorina

2017-01-01

Full Text Available Numerous studies have suggested the use of decoded error potentials in the brain to improve human-computer communication. Together with state-of-the-art scientific equipment, experiments have also tested instruments with more limited performance for the time being, such as Emotiv EPOC. This study presents a review of these trials and a summary of the results obtained. However, the level of these results indicates a promising prospect for using this headset as a human-computer interface for error decoding.

[The current state of the brain-computer interface problem].

Science.gov (United States)

Shurkhay, V A; Aleksandrova, E V; Potapov, A A; Goryainov, S A

2015-01-01

It was only 40 years ago that the first PC appeared. Over this period, rather short in historical terms, we have witnessed the revolutionary changes in lives of individuals and the entire society. Computer technologies are tightly connected with any field, either directly or indirectly. We can currently claim that computers are manifold superior to a human mind in terms of a number of parameters; however, machines lack the key feature: they are incapable of independent thinking (like a human). However, the key to successful development of humankind is collaboration between the brain and the computer rather than competition. Such collaboration when a computer broadens, supplements, or replaces some brain functions is known as the brain-computer interface. Our review focuses on real-life implementation of this collaboration.

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

Science.gov (United States)

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-11-30

Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images.

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Jacob, Mithun George; Wachs, Juan Pablo; Packer, Rebecca A

2013-06-01

This paper presents a method to improve the navigation and manipulation of radiological images through a sterile hand gesture recognition interface based on attentional contextual cues. Computer vision algorithms were developed to extract intention and attention cues from the surgeon's behavior and combine them with sensory data from a commodity depth camera. The developed interface was tested in a usability experiment to assess the effectiveness of the new interface. An image navigation and manipulation task was performed, and the gesture recognition accuracy, false positives and task completion times were computed to evaluate system performance. Experimental results show that gesture interaction and surgeon behavior analysis can be used to accurately navigate, manipulate and access MRI images, and therefore this modality could replace the use of keyboard and mice-based interfaces.

Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces.

Directory of Open Access Journals (Sweden)

Florent Bocquelet

2016-11-01

Full Text Available Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN trained on electromagnetic articulography (EMA data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer.

Brain-computer interface analysis of a dynamic visuo-motor task.

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Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

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Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface

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Norton, James J. S.; Lee, Dong Sup; Lee, Jung Woo; Lee, Woosik; Kwon, Ohjin; Won, Phillip; Jung, Sung-Young; Cheng, Huanyu; Jeong, Jae-Woong; Akce, Abdullah; Umunna, Stephen; Na, Ilyoun; Kwon, Yong Ho; Wang, Xiao-Qi; Liu, ZhuangJian; Paik, Ungyu; Huang, Yonggang; Bretl, Timothy; Yeo, Woon-Hong; Rogers, John A.

2015-01-01

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave). PMID:25775550

Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface.

Science.gov (United States)

Norton, James J S; Lee, Dong Sup; Lee, Jung Woo; Lee, Woosik; Kwon, Ohjin; Won, Phillip; Jung, Sung-Young; Cheng, Huanyu; Jeong, Jae-Woong; Akce, Abdullah; Umunna, Stephen; Na, Ilyoun; Kwon, Yong Ho; Wang, Xiao-Qi; Liu, ZhuangJian; Paik, Ungyu; Huang, Yonggang; Bretl, Timothy; Yeo, Woon-Hong; Rogers, John A

2015-03-31

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain-computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain-computer interface and elicitation of an event-related potential (P300 wave).

User-customized brain computer interfaces using Bayesian optimization

Science.gov (United States)

Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

2016-04-01

Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Recommendations for Integrating a P300-Based Brain Computer Interface in Virtual Reality Environments for Gaming

Directory of Open Access Journals (Sweden)

Grégoire Cattan

2018-05-01

Full Text Available The integration of a P300-based brain–computer interface (BCI into virtual reality (VR environments is promising for the video games industry. However, it faces several limitations, mainly due to hardware constraints and constraints engendered by the stimulation needed by the BCI. The main limitation is still the low transfer rate that can be achieved by current BCI technology. The goal of this paper is to review current limitations and to provide application creators with design recommendations in order to overcome them. We also overview current VR and BCI commercial products in relation to the design of video games. An essential recommendation is to use the BCI only for non-complex and non-critical tasks in the game. Also, the BCI should be used to control actions that are naturally integrated into the virtual world. Finally, adventure and simulation games, especially if cooperative (multi-user appear the best candidates for designing an effective VR game enriched by BCI technology.

Acid chat: gestural interface design

OpenAIRE

Gökhan, Ali Oytun; Gokhan, Ali Oytun

2005-01-01

AcidChat is an experimental design project that aims to create an innovative computer software interface for Internet chat software using today's well known technologies; Adobe Photoshop, Macromedia Freehand and digital photography. The aim of the project is to create new understandings of interface and it's usage, by adding new conceptions to chat based interfaces which creates a totally new look at the computer software and application. One of the key features is to add a gestural approach ...

Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

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Ceballos, G. A.; Hernández, L. F.

2015-04-01

Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

Evolution of brain-computer interfaces: going beyond classic motor physiology

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Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W.

2010-01-01

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future. PMID:19569892

SCOWLP: a web-based database for detailed characterization and visualization of protein interfaces

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Schroeder Michael

2006-03-01

Full Text Available Abstract Background Currently there is a strong need for methods that help to obtain an accurate description of protein interfaces in order to be able to understand the principles that govern molecular recognition and protein function. Many of the recent efforts to computationally identify and characterize protein networks extract protein interaction information at atomic resolution from the PDB. However, they pay none or little attention to small protein ligands and solvent. They are key components and mediators of protein interactions and fundamental for a complete description of protein interfaces. Interactome profiling requires the development of computational tools to extract and analyze protein-protein, protein-ligand and detailed solvent interaction information from the PDB in an automatic and comparative fashion. Adding this information to the existing one on protein-protein interactions will allow us to better understand protein interaction networks and protein function. Description SCOWLP (Structural Characterization Of Water, Ligands and Proteins is a user-friendly and publicly accessible web-based relational database for detailed characterization and visualization of the PDB protein interfaces. The SCOWLP database includes proteins, peptidic-ligands and interface water molecules as descriptors of protein interfaces. It contains currently 74,907 protein interfaces and 2,093,976 residue-residue interactions formed by 60,664 structural units (protein domains and peptidic-ligands and their interacting solvent. The SCOWLP web-server allows detailed structural analysis and comparisons of protein interfaces at atomic level by text query of PDB codes and/or by navigating a SCOP-based tree. It includes a visualization tool to interactively display the interfaces and label interacting residues and interface solvent by atomic physicochemical properties. SCOWLP is automatically updated with every SCOP release. Conclusion SCOWLP enriches

Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS.

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Vansteensel, Mariska J; Pels, Elmar G M; Bleichner, Martin G; Branco, Mariana P; Denison, Timothy; Freudenburg, Zachary V; Gosselaar, Peter; Leinders, Sacha; Ottens, Thomas H; Van Den Boom, Max A; Van Rijen, Peter C; Aarnoutse, Erik J; Ramsey, Nick F

2016-11-24

Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patient's eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).

Affective three-dimensional brain-computer interface created using a prism array-based display

Science.gov (United States)

Mun, Sungchul; Park, Min-Chul

2014-12-01

To avoid the vergence-accommodation mismatch and provide a strong sense of presence to users, we applied a prism array-based display when presenting three-dimensional (3-D) objects. Emotional pictures were used as visual stimuli to increase the signal-to-noise ratios of steady-state visually evoked potentials (SSVEPs) because involuntarily motivated selective attention by affective mechanisms can enhance SSVEP amplitudes, thus producing increased interaction efficiency. Ten male and nine female participants voluntarily participated in our experiments. Participants were asked to control objects under three viewing conditions: two-dimension (2-D), stereoscopic 3-D, and prism. The participants performed each condition in a counter-balanced order. One-way repeated measures analysis of variance showed significant increases in the positive predictive values in the prism condition compared to the 2-D and 3-D conditions. Participants' subjective ratings of realness and engagement were also significantly greater in the prism condition than in the 2-D and 3-D conditions, while the ratings for visual fatigue were significantly reduced in the prism condition than in the 3-D condition. The proposed methods are expected to enhance the sense of reality in 3-D space without causing critical visual fatigue. In addition, people who are especially susceptible to stereoscopic 3-D may be able to use the affective brain-computer interface.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

Science.gov (United States)

2012-01-01

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

Eye gaze in intelligent user interfaces gaze-based analyses, models and applications

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Nakano, Yukiko I; Bader, Thomas

2013-01-01

Remarkable progress in eye-tracking technologies opened the way to design novel attention-based intelligent user interfaces, and highlighted the importance of better understanding of eye-gaze in human-computer interaction and human-human communication. For instance, a user's focus of attention is useful in interpreting the user's intentions, their understanding of the conversation, and their attitude towards the conversation. In human face-to-face communication, eye gaze plays an important role in floor management, grounding, and engagement in conversation.Eye Gaze in Intelligent User Interfac

A Graphical User Interface for the Computational Fluid Dynamics Software OpenFOAM

OpenAIRE

Melbø, Henrik Kaald

2014-01-01

A graphical user interface for the computational fluid dynamics software OpenFOAM has been constructed. OpenFOAM is a open source and powerful numerical software, but has much to be wanted in the field of user friendliness. In this thesis the basic operation of OpenFOAM will be introduced and the thesis will emerge in a graphical user interface written in PyQt. The graphical user interface will make the use of OpenFOAM simpler, and hopefully make this powerful tool more available for the gene...

Electric-field-controlled interface dipole modulation for Si-based memory devices.

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Miyata, Noriyuki

2018-05-31

Various nonvolatile memory devices have been investigated to replace Si-based flash memories or emulate synaptic plasticity for next-generation neuromorphic computing. A crucial criterion to achieve low-cost high-density memory chips is material compatibility with conventional Si technologies. In this paper, we propose and demonstrate a new memory concept, interface dipole modulation (IDM) memory. IDM can be integrated as a Si field-effect transistor (FET) based memory device. The first demonstration of this concept employed a HfO 2 /Si MOS capacitor where the interface monolayer (ML) TiO 2 functions as a dipole modulator. However, this configuration is unsuitable for Si-FET-based devices due to its large interface state density (D it ). Consequently, we propose, a multi-stacked amorphous HfO 2 /1-ML TiO 2 /SiO 2 IDM structure to realize a low D it and a wide memory window. Herein we describe the quasi-static and pulse response characteristics of multi-stacked IDM MOS capacitors and demonstrate flash-type and analog memory operations of an IDM FET device.

A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces.

Science.gov (United States)

Heo, Jeong; Yoon, Heenam; Park, Kwang Suk

2017-06-23

Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain-computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles.

U.S. Army weapon systems human-computer interface style guide. Version 2

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Avery, L.W.; O`Mara, P.A.; Shepard, A.P.; Donohoo, D.T.

1997-12-31

A stated goal of the US Army has been the standardization of the human computer interfaces (HCIs) of its system. Some of the tools being used to accomplish this standardization are HCI design guidelines and style guides. Currently, the Army is employing a number of HCI design guidance documents. While these style guides provide good guidance for the command, control, communications, computers, and intelligence (C4I) domain, they do not necessarily represent the more unique requirements of the Army`s real time and near-real time (RT/NRT) weapon systems. The Office of the Director of Information for Command, Control, Communications, and Computers (DISC4), in conjunction with the Weapon Systems Technical Architecture Working Group (WSTAWG), recognized this need as part of their activities to revise the Army Technical Architecture (ATA), now termed the Joint Technical Architecture-Army (JTA-A). To address this need, DISC4 tasked the Pacific Northwest National Laboratory (PNNL) to develop an Army weapon systems unique HCI style guide, which resulted in the US Army Weapon Systems Human-Computer Interface (WSHCI) Style Guide Version 1. Based on feedback from the user community, DISC4 further tasked PNNL to revise Version 1 and publish Version 2. The intent was to update some of the research and incorporate some enhancements. This document provides that revision. The purpose of this document is to provide HCI design guidance for the RT/NRT Army system domain across the weapon systems subdomains of ground, aviation, missile, and soldier systems. Each subdomain should customize and extend this guidance by developing their domain-specific style guides, which will be used to guide the development of future systems within their subdomains.

