Brain-computer Interface Design Research Articles

SSVEP Based BCI for an Object Control in 2D Space

The Brain-Computer Interfaces can successfully help the healthy and disabled users, in performing various activities in their day to day life. But still such high expectations are yet to be fulfilled by the existing BCI designs due to their restricted reliability and less understanding of the brain mechanisms used in it. Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information transfer rate and require lesser training time than BCI systems using other paradigms. This work aimed to directly address the above problems by optimizing the BCI designs based on Steady-State Visual Evoked Potential (SSVEP) brain responses. The main goal of this work was to optimize the frequency to be used by the users and further enhance the information transfer rates and the reliability of multi-command SSVEP-based BCI systems. The BCI system is designed to allow control of a virtual ball in 2D space with more than 97% of average accuracy with different users.

High Performance EEG Signal Classification using Classifiability and the Twin SVM

Event Abstract Back to Event High Performance EEG Signal Classification using Classifiability and the Twin SVM Sumit Soman1* and Jayadeva .1 1 Indian Institute of Technology, Delhi, India, Department of Electrical Engineering, India Classification of Electroencephalogram (EEG) data for imagined motor movements has been a challenge in the design and development of Brain Computer Interfaces (BCIs). There are two principle challenges. The first is the variability in the recorded EEG data, which manifests across trials as well as across individuals. Consequently, features that are more discriminative need to be identified before any pattern recognition technique can be applied. The second challenge is in the pattern recognition domain. The number of data samples in a class of interest, e.g. a specific action, is a small fraction of the total data, which is composed of samples corresponding to all actions of all users. Building a robust classifier when learning from a highly unbalanced dataset is very difficult; minimizing the classification error typically causes the larger class to overwhelm the smaller one. We show that the combination of ‘classifiability’ for selecting the optimal frequency band and the use of the Twin Support Vector Machine (Twin SVM) for classification, yields significantly improved generalization. On benchmark BCI Competition datasets, the proposed approach often yields up to 20% improvement over the state-of-the-art. Figure 1 References Soman, Sumit and Jayadeva. "High performance EEG signal classification using classifiability and the Twin SVM." Applied Soft Computing Vol. 30 (May 2015): pp. 305-318 [http://www.sciencedirect.com/science/article/pii/S1568494615000204] Keywords: Brain Computer Interface, Classifiability, Support Vector Machines, Motor Imagery (MI), multiclass classification, Unbalanced data Conference: German-Japanese Adaptive BCI Workshop, Kyoto, Japan, 28 Oct - 29 Oct, 2015. Presentation Type: Poster presentation Topic: Adaptive BCI Citation: Soman S and . J (2015). High Performance EEG Signal Classification using Classifiability and the Twin SVM. Front. Comput. Neurosci. Conference Abstract: German-Japanese Adaptive BCI Workshop. doi: 10.3389/conf.fncom.2015.56.00020 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Sep 2015; Published Online: 04 Nov 2015. * Correspondence: Mr. Sumit Soman, Indian Institute of Technology, Delhi, India, Department of Electrical Engineering, Delhi, India, sumit.soman@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sumit Soman Jayadeva . Google Sumit Soman Jayadeva . Google Scholar Sumit Soman Jayadeva . PubMed Sumit Soman Jayadeva . Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

An Open-Ended Approach to BCI: Embracing Individual Differences by Allowing for User-Defined Mental Commands

An Open-Ended Approach to BCI: Embracing Individual Differences by Allowing for User-Defined Mental Commands

A signal-processing-based technique for P300 evoked potential detection with the applications into automated character recognition

In the past decade, many researchers have dedicated their efforts to exploring brain computer interface (BCI) technology. With a growing number of investigations into the BCI and its related areas, BCI systems nowadays are not only developed for the disabled but also for the normal and healthy people. In this paper, a signal-processing-based technique with its applications into the development of automated character recognition is introduced. The task of the pattern recognition to such a BCI design problem was mainly accomplished based on the detection of P300 evoked potentials. We approached this detection problem by employing a template-matching-based method to extract the morphological information from EEG signals first, and then applying a linear discriminant function (LDF) to the features selected for pattern classification. The entire detection process was further implemented on an existing BCI system platform, called the BCI2000 system. The algorithm performance was evaluated using an existing reliable database provided by BCI Competition 2003. Numerical experimental results produced by the database indicated that the proposed algorithm actually achieved 100% character recognition accuracy.

Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : application to fNIRS signals.

Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.

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

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.