Effect of police mobile computer terminal interface design on officer driving distraction.

Science.gov (United States)

Zahabi, Maryam; Kaber, David

2018-02-01

Several crash reports have identified in-vehicle distraction to be a primary cause of emergency vehicle crashes especially in law enforcement. Furthermore, studies have found that mobile computer terminals (MCTs) are the most frequently used in-vehicle technology for police officers. Twenty police officers participated in a driving simulator-based assessment of visual behavior, performance, workload and situation awareness with current and enhanced MCT interface designs. In general, results revealed MCT use while driving to decrease officer visual attention to the roadway, but usability improvements can reduce the level of visual distraction and secondary-task completion time. Results also suggest that use of MCTs while driving significantly reduces perceived level of driving environment awareness for police officers and increases cognitive workload. These findings may be useful for MCT manufacturers in improving interface designs to increase police officer and civilian safety. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrated multimodal human-computer interface and augmented reality for interactive display applications

Science.gov (United States)

Vassiliou, Marius S.; Sundareswaran, Venkataraman; Chen, S.; Behringer, Reinhold; Tam, Clement K.; Chan, M.; Bangayan, Phil T.; McGee, Joshua H.

2000-08-01

We describe new systems for improved integrated multimodal human-computer interaction and augmented reality for a diverse array of applications, including future advanced cockpits, tactical operations centers, and others. We have developed an integrated display system featuring: speech recognition of multiple concurrent users equipped with both standard air- coupled microphones and novel throat-coupled sensors (developed at Army Research Labs for increased noise immunity); lip reading for improving speech recognition accuracy in noisy environments, three-dimensional spatialized audio for improved display of warnings, alerts, and other information; wireless, coordinated handheld-PC control of a large display; real-time display of data and inferences from wireless integrated networked sensors with on-board signal processing and discrimination; gesture control with disambiguated point-and-speak capability; head- and eye- tracking coupled with speech recognition for 'look-and-speak' interaction; and integrated tetherless augmented reality on a wearable computer. The various interaction modalities (speech recognition, 3D audio, eyetracking, etc.) are implemented a 'modality servers' in an Internet-based client-server architecture. Each modality server encapsulates and exposes commercial and research software packages, presenting a socket network interface that is abstracted to a high-level interface, minimizing both vendor dependencies and required changes on the client side as the server's technology improves.

Brain Computer Interface on Track to Home.

Science.gov (United States)

Miralles, Felip; Vargiu, Eloisa; Dauwalder, Stefan; Solà, Marc; Müller-Putz, Gernot; Wriessnegger, Selina C; Pinegger, Andreas; Kübler, Andrea; Halder, Sebastian; Käthner, Ivo; Martin, Suzanne; Daly, Jean; Armstrong, Elaine; Guger, Christoph; Hintermüller, Christoph; Lowish, Hannah

2015-01-01

The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users' home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

As We May Think and Be: Brain-computer interfaces to expand the substrate of mind

Directory of Open Access Journals (Sweden)

Mijail Demian Serruya

2015-04-01

Full Text Available Over a half-century ago, the scientist Vannevar Bush explored the conundrum of how to tap the exponentially rising sea of human knowledge for the betterment of humanity. In his description of a hypothetical electronic library he dubbed the memex, he anticipated internet search and online encyclopedias (Bush, 1945. By blurring the boundary between brain and computer, brain-computer interfaces (BCI could lead to more efficient use of electronic resources (Schalk, 2008. We could expand the substrate of the mind itself rather than merely interfacing it to external computers. Components of brain-computer interfaces could be re-arranged to create brain-brain interfaces, or tightly interconnected links between a person’s brain and ectopic neural modules. Such modules – whether sitting in a bubbling Petri dish, rendered in reciprocally linked integrated circuits, or implanted in our belly – would mark the first step on to a path of breaking out of the limitations imposed by our phylogenetic past Novel BCI architectures could generate novel abilities to navigate and access information that might speed translational science efforts and push the boundaries of human knowledge in an unprecedented manner.

Measuring Emotion Regulation with Single Dry Electrode Brain Computer Interface

NARCIS (Netherlands)

van der Wal, C.N.; Irrmischer, M.; Guo, Y.; Friston, K.; Faisal, A.; Hill, S.; Peng, H.

2015-01-01

Wireless brain computer interfaces (BCI’s) are promising for new intelligent applications in which emotions are detected by measuring brain activity. Applications, such as serious games and video game therapy, are measuring and using the user’s emotional state in order to determine the intensity

A Python-based interface to examine motions in time series of solar images

Science.gov (United States)

Campos-Rozo, J. I.; Vargas Domínguez, S.

2017-10-01

Python is considered to be a mature programming language, besides of being widely accepted as an engaging option for scientific analysis in multiple areas, as will be presented in this work for the particular case of solar physics research. SunPy is an open-source library based on Python that has been recently developed to furnish software tools to solar data analysis and visualization. In this work we present a graphical user interface (GUI) based on Python and Qt to effectively compute proper motions for the analysis of time series of solar data. This user-friendly computing interface, that is intended to be incorporated to the Sunpy library, uses a local correlation tracking technique and some extra tools that allows the selection of different parameters to calculate, vizualize and analyze vector velocity fields of solar data, i.e. time series of solar filtergrams and magnetograms.

The web based user interface of RODOS

International Nuclear Information System (INIS)

Raskob, W.; Mueller, A.; Munz, E.; Rafat, M.

2003-01-01

and platform independent web technology. This enables accessing the RODOS systems by remote users from all kinds of computer platforms with Internet browser. The layout and content structure of this web interface have been designed and developed with a unique standardized interface layout and information structure under due consideration of the needs of the RODOS users. Two types of web-based interfaces have been realized: category B: active user with access to the RODOS system via web browser. The interaction with RODOS is limited to the level (2) and (3) mentioned above: category B users can only define interactive runs via input forms and select results from predefined information. They have no access to data bases and cannot operate RODOS in its automatic mode. Category C: passive user with access via web browser and - if desired - via X-desktop only to RODOS results produced by users of category A or B. The category B users define their requests to the RODOS system via an interactive Web-based interface. The corresponding HTML file is sent to the RODOS Web server. lt transforms the information into RODOS compatible input data, initiates the corresponding RODOS runs, produces an HTML results file and returns it to the web browser. The web browser receives the HTML file, it interprets the page content and displays the page. The layout, content and functions of the new web based interface for category B and category C users will be demonstrated. Example interactive runs will show the interaction with the RODOS system. fig. 1 (author)

WIFIP: a web-based user interface for automated synchrotron beamlines.

Science.gov (United States)

Sallaz-Damaz, Yoann; Ferrer, Jean Luc

2017-09-01

The beamline control software, through the associated graphical user interface (GUI), is the user access point to the experiment, interacting with synchrotron beamline components and providing automated routines. FIP, the French beamline for the Investigation of Proteins, is a highly automatized macromolecular crystallography (MX) beamline at the European Synchrotron Radiation Facility. On such a beamline, a significant number of users choose to control their experiment remotely. This is often performed with a limited bandwidth and from a large choice of computers and operating systems. Furthermore, this has to be possible in a rapidly evolving experimental environment, where new developments have to be easily integrated. To face these challenges, a light, platform-independent, control software and associated GUI are required. Here, WIFIP, a web-based user interface developed at FIP, is described. Further than being the present FIP control interface, WIFIP is also a proof of concept for future MX control software.

Towards psychologically adaptive brain-computer interfaces

Science.gov (United States)

Myrden, A.; Chau, T.

2016-12-01

Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

Principles of Motor Recovery in Post-Stroke Patients using Hand Exoskeleton Controlled by the Brain-Computer Interface Based on Motor Imagery

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Biryukova, E. V.; Bobrov, P.; Mokienko, O.; Alexandrov, A.V.

2017-01-01

Roč. 27, č. 1 (2017), s. 107-137 ISSN 1210-0552 Grant - others:Russian Ministry of Education and Science(RU) RFMEFI60715X0128 Institutional support: RVO:67985807 Keywords : brain computer interface * motor imagery * post-stroke and post-traumatic patients * arm and hand exoskeleton * proportional derivative controller * motor synergy * clinical application Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.394, year: 2016

An automated meta-monitoring mobile application and front-end interface for the ATLAS computing model

Energy Technology Data Exchange (ETDEWEB)

Kawamura, Gen; Quadt, Arnulf [II. Physikalisches Institut, Georg-August-Universitaet Goettingen (Germany)

2016-07-01

Efficient administration of computing centres requires advanced tools for the monitoring and front-end interface of the infrastructure. Providing the large-scale distributed systems as a global grid infrastructure, like the Worldwide LHC Computing Grid (WLCG) and ATLAS computing, is offering many existing web pages and information sources indicating the status of the services, systems and user jobs at grid sites. A meta-monitoring mobile application which automatically collects the information could give every administrator a sophisticated and flexible interface of the infrastructure. We describe such a solution; the MadFace mobile application developed at Goettingen. It is a HappyFace compatible mobile application which has a user-friendly interface. It also becomes very feasible to automatically investigate the status and problem from different sources and provides access of the administration roles for non-experts.

Software and man-machine interface considerations for a nuclear plant computer replacement and upgrade project

International Nuclear Information System (INIS)

Diamond, G.; Robinson, E.

1984-01-01

Some of the key software functions and Man-Machine Interface considerations in a computer replacement and upgrade project for a nuclear power plant are described. The project involves the installation of two separate computer systems: an Emergency Response Facilities Computer System (ERFCS) and a Plant Process Computer System (PPCS). These systems employ state-of-the-art computer hardware and software. The ERFCS is a new system intended to provide enhanced functions to meet NRC post-TMI guidelines. The PPCS is intended to replace and upgrade an existing obsolete plant computer system. A general overview of the hardware and software aspects of the replacement and upgrade is presented. The work done to develop the upgraded Man-Machine Interface is described. For the ERFCS, a detailed discussion is presented of the work done to develop logic to evaluate the readiness and performance of safety systems and their supporting functions. The Man-Machine Interface considerations of reporting readiness and performance to the operator are discussed. Finally, the considerations involved in the implementation of this logic in real-time software are discussed.. For the PPCS, a detailed discussion is presented of some new features

Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

Science.gov (United States)

Stoica, Adrian; Barrero, David F.; McDonald-Maier, Klaus

2013-01-01

This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen and measured the time and accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting and a small number of people (either odd or even number) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response.

An overview of computer-based natural language processing

Science.gov (United States)

Gevarter, W. B.

1983-01-01

Computer based Natural Language Processing (NLP) is the key to enabling humans and their computer based creations to interact with machines in natural language (like English, Japanese, German, etc., in contrast to formal computer languages). The doors that such an achievement can open have made this a major research area in Artificial Intelligence and Computational Linguistics. Commercial natural language interfaces to computers have recently entered the market and future looks bright for other applications as well. This report reviews the basic approaches to such systems, the techniques utilized, applications, the state of the art of the technology, issues and research requirements, the major participants and finally, future trends and expectations. It is anticipated that this report will prove useful to engineering and research managers, potential users, and others who will be affected by this field as it unfolds.

Development and functional demonstration of a wireless intraoral inductive tongue computer interface for severely disabled persons.

Science.gov (United States)

N S Andreasen Struijk, Lotte; Lontis, Eugen R; Gaihede, Michael; Caltenco, Hector A; Lund, Morten Enemark; Schioeler, Henrik; Bentsen, Bo

2017-08-01

Individuals with tetraplegia depend on alternative interfaces in order to control computers and other electronic equipment. Current interfaces are often limited in the number of available control commands, and may compromise the social identity of an individual due to their undesirable appearance. The purpose of this study was to implement an alternative computer interface, which was fully embedded into the oral cavity and which provided multiple control commands. The development of a wireless, intraoral, inductive tongue computer was described. The interface encompassed a 10-key keypad area and a mouse pad area. This system was embedded wirelessly into the oral cavity of the user. The functionality of the system was demonstrated in two tetraplegic individuals and two able-bodied individuals Results: The system was invisible during use and allowed the user to type on a computer using either the keypad area or the mouse pad. The maximal typing rate was 1.8 s for repetitively typing a correct character with the keypad area and 1.4 s for repetitively typing a correct character with the mouse pad area. The results suggest that this inductive tongue computer interface provides an esthetically acceptable and functionally efficient environmental control for a severely disabled user. Implications for Rehabilitation New Design, Implementation and detection methods for intra oral assistive devices. Demonstration of wireless, powering and encapsulation techniques suitable for intra oral embedment of assistive devices. Demonstration of the functionality of a rechargeable and fully embedded intra oral tongue controlled computer input device.

Silicon CMOS architecture for a spin-based quantum computer.

Science.gov (United States)

Veldhorst, M; Eenink, H G J; Yang, C H; Dzurak, A S

2017-12-15

Recent advances in quantum error correction codes for fault-tolerant quantum computing and physical realizations of high-fidelity qubits in multiple platforms give promise for the construction of a quantum computer based on millions of interacting qubits. However, the classical-quantum interface remains a nascent field of exploration. Here, we propose an architecture for a silicon-based quantum computer processor based on complementary metal-oxide-semiconductor (CMOS) technology. We show how a transistor-based control circuit together with charge-storage electrodes can be used to operate a dense and scalable two-dimensional qubit system. The qubits are defined by the spin state of a single electron confined in quantum dots, coupled via exchange interactions, controlled using a microwave cavity, and measured via gate-based dispersive readout. We implement a spin qubit surface code, showing the prospects for universal quantum computation. We discuss the challenges and focus areas that need to be addressed, providing a path for large-scale quantum computing.

BCILAB: a platform for brain-computer interface development

Science.gov (United States)

Kothe, Christian Andreas; Makeig, Scott

2013-10-01

Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.

Ethical Issues in Brain-Computer Interface Research, Development, and Dissemination

NARCIS (Netherlands)

Vlek, Rutger; Steines, David; Szibbo, Dyana; Kübler, Andrea; Schneider, Mary-Jane; Haselager, Pim; Nijboer, Femke

The steadily growing field of brain-computer interfacing (BCI) may develop useful technologies, with a potential impact not only on individuals, but also on society as a whole. At the same time, the development of BCI presents significant ethical and legal challenges. In a workshop during the 4th

Ethical Issues in Brain-Computer Interface Research, Development, and Dissemination

NARCIS (Netherlands)

Vlek, R.J.; Steines, D.; Szibbo, D.; Kübler, A.; Schneider, M.J.; Haselager, W.F.G.; Nijboer, F.

2012-01-01

The steadily growing field of brain–computer interfacing (BCI) may develop useful technologies, with a potential impact not only on individuals, but also on society as a whole. At the same time, the development of BCI presents significant ethical and legal challenges. In a workshop during the 4th

Experimental evaluation of multimodal human computer interface for tactical audio applications

NARCIS (Netherlands)

Obrenovic, Z.; Starcevic, D.; Jovanov, E.; Oy, S.

2002-01-01

Mission critical and information overwhelming applications require careful design of the human computer interface. Typical applications include night vision or low visibility mission navigation, guidance through a hostile territory, and flight navigation and orientation. Additional channels of

Catalyzing Inquiry at the Interface of Computing and Biology

Energy Technology Data Exchange (ETDEWEB)

John Wooley; Herbert S. Lin

2005-10-30

This study is the first comprehensive NRC study that suggests a high-level intellectual structure for Federal agencies for supporting work at the biology/computing interface. The report seeks to establish the intellectual legitimacy of a fundamentally cross-disciplinary collaboration between biologists and computer scientists. That is, while some universities are increasingly favorable to research at the intersection, life science researchers at other universities are strongly impeded in their efforts to collaborate. This report addresses these impediments and describes proven strategies for overcoming them. An important feature of the report is the use of well-documented examples that describe clearly to individuals not trained in computer science the value and usage of computing across the biological sciences, from genes and proteins to networks and pathways, from organelles to cells, and from individual organisms to populations and ecosystems. It is hoped that these examples will be useful to students in the life sciences to motivate (continued) study in computer science that will enable them to be more facile users of computing in their future biological studies.

Web-based computational chemistry education with CHARMMing I: Lessons and tutorial.

Science.gov (United States)

Miller, Benjamin T; Singh, Rishi P; Schalk, Vinushka; Pevzner, Yuri; Sun, Jingjun; Miller, Carrie S; Boresch, Stefan; Ichiye, Toshiko; Brooks, Bernard R; Woodcock, H Lee

2014-07-01

This article describes the development, implementation, and use of web-based "lessons" to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing) web user interface (http://www.charmming.org). Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMing's new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e.g., phones and tablets), allowing easy integration of these advanced simulation techniques into coursework. Finally, one of the primary objections to web-based systems like CHARMMing has been that "point and click" simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance.

Web-based computational chemistry education with CHARMMing I: Lessons and tutorial.

Directory of Open Access Journals (Sweden)

Benjamin T Miller

2014-07-01

Full Text Available This article describes the development, implementation, and use of web-based "lessons" to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing web user interface (http://www.charmming.org. Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMing's new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e.g., phones and tablets, allowing easy integration of these advanced simulation techniques into coursework. Finally, one of the primary objections to web-based systems like CHARMMing has been that "point and click" simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance.

Specifying computer-based counseling systems in health care: a new approach to user-interface and interaction design.

Science.gov (United States)

Herzberg, Dominikus; Marsden, Nicola; Kübler, Peter; Leonhardt, Corinna; Thomanek, Sabine; Jung, Hartmut; Becker, Annette

2009-04-01

Computer-based counseling systems in health care play an important role in the toolset available for medical doctors to inform, motivate and challenge their patients according to a well-defined therapeutic goal. The design, development and implementation of such systems require close collaboration between users, i.e. patients, and developers. While this is true of any software development process, it can be particularly challenging in the health counseling field, where there are multiple specialties and extremely heterogeneous user groups. In order to facilitate a structured design approach for counseling systems in health care, we developed (a) an iterative three-staged specification process, which enables early involvement of potential users in the development process, and (b) a specification language, which enables an author to consistently describe and define user interfaces and interaction designs in a stepwise manner. Due to the formal nature of our specifications, our implementation has some unique features, like early execution of prototypes, automated system generation and verification capabilities.

A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.

Science.gov (United States)

Lotte, F; Bougrain, L; Cichocki, A; Clerc, M; Congedo, M; Rakotomamonjy, A; Yger, F

2018-06-01

Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

Science.gov (United States)

Lotte, F.; Bougrain, L.; Cichocki, A.; Clerc, M.; Congedo, M.; Rakotomamonjy, A.; Yger, F.

2018-06-01

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach. We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results. We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance. This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges

UIMX: A User Interface Management System For Scientific Computing With X Windows

Science.gov (United States)

Foody, Michael

1989-09-01

Applications with iconic user interfaces, (for example, interfaces with pulldown menus, radio buttons, and scroll bars), such as those found on Apple's Macintosh computer and the IBM PC under Microsoft's Presentation Manager, have become very popular, and for good reason. They are much easier to use than applications with traditional keyboard-oriented interfaces, so training costs are much lower and just about anyone can use them. They are standardized between applications, so once you learn one application you are well along the way to learning another. The use of one reinforces the common elements between applications of the interface, and, as a result, you remember how to use them longer. Finally, for the developer, their support costs can be much lower because of their ease of use.

A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation

Science.gov (United States)

Li, Zheng; Jiang, Yi-han; Duan, Lian; Zhu, Chao-zhe

2017-08-01

Objective. Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). Approach. GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. Main results. Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus  brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.

A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers.

Science.gov (United States)

Heidrich, Regina O; Jensen, Emely; Rebelo, Francisco; Oliveira, Tiago

2015-01-01

This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).

Using real-time fMRI brain-computer interfacing to treat eating disorders.

Science.gov (United States)

Sokunbi, Moses O

2018-05-15

Real-time functional magnetic resonance imaging based brain-computer interfacing (fMRI neurofeedback) has shown encouraging outcomes in the treatment of psychiatric and behavioural disorders. However, its use in the treatment of eating disorders is very limited. Here, we give a brief overview of how to design and implement fMRI neurofeedback intervention for the treatment of eating disorders, considering the basic and essential components. We also attempt to develop potential adaptations of fMRI neurofeedback intervention for the treatment of anorexia nervosa, bulimia nervosa and binge eating disorder. Copyright © 2018 Elsevier B.V. All rights reserved.

EXPERIMENTAL AND THEORETICAL FOUNDATIONS AND PRACTICAL IMPLEMENTATION OF TECHNOLOGY BRAIN-COMPUTER INTERFACE

Directory of Open Access Journals (Sweden)

A. Ya. Kaplan

2013-01-01

Full Text Available Technology brain-computer interface (BCI allow saperson to learn how to control external devices via thevoluntary regulation of own EEG directly from the brain without the involvement in the process of nerves and muscles. At the beginning the main goal of BCI was to replace or restore motor function to people disabled by neuromuscular disorders. Currently, the task of designing the BCI increased significantly, more capturing different aspects of life a healthy person. This article discusses the theoretical, experimental and technological base of BCI development and systematized critical fields of real implementation of these technologies.

Improving the accessibility at home: implementation of a domotic application using a p300-based brain computer interface system

Directory of Open Access Journals (Sweden)

Rebeca Corralejo Palacios

2012-05-01

Full Text Available The aim of this study was to develop a Brain Computer Interface (BCI application to control domotic devices usually present at home. Previous studies have shown that people with severe disabilities, both physical and cognitive ones, do not achieve high accuracy results using motor imagery-based BCIs. To overcome this limitation, we propose the implementation of a BCI application using P300 evoked potentials, because neither extensive training nor extremely high concentration level are required for this kind of BCIs. The implemented BCI application allows to control several devices as TV, DVD player, mini Hi-Fi system, multimedia hard drive, telephone, heater, fan and lights. Our aim is that potential users, i.e. people with severe disabilities, are able to achieve high accuracy. Therefore, this domotic BCI application is useful to increase

The Identification, Implementation, and Evaluation of Critical User Interface Design Features of Computer-Assisted Instruction Programs in Mathematics for Students with Learning Disabilities

Science.gov (United States)

Seo, You-Jin; Woo, Honguk

2010-01-01

Critical user interface design features of computer-assisted instruction programs in mathematics for students with learning disabilities and corresponding implementation guidelines were identified in this study. Based on the identified features and guidelines, a multimedia computer-assisted instruction program, "Math Explorer", which delivers…

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

Science.gov (United States)

Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

2015-08-01

Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

Science.gov (United States)

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Development traumatic brain injury computer user interface for disaster area in Indonesia supported by emergency broadband access network.

Science.gov (United States)

Sutiono, Agung Budi; Suwa, Hirohiko; Ohta, Toshizumi; Arifin, Muh Zafrullah; Kitamura, Yohei; Yoshida, Kazunari; Merdika, Daduk; Qiantori, Andri; Iskandar

2012-12-01

Disasters bring consequences of negative impacts on the environment and human life. One of the common cause of critical condition is traumatic brain injury (TBI), namely, epidural (EDH) and subdural hematoma (SDH), due to downfall hard things during earthquake. We proposed and analyzed the user response, namely neurosurgeon, general doctor/surgeon and nurse when they interacted with TBI computer interface. The communication systems was supported by TBI web based applications using emergency broadband access network with tethered balloon and simulated in the field trial to evaluate the coverage area. The interface consisted of demography data and multi tabs for anamnesis, treatment, follow up and teleconference interfaces. The interface allows neurosurgeon, surgeon/general doctors and nurses to entry the EDH and SDH patient's data during referring them on the emergency simulation and evaluated based on time needs and their understanding. The average time needed was obtained after simulated by Lenovo T500 notebook using mouse; 8-10 min for neurosurgeons, 12-15 min for surgeons/general doctors and 15-19 min for nurses. By using Think Pad X201 Tablet, the time needed for entry data was 5-7 min for neurosurgeon, 7-10 min for surgeons/general doctors and 12-16 min for nurses. We observed that the time difference was depending on the computer type and user literacy qualification as well as their understanding on traumatic brain injury, particularly for the nurses. In conclusion, there are five data classification for simply TBI GUI, namely, 1) demography, 2) specific anamnesis for EDH and SDH, 3) treatment action and medicine of TBI, 4) follow up data display and 5) teleneurosurgery for streaming video consultation. The type of computer, particularly tablet PC was more convenient and faster for entry data, compare to that computer mouse touched pad. Emergency broadband access network using tethered balloon is possible to be employed to cover the communications systems in

Proprioceptive feedback and brain computer interface (BCI based neuroprostheses.