Understanding and Proposing a Design Rationale of Digital Games based on Brain-Computer Interface: Results of the AdmiralMind Battleship Study

Brain-Computer Interfaces (BCIs) present great potential to the field of digital games, though the design of this kind of interface is still a challenge for most of the game designers and developers. In this work it is presented a literature review about digital games based on BCI, aiming to analyze the interaction design of these games, to identify the approaches applied, limitations and implications related to BCIs design. After the review, we accomplish a reflection about the design decisions involved in the development of digital games based on BCI. This approach leads to the construction of a design rationale developed to support the process of BCI-based games, with its use established on the design of a battleship game based on BCI. As contributions of this work, we highlight the literature review, design rationale and results of the study accomplished.

The Brainwave Concentration Analysis of Direction Recognition

This research proposed a formula for calculating the concentration based on the view of cognitive neuroscience and a design of Brain Computer Interface (BCI) to show the strength of concentration when performing the direction recognition. The experiences proceeded by catching brainwave signals of human vision while sensing the test interfaces of graphics representing direction by brain-wave sensor and analyzed the characteristic frequency band of the brainwave related to direction recognition, then formulate the attention index of direction recognition based on the cognitive neuroscience. This research is to analyze and discuss every kind of brainwave frequency band characteristics while subjects recognize the directions; moreover, the proposed brain computer interface not only could demonstrate the strength of the subject's attention, but also could provide subject to play the contest of brainwave game with virtual computer player, providing the feasibility of future brainwave application development

A Batch-mode Active Learning Method Based on the Nearest Average-class Distance (NACD) for Multiclass Brain-Computer Interfaces

In this paper, a novel batch-mode active learning method based on the nearest average-class distance (ALNACD) is proposed to solve multi-class problems with Linear Discriminate Analysis (LDA) classiflers. Using the Nearest Average-class Distance (NACD) query function, the ALNACD algorithm selects a batch of most uncertain samples from unlabeled data to improve gradually pre-trained classiflers’ performance. As our method only needs a small set of labeled samples to train initial classiflers, it is very useful in applications like Brain-computer Interface (BCI) design. To verify the efiectiveness of the proposed ALNACD method, we test the ALNACD algorithm on the Dataset 2a of BCI Competition IV. The test results show that the ALNACD algorithm ofiers similar classiflcation results using less sample labeling efiort than Random Sampling (RS) method. It also provides competitive results compared with active Support Vector Machine (active SVM), but uses less time than the active SVM in terms of the training.

A Design of BCI based Environment System for Immersion of FPS Game

We present a methodology about BCI (brain computer interface) based environment system for immersion of FPS game play. FPS (First- person shooter) game is a video game genre centered on gun and projectile weapon-based combat through a first-person perspective, immersion is important factor in this game. FPS game designer effort to raise players immersion when make game using interface, graphic effect, sound etc. We implemented real-time game environment changing system using the BCI to raise the degree of immersion in FPS game. EEG 1 is the recording of electrical activity along the scalp. We measure user’s EEG using MindSet 2 for implemented game playing time to get user’s concentration rate. This value operates our environment system in virtual space. In virtual space, each player experience different environment situation because each player has various concentration rate. Implemented environment system is formed with alternation of day with night and weather. Human body condition is changed depending on day and night. Player can feel change of character’s condition via player’s condition by our system. Implemented system can be applied to various game genres to raise player’s immersion for game playing time.

Toward a Semi-Self-Paced EEG Brain Computer Interface: Decoding Initiation State from Non-Initiation State in Dedicated Time Slots

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are “synchronous” systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in “asynchronous” BCIs subjects pace the interaction and the system must determine when the subject’s control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject’s intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.

Blind Source Separation Based of Brain Computer Interface System: A review

This study reviews the originality and development of the Brain Computer Interface (BCI) system and focus on the BCI system design based on Blind Source Separation (BSS) techniques. The study also provides the recent trends and discusses some of a new ideas for BSS techniques in BCI architecture, articles which discussing the BCI system development were analysed, types of the BCI systems and the recent BCI design were explored. Since 1970 when the research of BCI system began in the California Los Angeles University, the interest and the amount of research in BCI have been increased significantly; especially, when the BSS theory emerged in 1982 by a simple discussion between researchers. A lot of refereed journals and conference papers are reviewed and categorized to make this study in useful form. However, there are a few comprehensive reviews of BSS techniques in BCI literature. The review concludes with a brief discussion and expected future of the BCI.

An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender.