Directory of Open Access Journals (Sweden)

Ander Ramos-Murguialday

Full Text Available Brain computer interface (BCI technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1 motor imagery of the hand movement without any overt movement and without feedback, (2 motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3 passive (the orthosis passively opens and closes the hand without imagery and (4 active (overt movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants. Group 1 (n = 9 received contingent positive feedback (participants' sensorimotor rhythm (SMR desynchronization was directly linked to hand orthosis movements, group 2 (n = 8 contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements and group 3 (n = 7 sham feedback (no link between brain oscillations and orthosis movements. We observed that proprioceptive feedback (feeling and seeing hand movements improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and

Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses.

Science.gov (United States)

Ramos-Murguialday, Ander; Schürholz, Markus; Caggiano, Vittorio; Wildgruber, Moritz; Caria, Andrea; Hammer, Eva Maria; Halder, Sebastian; Birbaumer, Niels

2012-01-01

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive

Interactive displays natural human-interface technologies

CERN Document Server

Bhowmik, Achintya K

2014-01-01

One of the first books to provide an in-depth discussion of the technologies, applications and trends in the rapidly emerging field of interactive displays (touch, gesture & voice) The book will cover the technologies, applications and trends in the field of interactive displays, namely interfaces based on touch, gesture and voice and those using a combination of these technologies. The book will be split into 4 main parts with each being dedicated to a specific user interface. Part 1 ''Touch Interfaces'' will provide a review of the currently deployed touch-screen technologies and applications. It will also cover the recent developments towards achieving thinner, lightweight and cost-reduced touch screen panels in the future via integration of touch functionalities. Part 2 ''Gesture Interfaces'' will examine techniques and applications in stereoscopic 3D computer vision, structured-light 3D computer vision and time-of-flight 3D computer vision in gesture interfaces. Part 3 ''Voice Interfaces'' will revie...

Orientation-modulated attention effect on visual evoked potential: Application for PIN system using brain-computer interface.

Science.gov (United States)

Wilaiprasitporn, Theerawit; Yagi, Tohru

2015-01-01

This research demonstrates the orientation-modulated attention effect on visual evoked potential. We combined this finding with our previous findings about the motion-modulated attention effect and used the result to develop novel visual stimuli for a personal identification number (PIN) application based on a brain-computer interface (BCI) framework. An electroencephalography amplifier with a single electrode channel was sufficient for our application. A computationally inexpensive algorithm and small datasets were used in processing. Seven healthy volunteers participated in experiments to measure offline performance. Mean accuracy was 83.3% at 13.9 bits/min. Encouraged by these results, we plan to continue developing the BCI-based personal identification application toward real-time systems.

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Science.gov (United States)

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

Interfacing a fieldable multichannel analyzer to a MicroVAX computer

International Nuclear Information System (INIS)

Litherland, K.R.; Johnson, M.W.

1990-01-01

This paper reports on software written for interfacing the D.S. Davidson Model 2056 portable multichannel analyzer to a MicroVAX computer running the VMS operating system. The operational objective of the software is to give the user a nearly transparent mechanism for controlling the analyzer with functions equivalent to those on the analyzer's own keyboard, thus minimizing the training requirement for the user. The software is written in VMS enhanced Fortran and consists of a main control program, several subprocesses, and libraries containing graphics commands and other information. Interfaces to other commercially available software packages for data storage and manipulation are provided. Problems encountered and their programming solutions are discussed

An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People.

Science.gov (United States)

Martinez-Cagigal, Victor; Gomez-Pilar, Javier; Alvarez, Daniel; Hornero, Roberto

2017-08-01

This paper presents an electroencephalographic (EEG) P300-based brain-computer interface (BCI) Internet browser. The system uses the "odd-ball" row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users. In addition, all of them were synchronous applications, where it was necessary to introduce a "read-mode" command in order to avoid a continuous command selection. Thus, the aim of this study is twofold: 1) to test our web browser with a population of multiple sclerosis (MS) patients in order to assess the usefulness of our proposal to meet their daily communication needs; and 2) to overcome the aforementioned limitation by adding a threshold that discerns between control and non-control states, allowing the user to calmly read the web page without undesirable selections. The browser was tested with sixteen MS patients and five healthy volunteers. Both quantitative and qualitative metrics were obtained. MS participants reached an average accuracy of 84.14%, whereas 95.75% was achieved by control subjects. Results show that MS patients can successfully control the BCI web browser, improving their personal autonomy.

User Interface History

DEFF Research Database (Denmark)

Jørgensen, Anker Helms; Myers, Brad A

2008-01-01

User Interfaces have been around as long as computers have existed, even well before the field of Human-Computer Interaction was established. Over the years, some papers on the history of Human-Computer Interaction and User Interfaces have appeared, primarily focusing on the graphical interface e...

The Graphical User Interface: Crisis, Danger, and Opportunity.

Science.gov (United States)

Boyd, L. H.; And Others

1990-01-01

This article describes differences between the graphical user interface and traditional character-based interface systems, identifies potential problems posed by graphic computing environments for blind computer users, and describes some programs and strategies that are being developed to provide access to those environments. (Author/JDD)

Treatment of human-computer interface in a decision support system

International Nuclear Information System (INIS)

Heger, A.S.; Duran, F.A.; Cox, R.G.

1992-01-01

One of the most challenging applications facing the computer community is development of effective adaptive human-computer interface. This challenge stems from the complex nature of the human part of this symbiosis. The application of this discipline to the environmental restoration and waste management is further complicated due to the nature of environmental data. The information that is required to manage environmental impacts of human activity is fundamentally complex. This paper will discuss the efforts at Sandia National Laboratories in developing the adaptive conceptual model manager within the constraint of the environmental decision-making. A computer workstation, that hosts the Conceptual Model Manager and the Sandia Environmental Decision Support System will also be discussed

Brain Computer Interface on Track to Home

Directory of Open Access Journals (Sweden)

Felip Miralles

2015-01-01

Full Text Available The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs, to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users’ home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

P300 brain computer interface: current challenges and emerging trends

Science.gov (United States)

Fazel-Rezai, Reza; Allison, Brendan Z.; Guger, Christoph; Sellers, Eric W.; Kleih, Sonja C.; Kübler, Andrea

2012-01-01

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility. PMID:22822397

Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP

Science.gov (United States)

Yi, Weibo; Qiu, Shuang; Wang, Kun; Qi, Hongzhi; Zhao, Xin; He, Feng; Zhou, Peng; Yang, Jiajia; Ming, Dong

2017-04-01

Objective. We proposed a novel simultaneous hybrid brain-computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated. Approach. 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously. Main results. The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature. Significance. The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.

Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

Directory of Open Access Journals (Sweden)

Molina Gary N Garcia

2003-01-01

Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

Directory of Open Access Journals (Sweden)

Matthew Sybeldon

2017-01-01

Full Text Available Brain–Computer Interfaces (BCI using Steady-State Visual Evoked Potentials (SSVEP are sometimes used by injured patients seeking to use a computer. Canonical Correlation Analysis (CCA is seen as state-of-the-art for SSVEP BCI systems. However, this assumes that the user has full control over their covert attention, which may not be the case. This introduces high calibration requirements when using other machine learning techniques. These may be circumvented by using transfer learning to utilize data from other participants. This paper proposes a combination of ensemble learning via Learn++ for Nonstationary Environments (Learn++.NSEand similarity measures such as mutual information to identify ensembles of pre-existing data that result in higher classification. Results show that this approach performed worse than CCA in participants with typical SSVEP responses, but outperformed CCA in participants whose SSVEP responses violated CCA assumptions. This indicates that similarity measures and Learn++.NSE can introduce a transfer learning mechanism to bring SSVEP system accessibility to users unable to control their covert attention.

Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface.

Science.gov (United States)

Bashford, Luke; Mehring, Carsten

2016-01-01

To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.

Intelligent Context-Aware and Adaptive Interface for Mobile LBS.

Science.gov (United States)

Feng, Jiangfan; Liu, Yanhong

2015-01-01

Context-aware user interface plays an important role in many human-computer Interaction tasks of location based services. Although spatial models for context-aware systems have been studied extensively, how to locate specific spatial information for users is still not well resolved, which is important in the mobile environment where location based services users are impeded by device limitations. Better context-aware human-computer interaction models of mobile location based services are needed not just to predict performance outcomes, such as whether people will be able to find the information needed to complete a human-computer interaction task, but to understand human processes that interact in spatial query, which will in turn inform the detailed design of better user interfaces in mobile location based services. In this study, a context-aware adaptive model for mobile location based services interface is proposed, which contains three major sections: purpose, adjustment, and adaptation. Based on this model we try to describe the process of user operation and interface adaptation clearly through the dynamic interaction between users and the interface. Then we show how the model applies users' demands in a complicated environment and suggested the feasibility by the experimental results.

Modeling soft factors in computer-based wargames

Science.gov (United States)

Alexander, Steven M.; Ross, David O.; Vinarskai, Jonathan S.; Farr, Steven D.

2002-07-01

Computer-based wargames have seen much improvement in recent years due to rapid increases in computing power. Because these games have been developed for the entertainment industry, most of these advances have centered on the graphics, sound, and user interfaces integrated into these wargames with less attention paid to the game's fidelity. However, for a wargame to be useful to the military, it must closely approximate as many of the elements of war as possible. Among the elements that are typically not modeled or are poorly modeled in nearly all military computer-based wargames are systematic effects, command and control, intelligence, morale, training, and other human and political factors. These aspects of war, with the possible exception of systematic effects, are individually modeled quite well in many board-based commercial wargames. The work described in this paper focuses on incorporating these elements from the board-based games into a computer-based wargame. This paper will also address the modeling and simulation of the systemic paralysis of an adversary that is implied by the concept of Effects Based Operations (EBO). Combining the fidelity of current commercial board wargames with the speed, ease of use, and advanced visualization of the computer can significantly improve the effectiveness of military decision making and education. Once in place, the process of converting board wargames concepts to computer wargames will allow the infusion of soft factors into military training and planning.

Brain-Computer Interface Spellers: A Review.

Science.gov (United States)

Rezeika, Aya; Benda, Mihaly; Stawicki, Piotr; Gembler, Felix; Saboor, Abdul; Volosyak, Ivan

2018-03-30

A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

Brain–Computer Interface Spellers: A Review

Science.gov (United States)

Gembler, Felix; Saboor, Abdul

2018-01-01

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers. PMID:29601538

Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits.

Science.gov (United States)

Alonso-Valerdi, Luz Maria; Salido-Ruiz, Ricardo Antonio; Ramirez-Mendoza, Ricardo A

2015-12-01

When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system. Copyright © 2015 Elsevier Ltd. All rights reserved.

Some computer graphical user interfaces in radiation therapy.

Science.gov (United States)

Chow, James C L

2016-03-28

In this review, five graphical user interfaces (GUIs) used in radiation therapy practices and researches are introduced. They are: (1) the treatment time calculator, superficial X-ray treatment time calculator (SUPCALC) used in the superficial X-ray radiation therapy; (2) the monitor unit calculator, electron monitor unit calculator (EMUC) used in the electron radiation therapy; (3) the multileaf collimator machine file creator, sliding window intensity modulated radiotherapy (SWIMRT) used in generating fluence map for research and quality assurance in intensity modulated radiation therapy; (4) the treatment planning system, DOSCTP used in the calculation of 3D dose distribution using Monte Carlo simulation; and (5) the monitor unit calculator, photon beam monitor unit calculator (PMUC) used in photon beam radiation therapy. One common issue of these GUIs is that all user-friendly interfaces are linked to complex formulas and algorithms based on various theories, which do not have to be understood and noted by the user. In that case, user only needs to input the required information with help from graphical elements in order to produce desired results. SUPCALC is a superficial radiation treatment time calculator using the GUI technique to provide a convenient way for radiation therapist to calculate the treatment time, and keep a record for the skin cancer patient. EMUC is an electron monitor unit calculator for electron radiation therapy. Instead of doing hand calculation according to pre-determined dosimetric tables, clinical user needs only to input the required drawing of electron field in computer graphical file format, prescription dose, and beam parameters to EMUC to calculate the required monitor unit for the electron beam treatment. EMUC is based on a semi-experimental theory of sector-integration algorithm. SWIMRT is a multileaf collimator machine file creator to generate a fluence map produced by a medical linear accelerator. This machine file controls

The computer-controlled GPIB-RS232 interface for data transmission

International Nuclear Information System (INIS)

Bai Xiaowei

1993-01-01

A kind of RS232-GPIB interface circuit is introduced, which provides communication between the serial system and the instrument with GPIB. Port P 1 of 8031 is used to select function mode as listener, talker or others. Under the control of a personal computer, the data communication is completed both in serial and the parallel modes

A Brain-Computer Interface (BCI) system to use arbitrary Windows applications by directly controlling mouse and keyboard.

Science.gov (United States)

Spuler, Martin

2015-08-01

A Brain-Computer Interface (BCI) allows to control a computer by brain activity only, without the need for muscle control. In this paper, we present an EEG-based BCI system based on code-modulated visual evoked potentials (c-VEPs) that enables the user to work with arbitrary Windows applications. Other BCI systems, like the P300 speller or BCI-based browsers, allow control of one dedicated application designed for use with a BCI. In contrast, the system presented in this paper does not consist of one dedicated application, but enables the user to control mouse cursor and keyboard input on the level of the operating system, thereby making it possible to use arbitrary applications. As the c-VEP BCI method was shown to enable very fast communication speeds (writing more than 20 error-free characters per minute), the presented system is the next step in replacing the traditional mouse and keyboard and enabling complete brain-based control of a computer.

Brain-computer interface after nervous system injury.

Science.gov (United States)

Burns, Alexis; Adeli, Hojjat; Buford, John A

2014-12-01

Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. © The Author(s) 2014.

Gaze-and-brain-controlled interfaces for human-computer and human-robot interaction

Directory of Open Access Journals (Sweden)

Shishkin S. L.

2017-09-01

Full Text Available Background. Human-machine interaction technology has greatly evolved during the last decades, but manual and speech modalities remain single output channels with their typical constraints imposed by the motor system’s information transfer limits. Will brain-computer interfaces (BCIs and gaze-based control be able to convey human commands or even intentions to machines in the near future? We provide an overview of basic approaches in this new area of applied cognitive research. Objective. We test the hypothesis that the use of communication paradigms and a combination of eye tracking with unobtrusive forms of registering brain activity can improve human-machine interaction. Methods and Results. Three groups of ongoing experiments at the Kurchatov Institute are reported. First, we discuss the communicative nature of human-robot interaction, and approaches to building a more e cient technology. Specifically, “communicative” patterns of interaction can be based on joint attention paradigms from developmental psychology, including a mutual “eye-to-eye” exchange of looks between human and robot. Further, we provide an example of “eye mouse” superiority over the computer mouse, here in emulating the task of selecting a moving robot from a swarm. Finally, we demonstrate a passive, noninvasive BCI that uses EEG correlates of expectation. This may become an important lter to separate intentional gaze dwells from non-intentional ones. Conclusion. The current noninvasive BCIs are not well suited for human-robot interaction, and their performance, when they are employed by healthy users, is critically dependent on the impact of the gaze on selection of spatial locations. The new approaches discussed show a high potential for creating alternative output pathways for the human brain. When support from passive BCIs becomes mature, the hybrid technology of the eye-brain-computer (EBCI interface will have a chance to enable natural, fluent, and the

Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users.

Science.gov (United States)

Rutkowski, Tomasz M; Mori, Hiromu

2015-04-15

The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm. In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI. Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies. We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques. The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research. Copyright © 2014 Elsevier B.V. All rights reserved.

Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces.

Science.gov (United States)

Cao, Lei; Ju, Zhengyu; Li, Jie; Jian, Rongjun; Jiang, Changjun

2015-09-30

Steady-state visual evoked potential (SSVEP) has been widely applied to develop brain computer interface (BCI) systems. The essence of SSVEP recognition is to recognize the frequency component of target stimulus focused by a subject significantly present in EEG spectrum. In this paper, a novel statistical approach based on sequence detection (SD) is proposed for improving the performance of SSVEP recognition. This method uses canonical correlation analysis (CCA) coefficients to observe SSVEP signal sequence. And then, a threshold strategy is utilized for SSVEP recognition. The result showed the classification performance with the longer duration of time window achieved the higher accuracy for most subjects. And the average time costing per trial was lower than the predefined recognition time. It was implicated that our approach could improve the speed of BCI system in contrast to other methods. Comparison with existing method(s): In comparison with other resultful algorithms, experimental accuracy of SD approach was better than those using a widely used CCA-based method and two newly proposed algorithms, least absolute shrinkage and selection operator (LASSO) recognition model as well as multivariate synchronization index (MSI) method. Furthermore, the information transfer rate (ITR) obtained by SD approach was higher than those using other three methods for most participants. These conclusions demonstrated that our proposed method was promising for a high-speed online BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

Myndplay: Measuring Attention Regulation with Single Dry Electrode Brain Computer Interface

NARCIS (Netherlands)

van der Wal, C.N.; Irrmischer, M.; Guo, Y.; Friston, K.; Faisal, A.; Hill, S.; Peng, H.

2015-01-01

Future applications for the detection of attention can be helped by the development and validation of single electrode brain computer interfaces that are small and user-friendly. The two objectives of this study were: to (1) understand the correlates of attention regulation as detected with the

Practical speech user interface design

CERN Document Server

Lewis, James R

2010-01-01

Although speech is the most natural form of communication between humans, most people find using speech to communicate with machines anything but natural. Drawing from psychology, human-computer interaction, linguistics, and communication theory, Practical Speech User Interface Design provides a comprehensive yet concise survey of practical speech user interface (SUI) design. It offers practice-based and research-based guidance on how to design effective, efficient, and pleasant speech applications that people can really use. Focusing on the design of speech user interfaces for IVR application

Brain-computer interfacing under distraction: an evaluation study

Science.gov (United States)

Brandl, Stephanie; Frølich, Laura; Höhne, Johannes; Müller, Klaus-Robert; Samek, Wojciech

2016-10-01

Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this ‘simulated’ out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.

뇌-컴퓨터 쿸터페쿴스 (Brain-Computer Interfaces) 기술엿 대한 국내·외 연구개발 뿙향 조사 (Research and Development in Brain-Computer Interfacing Technology: A Comprehensive Technical Review). Final Report.

NARCIS (Netherlands)

Nam, Chang Soo; Kim, Sung-Phil; Krusienkki, Dean; Nijholt, Antinus

2015-01-01

This report commisioned by the Korean American Scientists and Engineers Association (KSEA) and written with the support of the Korea Federation of Science and Technology Societies (KOFST) surveys research and development trends in the area of brain-computer interface (Brain-Computer Interfaces, BCI)

Overview Electrotactile Feedback for Enhancing Human Computer Interface

Science.gov (United States)

Pamungkas, Daniel S.; Caesarendra, Wahyu

2018-04-01

To achieve effective interaction between a human and a computing device or machine, adequate feedback from the computing device or machine is required. Recently, haptic feedback is increasingly being utilised to improve the interactivity of the Human Computer Interface (HCI). Most existing haptic feedback enhancements aim at producing forces or vibrations to enrich the user’s interactive experience. However, these force and/or vibration actuated haptic feedback systems can be bulky and uncomfortable to wear and only capable of delivering a limited amount of information to the user which can limit both their effectiveness and the applications they can be applied to. To address this deficiency, electrotactile feedback is used. This involves delivering haptic sensations to the user by electrically stimulating nerves in the skin via electrodes placed on the surface of the skin. This paper presents a review and explores the capability of electrotactile feedback for HCI applications. In addition, a description of the sensory receptors within the skin for sensing tactile stimulus and electric currents alsoseveral factors which influenced electric signal to transmit to the brain via human skinare explained.

An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

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Jinyi Long

2017-01-01

Full Text Available The hybrid brain computer interface (BCI based on motor imagery (MI and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

A Computer- Based Digital Signal Processing for Nuclear Scintillator Detectors

International Nuclear Information System (INIS)

Ashour, M.A.; Abo Shosha, A.M.

2000-01-01

In this paper, a Digital Signal Processing (DSP) Computer-based system for the nuclear scintillation signals with exponential decay is presented. The main objective of this work is to identify the characteristics of the acquired signals smoothly, this can be done by transferring the signal environment from random signal domain to deterministic domain using digital manipulation techniques. The proposed system consists of two major parts. The first part is the high performance data acquisition system (DAQ) that depends on a multi-channel Logic Scope. Which is interfaced with the host computer through the General Purpose Interface Board (GPIB) Ver. IEEE 488.2. Also, a Graphical User Interface (GUI) has been designed for this purpose using the graphical programming facilities. The second of the system is the DSP software Algorithm which analyses, demonstrates, monitoring these data to obtain the main characteristics of the acquired signals; the amplitude, the pulse count, the pulse width, decay factor, and the arrival time

Overlapped flowers yield detection using computer-based interface

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Anuradha Sharma

2016-09-01

Full Text Available Precision agriculture has always dealt with the accuracy and timely information about agricultural products. With the help of computer hardware and software technology designing a decision support system that could generate flower yield information and serve as base for management and planning of flower marketing is made so easy. Despite such technologies, some problem still arise, for example, a colour homogeneity of a specimen which cannot be obtained similar to actual colour of image and overlapping of image. In this paper implementing a new ‘counting algorithm’ for overlapped flower is being discussed. For implementing this algorithm, some techniques and operations such as colour image segmentation technique, image segmentation, using HSV colour space and morphological operations have been used. In this paper used two most popular colour space; those are RGB and HSV. HSV colour space decouples brightness from a chromatic component in the image, by which it provides better result in case for occlusion and overlapping.

HomPPI: a class of sequence homology based protein-protein interface prediction methods

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Dobbs Drena

2011-06-01

both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/. Conclusions Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

Science.gov (United States)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

Evaluation of a Compact Hybrid Brain-Computer Interface System

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Jaeyoung Shin

2017-01-01

Full Text Available We realized a compact hybrid brain-computer interface (BCI system by integrating a portable near-infrared spectroscopy (NIRS device with an economical electroencephalography (EEG system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA and baseline (BL tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

Science.gov (United States)

Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

2014-01-01

Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

User interfaces for computational science: A domain specific language for OOMMF embedded in Python

Science.gov (United States)

Beg, Marijan; Pepper, Ryan A.; Fangohr, Hans

2017-05-01

Computer simulations are used widely across the engineering and science disciplines, including in the research and development of magnetic devices using computational micromagnetics. In this work, we identify and review different approaches to configuring simulation runs: (i) the re-compilation of source code, (ii) the use of configuration files, (iii) the graphical user interface, and (iv) embedding the simulation specification in an existing programming language to express the computational problem. We identify the advantages and disadvantages of different approaches and discuss their implications on effectiveness and reproducibility of computational studies and results. Following on from this, we design and describe a domain specific language for micromagnetics that is embedded in the Python language, and allows users to define the micromagnetic simulations they want to carry out in a flexible way. We have implemented this micromagnetic simulation description language together with a computational backend that executes the simulation task using the Object Oriented MicroMagnetic Framework (OOMMF). We illustrate the use of this Python interface for OOMMF by solving the micromagnetic standard problem 4. All the code is publicly available and is open source.

Particle self-assembly at ionic liquid-based interfaces.

Science.gov (United States)

Frost, Denzil S; Nofen, Elizabeth M; Dai, Lenore L

2014-04-01

This review presents an overview of the nature of ionic liquid (IL)-based interfaces and self-assembled particle morphologies of IL-in-water, oil- and water-in-IL, and novel IL-in-IL Pickering emulsions with emphasis on their unique phenomena, by means of experimental and computational studies. In IL-in-water Pickering emulsions, particles formed monolayers at ionic liquid-water interfaces and were close-packed on fully covered emulsion droplets or aggregated on partially covered droplets. Interestingly, other than equilibrating at the ionic liquid-water interfaces, microparticles with certain surface chemistries were extracted into the ionic liquid phase with a high efficiency. These experimental findings were supported by potential of mean force calculations, which showed large energy drops as hydrophobic particles crossed the interface into the IL phase. In the oil- and water-in-IL Pickering emulsions, microparticles with acidic surface chemistries formed monolayer bridges between the internal phase droplets rather than residing at the oil/water-ionic liquid interfaces, a significant deviation from traditional Pickering emulsion morphology. Molecular dynamics simulations revealed aspects of the mechanism behind this bridging phenomenon, including the role of the droplet phase, surface chemistry, and inter-particle film. Novel IL-in-IL Pickering emulsions exhibited an array of self-assembled morphologies including the previously observed particle absorption and bridging phenomena. The appearance of these morphologies depended on the particle surface chemistry as well as the ILs used. The incorporation of particle self-assembly with ionic liquid science allows for new applications at the intersection of these two fields, and have the potential to be numerous due to the tunability of the ionic liquids and particles incorporated, as well as the particle morphology by combining certain groups of particle surface chemistry, IL type (protic or aprotic), and whether oil

Ethical aspects of brain computer interfaces: a scoping review

OpenAIRE

Burwell, Sasha; Sample, Matthew; Racine, Eric

2017-01-01

Background Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g...

Shaping of neuronal activity through a Brain Computer Interface

OpenAIRE

Valero-Aguayo, Luis; Silva-Sauer, Leandro; Velasco-Alvarez, Ricardo; Ron-Angevin, Ricardo

2014-01-01

Neuronal responses are human actions which can be measured by an EEG, and which imply changes in waves when neurons are synchronized. This activity could be changed by principles of behaviour analysis. This research tests the efficacy of the behaviour shaping procedure to progressively change neuronal activity, so that those brain responses are adapted according to the differential reinforcement of visual feedback. The Brain Computer Interface (BCI) enables us to record the EEG in real ti...

Intelligent Context-Aware and Adaptive Interface for Mobile LBS

Directory of Open Access Journals (Sweden)

Jiangfan Feng

2015-01-01

Full Text Available Context-aware user interface plays an important role in many human-computer Interaction tasks of location based services. Although spatial models for context-aware systems have been studied extensively, how to locate specific spatial information for users is still not well resolved, which is important in the mobile environment where location based services users are impeded by device limitations. Better context-aware human-computer interaction models of mobile location based services are needed not just to predict performance outcomes, such as whether people will be able to find the information needed to complete a human-computer interaction task, but to understand human processes that interact in spatial query, which will in turn inform the detailed design of better user interfaces in mobile location based services. In this study, a context-aware adaptive model for mobile location based services interface is proposed, which contains three major sections: purpose, adjustment, and adaptation. Based on this model we try to describe the process of user operation and interface adaptation clearly through the dynamic interaction between users and the interface. Then we show how the model applies users’ demands in a complicated environment and suggested the feasibility by the experimental results.

Computer simulation study of the nematic-vapour interface in the Gay-Berne model

Science.gov (United States)

Rull, Luis F.; Romero-Enrique, José Manuel

2017-06-01

We present computer simulations of the vapour-nematic interface of the Gay-Berne model. We considered situations which correspond to either prolate or oblate molecules. We determine the anchoring of the nematic phase and correlate it with the intermolecular potential parameters. On the other hand, we evaluate the surface tension associated to this interface. We find a corresponding states law for the surface tension dependence on the temperature, valid for both prolate and oblate molecules.

Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients

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Jessica Cantillo-Negrete

2018-01-01

Full Text Available Motor imagery-based brain-computer interfaces (BCI have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients’ average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects’ average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System’s performance showed that it has a potential to be used for hand rehabilitation of stroke patients.

Virtual Character Animation Based on Affordable Motion Capture and Reconfigurable Tangible Interfaces.

Science.gov (United States)

Lamberti, Fabrizio; Paravati, Gianluca; Gatteschi, Valentina; Cannavo, Alberto; Montuschi, Paolo

2018-05-01

Software for computer animation is generally characterized by a steep learning curve, due to the entanglement of both sophisticated techniques and interaction methods required to control 3D geometries. This paper proposes a tool designed to support computer animation production processes by leveraging the affordances offered by articulated tangible user interfaces and motion capture retargeting solutions. To this aim, orientations of an instrumented prop are recorded together with animator's motion in the 3D space and used to quickly pose characters in the virtual environment. High-level functionalities of the animation software are made accessible via a speech interface, thus letting the user control the animation pipeline via voice commands while focusing on his or her hands and body motion. The proposed solution exploits both off-the-shelf hardware components (like the Lego Mindstorms EV3 bricks and the Microsoft Kinect, used for building the tangible device and tracking animator's skeleton) and free open-source software (like the Blender animation tool), thus representing an interesting solution also for beginners approaching the world of digital animation for the first time. Experimental results in different usage scenarios show the benefits offered by the designed interaction strategy with respect to a mouse & keyboard-based interface both for expert and non-expert users.

Neuroengineering tools/applications for bidirectional interfaces, brain computer interfaces, and neuroprosthetic implants - a review of recent progress

Directory of Open Access Journals (Sweden)

Ryan M Rothschild

2010-10-01

Full Text Available The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain-computer interfaces (BCIs, bidirectional interfaces and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography (EEG and near-infrared spectroscopy (NIRS. Then the problem of gliosis and solutions for (semi- permanent implant biocompatibility such as innovative implant coatings, materials and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators (IPGs and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation (TMS are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques.

Electroencephalogram-Based Brain–Computer Interface and Lower-Limb Prosthesis Control: A Case Study

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Douglas P. Murphy

2017-12-01

Full Text Available ObjectiveThe purpose of this study was to establish the feasibility of manipulating a prosthetic knee directly by using a brain–computer interface (BCI system in a transfemoral amputee. Although the other forms of control could be more reliable and quick (e.g., electromyography control, the electroencephalography (EEG-based BCI may provide amputees an alternative way to control a prosthesis directly from brain.MethodsA transfemoral amputee subject was trained to activate a knee-unlocking switch through motor imagery of the movement of his lower extremity. Surface scalp electrodes transmitted brain wave data to a software program that was keyed to activate the switch when the event-related desynchronization in EEG reached a certain threshold. After achieving more than 90% reliability for switch activation by EEG rhythm-feedback training, the subject then progressed to activating the knee-unlocking switch on a prosthesis that turned on a motor and unlocked a prosthetic knee. The project took place in the prosthetic department of a Veterans Administration medical center. The subject walked back and forth in the parallel bars and unlocked the knee for swing phase and for sitting down. The success of knee unlocking through this system was measured. Additionally, the subject filled out a questionnaire on his experiences.ResultsThe success of unlocking the prosthetic knee mechanism ranged from 50 to 100% in eight test segments.ConclusionThe performance of the subject supports the feasibility for BCI control of a lower extremity prosthesis using surface scalp EEG electrodes. Investigating direct brain control in different types of patients is important to promote real-world BCI applications.

(Covert attention and visual speller design in an ERP-based brain-computer interface

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Treder Matthias S

2010-05-01

Full Text Available Abstract Background In a visual oddball paradigm, attention to an event usually modulates the event-related potential (ERP. An ERP-based brain-computer interface (BCI exploits this neural mechanism for communication. Hitherto, it was unclear to what extent the accuracy of such a BCI requires eye movements (overt attention or whether it is also feasible for targets in the visual periphery (covert attention. Also unclear was how the visual design of the BCI can be improved to meet peculiarities of peripheral vision such as low spatial acuity and crowding. Method Healthy participants (N = 13 performed a copy-spelling task wherein they had to count target intensifications. EEG and eye movements were recorded concurrently. First, (covert attention was investigated by way of a target fixation condition and a central fixation condition. In the latter, participants had to fixate a dot in the center of the screen and allocate their attention to a target in the visual periphery. Second, the effect of visual speller layout was investigated by comparing the symbol Matrix to an ERP-based Hex-o-Spell, a two-levels speller consisting of six discs arranged on an invisible hexagon. Results We assessed counting errors, ERP amplitudes, and offline classification performance. There is an advantage (i.e., less errors, larger ERP amplitude modulation, better classification of overt attention over covert attention, and there is also an advantage of the Hex-o-Spell over the Matrix. Using overt attention, P1, N1, P2, N2, and P3 components are enhanced by attention. Using covert attention, only N2 and P3 are enhanced for both spellers, and N1 and P2 are modulated when using the Hex-o-Spell but not when using the Matrix. Consequently, classifiers rely mainly on early evoked potentials in overt attention and on later cognitive components in covert attention. Conclusions Both overt and covert attention can be used to drive an ERP-based BCI, but performance is markedly lower

Vibrotactile Feedback for Brain-Computer Interface Operation

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Febo Cincotti

2007-01-01

Full Text Available To be correctly mastered, brain-computer interfaces (BCIs need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury, we compared vibrotactile and visual feedback, addressing: (I the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II the compatibility of this form of feedback in presence of a visual distracter; (III the performance in presence of a complex visual task on the same (visual or different (tactile sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users.

A simple interface to computational fluid dynamics programs for building environment simulations

Energy Technology Data Exchange (ETDEWEB)

Broderick, III, C R; Chen, Q [Massachusetts Institute of Technology, Cambridge, MA (United States)

2000-07-01

It is becoming a popular practice for architects and HVAC engineers to simulate airflow in and around buildings by computational fluid dynamics (CFD) methods in order to predict indoor and outdoor environment. However, many CFD programs are crippled by a historically poor and inefficient user interface system, particularly for users with little training in numerical simulation. This investigation endeavors to create a simplified CFD interface (SCI) that allows architects and buildings engineers to use CFD without excessive training. The SCI can be easily integrated into new CFD programs. (author)

Computer interfacing of the unified systems for personnel supervising in nuclear units

International Nuclear Information System (INIS)

Staicu, M.

1997-01-01

The dosimetric supervising of the personnel working in nuclear units is based on the information supplied by: 1) the dosimetric data obtained by the method of thermoluminescence; 2) the dosimetric data obtained by the method of photo dosimetry: 3) the records from medical periodic control. To create a unified system of supervising the following elements were combined: a) an Automatic System of TLD Reading and Data Processing (SACDTL). The data from this system are transmitted 'on line' to the computer; b) the measuring line of the optical density of exposed dosimetric films. The interface achieved within the general ensemble SACDTL could be adapted to this line of measurement. The transmission of the data from the measurement line to the computer is made 'on line'; c) the medical surveillance data for each person transmitted 'off line' to the database computer. The unified system resulting from the unification of the three supervising systems will achieve the following general functions: - registering of the personnel working in the nuclear field; - recording the dosimetric data; - processing and presentation of the data; - issuing of measurement bulletins. Thus, by means of unified database, dosimetric intercomparison and correlative studies can be undertaken. (author)

The brain-computer interface cycle.

Science.gov (United States)

van Gerven, Marcel; Farquhar, Jason; Schaefer, Rebecca; Vlek, Rutger; Geuze, Jeroen; Nijholt, Anton; Ramsey, Nick; Haselager, Pim; Vuurpijl, Louis; Gielen, Stan; Desain, Peter

2009-08-01

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

Science.gov (United States)

Shishkin, Sergei L.; Nuzhdin, Yuri O.; Svirin, Evgeny P.; Trofimov, Alexander G.; Fedorova, Anastasia A.; Kozyrskiy, Bogdan L.; Velichkovsky, Boris M.

2016-01-01

We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Sergei L. Shishkin

2016-11-01

Full Text Available We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs were collected during game’s control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant’s averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200..500 ms relative to the fixation onset. For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower results were obtained for the shrinkage LDA classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for

Hacking the brain: Brain-computer interfacing technology and the ethics of neurosecurity

NARCIS (Netherlands)

Ienca, M.; Haselager, W.F.G.

2016-01-01

Brain-computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are

Papers from the Fifth International Brain-Computer Interface Meeting

Science.gov (United States)

Huggins, Jane E.; Wolpaw, Jonathan R.

2014-06-01

Brain-computer interfaces (BCIs), also known as brain-machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use. The papers in this special section report some of the work presented at the Fifth International BCI Meeting held on 3-7 June 2013 at the Asilomar Conference Center in Pacific Grove, California, USA. Like its predecessors over the past 15 years, this meeting was supported by the National Institutes of Health, the National Science Foundation, and a variety of other governmental and private sponsors [1]. This fifth meeting was organized and managed by a program committee of BCI researchers from throughout the world [2]. It retained the distinctive retreat-style format developed by the Wadsworth Center researchers who organized and managed the first four meetings. The 301 attendees came from 165 research groups in 29 countries; 37% were students or postdoctoral fellows. Of more than 200 extended abstracts submitted for peer review, 25 were selected for oral presentation [3], and 181 were presented as posters [4] and published in the open-access conference proceedings [5]. The meeting featured 19 highly interactive workshops [6] covering the broad spectrum of BCI research and development, as well as many demonstrations of BCI systems and associated technology. Like the first four meetings, this one included attendees and embraced topics from across the broad spectrum of disciplines essential to effective BCI research and development, including

Ethical aspects of brain computer interfaces: a scoping review.

Science.gov (United States)

Burwell, Sasha; Sample, Matthew; Racine, Eric

2017-11-09

Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g., communication). The unprecedented direct connection created by BCI between human brains and computer hardware raises various ethical, social, and legal challenges that merit further examination and discussion. To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers.

Natural language interface for nuclear data bases

International Nuclear Information System (INIS)

Heger, A.S.; Koen, B.V.

1987-01-01

A natural language interface has been developed for access to information from a data base, simulating a nuclear plant reliability data system (NPRDS), one of the several existing data bases serving the nuclear industry. In the last decade, the importance of information has been demonstrated by the impressive diffusion of data base management systems. The present methods that are employed to access data bases fall into two main categories of menu-driven systems and use of data base manipulation languages. Both of these methods are currently used by NPRDS. These methods have proven to be tedious, however, and require extensive training by the user for effective utilization of the data base. Artificial intelligence techniques have been used in the development of several intelligent front ends for data bases in nonnuclear domains. Lunar is a natural language program for interface to a data base describing moon rock samples brought back by Apollo. Intellect is one of the first data base question-answering systems that was commercially available in the financial area. Ladder is an intelligent data base interface that was developed as a management aid to Navy decision makers. A natural language interface for nuclear data bases that can be used by nonprogrammers with little or no training provides a means for achieving this goal for this industry

Error-Free Text Typing Performance of an Inductive Intra-Oral Tongue Computer Interface for Severely Disabled Individuals.

Science.gov (United States)

Andreasen Struijk, Lotte N S; Bentsen, Bo; Gaihede, Michael; Lontis, Eugen R

2017-11-01

For severely paralyzed individuals, alternative computer interfaces are becoming increasingly essential for everyday life as social and vocational activities are facilitated by information technology and as the environment becomes more automatic and remotely controllable. Tongue computer interfaces have proven to be desirable by the users partly due to their high degree of aesthetic acceptability, but so far the mature systems have shown a relatively low error-free text typing efficiency. This paper evaluated the intra-oral inductive tongue computer interface (ITCI) in its intended use: Error-free text typing in a generally available text editing system, Word. Individuals with tetraplegia and able bodied individuals used the ITCI for typing using a MATLAB interface and for Word typing for 4 to 5 experimental days, and the results showed an average error-free text typing rate in Word of 11.6 correct characters/min across all participants and of 15.5 correct characters/min for participants familiar with tongue piercings. Improvements in typing rates between the sessions suggest that typing ratescan be improved further through long-term use of the ITCI.

Design of a microprocessor-based Control, Interface and Monitoring (CIM unit for turbine engine controls research

Science.gov (United States)

Delaat, J. C.; Soeder, J. F.

1983-01-01

High speed minicomputers were used in the past to implement advanced digital control algorithms for turbine engines. These minicomputers are typically large and expensive. It is desirable for a number of reasons to use microprocessor-based systems for future controls research. They are relatively compact, inexpensive, and are representative of the hardware that would be used for actual engine-mounted controls. The Control, Interface, and Monitoring Unit (CIM) contains a microprocessor-based controls computer, necessary interface hardware and a system to monitor while it is running an engine. It is presently being used to evaluate an advanced turbofan engine control algorithm.

A high speed, selective multi-ADC to computer data transfer interface, for nuclear physics experiments

International Nuclear Information System (INIS)

Arctaedius, T.; Ekstroem, R.E.

1986-08-01

A link connecting up to fifteen Analog to Digital Converters with a computer, through a Direct Memory Access interface, is described. The interface decides which of the connected ADC:s that participate in an event, and transfers the output-data from these to the computer, accompanied with a 2-byte word identifying the participating ADC:s. This data format can be recorded on tape without further transformations, and is easy to unfold at the off-line analysis. Data transfer is accomplished in less than a few microseconds, which is made possible by the use of high speed TTL circuits. (authors)

TMS communications software. Volume 2: Bus interface unit

Science.gov (United States)

Gregor, P. J.

1979-01-01

A data bus communication system to support the space shuttle's Trend Monitoring System (TMS) and to provide a basis for evaluation of the bus concept is described. Installation of the system included developing both hardware and software interfaces between the bus and the specific TMS computers and terminals. The software written for the microprocessor-based bus interface units is described. The software implements both the general bus communications protocol and also the specific interface protocols for the TMS computers and terminals.

A new workstation based man/machine interface system for the JT-60 Upgrade

International Nuclear Information System (INIS)

Yonekawa, I.; Shimono, M.; Totsuka, T.; Yamagishi, K.

1992-01-01

Development of a new man/machine interface system was stimulated by the requirements of making the JT-60 operator interface more 'friendly' on the basis of the past five-year operational experience. Eleven Sun/3 workstations and their supervisory mini-computer HIDIC V90/45 are connected through the standard network; Ethernet. The network is also connected to the existing 'ZENKEI' mini-computer system through the shared memory on the HIDIC V90/45 mini-computer. Improved software, such as automatic setting of the discharge conditions, consistency check among the related parameters and easy operation for discharge result data display, offered the 'user-friendly' environments. This new man/machine interface system leads to the efficient operation of the JT-60. (author)

Development of educational software for beam loading analysis using pen-based user interfaces

Directory of Open Access Journals (Sweden)

Yong S. Suh

2014-01-01

Full Text Available Most engineering software tools use typical menu-based user interfaces, and they may not be suitable for learning tools because the solution processes are hidden and students can only see the results. An educational tool for simple beam analyses is developed using a pen-based user interface with a computer so students can write and sketch by hand. The geometry of beam sections is sketched, and a shape matching technique is used to recognize the sketch. Various beam loads are added by sketching gestures or writing singularity functions. Students sketch the distributions of the loadings by sketching the graphs, and they are automatically checked and the system provides aids in grading the graphs. Students receive interactive graphical feedback for better learning experiences while they are working on solving the problems.

A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

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Francesco Cavrini

2016-01-01

Full Text Available We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.

HuRECA: Human Reliability Evaluator for Computer-based Control Room Actions

International Nuclear Information System (INIS)

Kim, Jae Whan; Lee, Seung Jun; Jang, Seung Cheol

2011-01-01

As computer-based design features such as computer-based procedures (CBP), soft controls (SCs), and integrated information systems are being adopted in main control rooms (MCR) of nuclear power plants, a human reliability analysis (HRA) method capable of dealing with the effects of these design features on human reliability is needed. From the observations of human factors engineering verification and validation experiments, we have drawn some major important characteristics on operator behaviors and design-related influencing factors (DIFs) from the perspective of human reliability. Firstly, there are new DIFs that should be considered in developing an HRA method for computer-based control rooms including especially CBP and SCs. In the case of the computer-based procedure rather than the paper-based procedure, the structural and managerial elements should be considered as important PSFs in addition to the procedural contents. In the case of the soft controllers, the so-called interface management tasks (or secondary tasks) should be reflected in the assessment of human error probability. Secondly, computer-based control rooms can provide more effective error recovery features than conventional control rooms. Major error recovery features for computer-based control rooms include the automatic logic checking function of the computer-based procedure and the information sharing feature of the general computer-based designs

Computer interfacing

CERN Document Server

Dixey, Graham

1994-01-01

This book explains how computers interact with the world around them and therefore how to make them a useful tool. Topics covered include descriptions of all the components that make up a computer, principles of data exchange, interaction with peripherals, serial communication, input devices, recording methods, computer-controlled motors, and printers.In an informative and straightforward manner, Graham Dixey describes how to turn what might seem an incomprehensible 'black box' PC into a powerful and enjoyable tool that can help you in all areas of your work and leisure. With plenty of handy

sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Tatsiana Malechka

2015-02-01

Full Text Available Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI. In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP-BCI, an Event-related (de-synchronization (ERD/ERS-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

Effect of Sb Segregation on Conductance and Catalytic Activity at Pt/Sb-Doped SnO2 Interface: A Synergetic Computational and Experimental Study

DEFF Research Database (Denmark)

Hu, Qiang; Colmenares Rausseo, Luis César; Martinez, Umberto

2015-01-01

a combined computational and experimental study. It was found that Sb-dopant atoms prefer to segregate toward the ATO/Pt interface. The deposited Pt catalysts, interestingly, not only promote Sb segregation, but also suppress the occurrence of Sb3+ species, a charge carrier neutralizer at the interface...... to support future applications of ATO/Pt-based materials as possible cathodes for PEMFC applications with enhanced durability under practical applications....

Correction: Cecotti, H. and Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials. Brain Sci. 2014, 4, 335–355

Directory of Open Access Journals (Sweden)

Hubert Cecotti

2014-09-01

Full Text Available The authors wish to make the following correction to this paper (Cecotti, H.; Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials. Brain Sci. 2014, 4, 335–355: Due to an internal error, the reference numbers in the original published paper were not shown, and the error was not due to the authors. The former main text should be replaced as below.

User interfaces for computational science: A domain specific language for OOMMF embedded in Python

Directory of Open Access Journals (Sweden)

Marijan Beg

2017-05-01

Full Text Available Computer simulations are used widely across the engineering and science disciplines, including in the research and development of magnetic devices using computational micromagnetics. In this work, we identify and review different approaches to configuring simulation runs: (i the re-compilation of source code, (ii the use of configuration files, (iii the graphical user interface, and (iv embedding the simulation specification in an existing programming language to express the computational problem. We identify the advantages and disadvantages of different approaches and discuss their implications on effectiveness and reproducibility of computational studies and results. Following on from this, we design and describe a domain specific language for micromagnetics that is embedded in the Python language, and allows users to define the micromagnetic simulations they want to carry out in a flexible way. We have implemented this micromagnetic simulation description language together with a computational backend that executes the simulation task using the Object Oriented MicroMagnetic Framework (OOMMF. We illustrate the use of this Python interface for OOMMF by solving the micromagnetic standard problem 4. All the code is publicly available and is open source.

A CAMAC-based data acquisition system with a Macintosh interface

International Nuclear Information System (INIS)

McKisson, J.E.; Ely, D.W.; Weisenberger, A.G.; Piercy, R.B.; Haskins, P.S.

1990-01-01

This paper describes a commercially available Macintosh-based data acquisition system and its application to a specific measurement. Based on Computer Aided Measurement and Control (CAMAC) and Nuclear Instrumentation Module (NIM) standard modules, the data acquisition system features a hardware and software interface to a Macintosh computer. This system has been used both for laboratory and remote site measurements, and has been found to perform well as both a highly interactive laboratory system and as a very automatable system for long term data acquisition. Ease in configuration allows for flexibility in fast response applications where a data acquisition system is needed in short time. The system software also supports much of the data analysis and presentation of results with a versatile set of histogram display and manipulation tools. In a recent application, the system controlled data acquisition for two germanium detectors used as part of the whole- spacecraft induced activation measurements of the Long Duration Exposure Facility (LDEF) satellite

Light on! Real world evaluation of a P300-based brain-computer interface (BCI) for environment control in a smart home.

Science.gov (United States)

Carabalona, Roberta; Grossi, Ferdinando; Tessadri, Adam; Castiglioni, Paolo; Caracciolo, Antonio; de Munari, Ilaria

2012-01-01

Brain-computer interface (BCI) systems aim to enable interaction with other people and the environment without muscular activation by the exploitation of changes in brain signals due to the execution of cognitive tasks. In this context, the visual P300 potential appears suited to control smart homes through BCI spellers. The aim of this work is to evaluate whether the widely used character-speller is more sustainable than an icon-based one, designed to operate smart home environment or to communicate moods and needs. Nine subjects with neurodegenerative diseases and no BCI experience used both speller types in a real smart home environment. User experience during BCI tasks was evaluated recording concurrent physiological signals. Usability was assessed for each speller type immediately after use. Classification accuracy was lower for the icon-speller, which was also more attention demanding. However, in subjective evaluations, the effect of a real feedback partially counterbalanced the difficulty in BCI use. Since inclusive BCIs require to consider interface sustainability, we evaluated different ergonomic aspects of the interaction of disabled users with a character-speller (goal: word spelling) and an icon-speller (goal: operating a real smart home). We found the first one as more sustainable in terms of accuracy and cognitive effort.

Evaluating a multi-player brain-computer interface game: challenge versus co-experience

NARCIS (Netherlands)

Gürkök, Hayrettin; Volpe, G; Reidsma, Dennis; Poel, Mannes; Camurri, A.; Obbink, Michel; Nijholt, Antinus

2013-01-01

Brain–computer interfaces (BCIs) have started to be considered as game controllers. The low level of control they provide prevents them from providing perfect control but allows the design of challenging games which can be enjoyed by players. Evaluation of enjoyment, or user experience (UX), is

Citation analysis of Computer Standards & Interfaces: Technical or also non-technical focus?

NARCIS (Netherlands)

G. van de Kaa (Geerten); H.J. de Vries (Henk); B. Baskaran (Balakumaran)

2015-01-01

textabstractThis paper analyzes to which extent research published in Computer Standards & Interfaces (CSI) has a technical focus. We find that CSI has been following its scope very closely in the last three years and that the majority of its publications have a technical focus. Articles published

Virtual microscopy: merging of computer mediated communication and intuitive interfacing

Science.gov (United States)

de Ridder, Huib; de Ridder-Sluiter, Johanna G.; Kluin, Philip M.; Christiaans, Henri H. C. M.

2009-02-01

Ubiquitous computing (or Ambient Intelligence) is an upcoming technology that is usually associated with futuristic smart environments in which information is available anytime anywhere and with which humans can interact in a natural, multimodal way. However spectacular the corresponding scenarios may be, it is equally challenging to consider how this technology may enhance existing situations. This is illustrated by a case study from the Dutch medical field: central quality reviewing for pathology in child oncology. The main goal of the review is to assess the quality of the diagnosis based on patient material. The sharing of knowledge in social face-to-face interaction during such meeting is an important advantage. At the same time there is the disadvantage that the experts from the seven Dutch academic medical centers have to travel to the review meeting and that the required logistics to collect and bring patient material and data to the meeting is cumbersome and time-consuming. This paper focuses on how this time-consuming, nonefficient way of reviewing can be replaced by a virtual collaboration system by merging technology supporting Computer Mediated Collaboration and intuitive interfacing. This requires insight in the preferred way of communication and collaboration as well as knowledge about preferred interaction style with a virtual shared workspace.

Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system.

Science.gov (United States)

Li, Yuanqing; Pan, Jiahui; He, Yanbin; Wang, Fei; Laureys, Steven; Xie, Qiuyou; Yu, Ronghao

2015-12-15

For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Charles Yaacoub

2017-01-01

Full Text Available Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5% while improving the accuracy, sensitivity, specificity, and precision of the classifier.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

Science.gov (United States)

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

Science.gov (United States)

Hill, N. J.; Schölkopf, B.

2012-04-01

We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare ‘oddball’ stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

Science.gov (United States)

Hill, N J; Schölkopf, B

2012-01-01

We report on the development and online testing of an EEG-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects’ modulation of N1 and P3 ERP components measured during single 5-second stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare “oddball” stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly-known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention-modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject’s attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology. PMID:22333135

Data communications in a parallel active messaging interface of a parallel computer

Science.gov (United States)

Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E

2013-10-29

Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the parallel computer including a plurality of compute nodes that execute a parallel application, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a data communications instruction, the instruction characterized by an instruction type, the instruction specifying a transmission of transfer data from the origin endpoint to a target endpoint and transmitting, in accordance with the instruction type, the transfer data from the origin endpoint to the target endpoint.

An interface energy density-based theory considering the coherent interface effect in nanomaterials

Science.gov (United States)

Yao, Yin; Chen, Shaohua; Fang, Daining

2017-02-01

To characterize the coherent interface effect conveniently and feasibly in nanomaterials, a continuum theory is proposed that is based on the concept of the interface free energy density, which is a dominant factor affecting the mechanical properties of the coherent interface in materials of all scales. The effect of the residual strain caused by self-relaxation and the lattice misfit of nanomaterials, as well as that due to the interface deformation induced by an external load on the interface free energy density is considered. In contrast to the existing theories, the stress discontinuity at the interface is characterized by the interface free energy density through an interface-induced traction. As a result, the interface elastic constant introduced in previous theories, which is not easy to determine precisely, is avoided in the present theory. Only the surface energy density of the bulk materials forming the interface, the relaxation parameter induced by surface relaxation, and the mismatch parameter for forming a coherent interface between the two surfaces are involved. All the related parameters are far easier to determine than the interface elastic constants. The effective bulk and shear moduli of a nanoparticle-reinforced nanocomposite are predicted using the proposed theory. Closed-form solutions are achieved, demonstrating the feasibility and convenience of the proposed model for predicting the interface effect in nanomaterials.

A high-speed interface for multi-channel analyzer

International Nuclear Information System (INIS)

Shen Ji; Zheng Zhong; Qiao Chong; Chen Ziyu; Ye Yunxiu; Ye Zhenyu

2003-01-01

This paper presents a high-speed computer interface for multi-channel analyzer based on DMA technique. Its essential principle and operating procedure are introduced. By the detecting of γ spectrum of 137 Cs with the interface, it's proved that the interface can meet the requirements of high-speed data acquisition

Training leads to increased auditory brain-computer interface performance of end-users with motor impairments.

Science.gov (United States)

Halder, S; Käthner, I; Kübler, A

2016-02-01

Auditory brain-computer interfaces are an assistive technology that can restore communication for motor impaired end-users. Such non-visual brain-computer interface paradigms are of particular importance for end-users that may lose or have lost gaze control. We attempted to show that motor impaired end-users can learn to control an auditory speller on the basis of event-related potentials. Five end-users with motor impairments, two of whom with additional visual impairments, participated in five sessions. We applied a newly developed auditory brain-computer interface paradigm with natural sounds and directional cues. Three of five end-users learned to select symbols using this method. Averaged over all five end-users the information transfer rate increased by more than 1800% from the first session (0.17 bits/min) to the last session (3.08 bits/min). The two best end-users achieved information transfer rates of 5.78 bits/min and accuracies of 92%. Our results show that an auditory BCI with a combination of natural sounds and directional cues, can be controlled by end-users with motor impairment. Training improves the performance of end-users to the level of healthy controls. To our knowledge, this is the first time end-users with motor impairments controlled an auditory brain-computer interface speller with such high accuracy and information transfer rates. Further, our results demonstrate that operating a BCI with event-related potentials benefits from training and specifically end-users may require more than one session to develop their full potential. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Molecular Computational Investigation of Electron Transfer Kinetics across Cytochrome-Iron Oxide Interfaces

International Nuclear Information System (INIS)

Kerisit, Sebastien N.; Rosso, Kevin M.; Dupuis, Michel; Valiev, Marat

2007-01-01

The interface between electron transfer proteins such as cytochromes and solid phase mineral oxides is central to the activity of dissimilatory-metal reducing bacteria. A combination of potential-based molecular dynamics simulations and ab initio electronic structure calculations are used in the framework of Marcus' electron transfer theory to compute elementary electron transfer rates from a well-defined cytochrome model, namely the small tetraheme cytochrome (STC) from Shewanella oneidensis, to surfaces of the iron oxide mineral hematite (a-Fe2O3). Room temperature molecular dynamics simulations show that an isolated STC molecule favors surface attachment via direct contact of hemes I and IV at the poles of the elongated axis, with electron transfer distances as small as 9 Angstroms. The cytochrome remains attached to the mineral surface in the presence of water and shows limited surface diffusion at the interface. Ab initio electronic coupling matrix element (VAB) calculations of configurations excised from the molecular dynamics simulations reveal VAB values ranging from 1 to 20 cm-1, consistent with nonadiabaticity. Using these results, together with experimental data on the redox potential of hematite and hemes in relevant cytochromes and calculations of the reorganization energy from cluster models, we estimate the rate of electron transfer across this model interface to range from 1 to 1000 s-1 for the most exothermic driving force considered in this work, and from 0.01 to 20 s-1 for the most endothermic. This fairly large range of electron transfer rates highlights the sensitivity of the rate upon the electronic coupling matrix element, which is in turn dependent on the fluctuations of the heme configuration at the interface. We characterize this dependence using an idealized bis-imidazole heme to compute from first principles the VAB variation due to porphyrin ring orientation, electron transfer distance, and mineral surface termination. The electronic

Bigger data for big data: from Twitter to brain-computer interfaces.

Science.gov (United States)

Roesch, Etienne B; Stahl, Frederic; Gaber, Mohamed Medhat

2014-02-01

We are sympathetic with Bentley et al.'s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain-computer interfaces.

Real time system design of motor imagery brain-computer interface based on multi band CSP and SVM

Science.gov (United States)

Zhao, Li; Li, Xiaoqin; Bian, Yan

2018-04-01

Motion imagery (MT) is an effective method to promote the recovery of limbs in patients after stroke. Though an online MT brain computer interface (BCT) system, which apply MT, can enhance the patient's participation and accelerate their recovery process. The traditional method deals with the electroencephalogram (EEG) induced by MT by common spatial pattern (CSP), which is used to extract information from a frequency band. Tn order to further improve the classification accuracy of the system, information of two characteristic frequency bands is extracted. The effectiveness of the proposed feature extraction method is verified by off-line analysis of competition data and the analysis of online system.

Space as interface

DEFF Research Database (Denmark)

Lykke-Olesen, Andreas

2006-01-01

multiple projects spanning over fields such as tangible user interfaces, augmented reality, and mobile computing, a conceptual framework characterizing camera-based mixed interaction spaces is developed. To show the applicability of the framework, it is deployed on one of the presented cases and discussed...

Multimodal 2D Brain Computer Interface.

Science.gov (United States)

Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

2015-08-01

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

A python based interface for the tandem-linac control system

International Nuclear Information System (INIS)

Ajith Kumar, B.P.

2011-01-01

The control system for the Tandem-LINAC accelerator system at IUAC is a client-server design running on a network of PCs under the GNU/Linux operating system. The computers connected to the devices in the accelerator run a server program. The computers providing the user interface runs different kinds of client programs that communicates to the servers over a TCT/IP network to control/monitor the accelerator parameters. Both the programs were written in C language and some programming expertise was required to write the client programs. The addition of a Python language interface has enabled the users to write programs for specific tasks like data logging and partial automation of the operation with minimal effort. (author)

What Do IT-People Know About the (Nordic) History of Computers and User Interfaces?

DEFF Research Database (Denmark)

Jørgensen, Anker Helms

2009-01-01

:  This paper reports a preliminary, empirical exploration of what IT-people know about the history of computers and user interfaces.  The principal motivation for the study is that the younger generations such as students in IT seem to know very little about these topics.  The study employed...... to become the designation or even the icon for the computer.  In other words, one of the key focal points in the area of human-computer interaction: to make the computer as such invisible seems to have been successful...

A comparison of two spelling Brain-Computer Interfaces based on visual P3 and SSVEP in Locked-In Syndrome.

Directory of Open Access Journals (Sweden)

Adrien Combaz

Full Text Available We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP-based Brain-Computer Interfaces (BCIs for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS.Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems. We also investigated the effect of the quality of life and level of motor impairment on the performance.All seven patients were able to achieve an accuracy of 70% or more with the SSVEP-based BCI, compared to 3 patients with the P3-based BCI, showing a better performance with the SSVEP BCI than with the P3 BCI in the studied cohort. Moreover, the better performance of the SSVEP-based BCI was accompanied by a lower mental workload and a higher overall satisfaction. No relationship was found between BCI performance and level of motor impairment or quality of life.Our results show a better usability of the SSVEP-based BCI than the P3-based one for the sessions performed by the tested population of locked-in patients with respect to all the criteria considered. The study shows the advantage of developing alternative BCIs with respect to the traditional matrix-based P3 speller using different designs and signal modalities such as SSVEPs to build a faster, more accurate, less mentally demanding and more satisfying BCI by testing both types of BCIs on a convenience sample of LIS patients.

Automated selection of brain regions for real-time fMRI brain-computer interfaces

Science.gov (United States)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.