BackgroundOne of the difficulties for the implementation of Brain-Computer Interface (BCI) systems for motor impaired patients is the time consumed in the system design process, since patients do not have the adequate physical nor psychological conditions to complete the process. For this reason most of BCIs are designed in a subject-dependent approach using data of healthy subjects. The developing of subject-independent systems is an option to decrease the required training sessions to design a BCI with patient functionality. This paper presents a proof-of-concept study to evaluate subject-independent system based on hand motor imagery taking gender into account.MethodsSubject-Independent BCIs are proposed using Common Spatial Patterns and log variance features of two groups of healthy subjects; one of the groups was composed by people of male gender and the other one by people of female gender. The performance of the developed gender-specific BCI designs was evaluated with respect to a subject-independent BCI designed without taking gender into account, and afterwards its performance was evaluated with data of two healthy subjects that were not included in the initial sample. As an additional test to probe the potential use for subcortical stroke patients we applied the methodology to two patients with right hand weakness. T-test was employed to determine the significance of the difference between traditional approach and the proposed gender-specific approach.ResultsFor most of the tested conditions, the gender-specific BCIs have a statistically significant better performance than those that did not take gender into account. It was also observed that with a BCI designed with log-variance features in the alpha and beta band of healthy subjects’ data, it was possible to classify hand motor imagery of subcortical stroke patients above the practical level of chance.ConclusionsA larger subjects’ sample test may be necessary to improve the performances of the gender-specific BCIs and to further test this methodology on different patients. The reduction of complexity in the implementation of BCI systems could bring these systems closer to applications such as controlling devices for the motor rehabilitation of stroke patients, and therefore, contribute to a more effective neurological rehabilitation.

An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy

BackgroundBrain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke.Methods32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data.ResultsStroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset.ConclusionsThis exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

A novel steady-state visually evoked potential-based brain–computer interface design: Character Plotter

In this paper, an electroencephalogram-based innovative brain–computer interface (BCI), known as “Character Plotter”, is presented. The proposed design uses steady-state visually evoked potentials. The subjects generate characters by drawing, step by step, lines between six circles on the computer screen. Additionally, there are three circles for controlling the drawing procedure. The features obtained from canonical correlation analysis are used for classification. The proposed synchronous BCI design was tested on 16 subjects in offline and online experimental tasks using support vector machines, linear discriminant analysis and the nearest mean classifier. The obtained classification performances indicate that the proposed design can be successfully used in classification. The Character Plotter converges to the natural writing scheme of humans. Therefore, subjects can adapt to the BCI design in a short training session.

Design and Implementation of Wearable and Wireless Multi-Channel Brain-Computer Interface with the Novel Dry Sensors

Design and Implementation of Wearable and Wireless Multi-Channel Brain-Computer Interface with the Novel Dry Sensors

Asynchronous Brain–Computer Interface Based on Steady-State Visual-Evoked Potential

Asynchronous brain–computer interface (BCI) systems are more practicable than synchronous ones in real-world applications. A key challenge in asynchronous BCI design is to discriminate intentional control (IC) and non-intentional control (NC) states. In this paper, we present a two-stage asynchronous protocol for a steady-state visual-evoked potential-based BCI. First, we estimate a threshold using canonical correlation analysis coefficients in synchronous mode; then, we combine it with a sliding window strategy to continuously detect the mental state of the user. If the current state is judged as an IC state, then the system will output command. Our results show that the average positive predictive value of the system is 77.06 % and that its average false-positive rate in the NC state and IC state are 2.37 and 12.05 %, respectively.

Classification of neural signals from sparse autoregressive features

This paper introduces a signal classification framework that can be used for brain–computer interface design. The actual classification is performed on sparse autoregressive features. It can use any well-known classification algorithm, such as discriminant analysis, linear logistic regression and support vector machines. The autoregressive coefficients of all signals and channels are simultaneously estimated by the group lasso, and the estimation is guided by the classification performance. Thanks to the variable selection capability of the group lasso, the framework can drop individual autoregressive coefficients that are useless in the prediction stage. Also, the framework is relatively insensitive to the chosen autoregressive order. We devise an efficient algorithm to solve this problem. We test our approach on Keirn and Aunon's data, used for binary classification of electroencephalogram signals, achieving promising results.

Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury

Spinal cord injury (SCI) often affects a person's ability to perform critical activities of daily living and can negatively affect his or her quality of life. Assistive technology aims to bridge this gap in order to augment function and increase independence. It is critical to involve consumers in the design and evaluation process as new technologies such as brain-computer interfaces (BCIs) are developed. In a survey study of 57 veterans with SCI participating in the 2010 National Veterans Wheelchair Games, we found that restoration of bladder and bowel control, walking, and arm and hand function (tetraplegia only) were all high priorities for improving quality of life. Many of the participants had not used or heard of some currently available technologies designed to improve function or the ability to interact with their environment. The majority of participants in this study were interested in using a BCI, particularly for controlling functional electrical stimulation to restore lost function. Independent operation was considered to be the most important design criteria. Interestingly, many participants reported that they would consider surgery to implant a BCI even though noninvasiveness was a high-priority design requirement. This survey demonstrates the interest of individuals with SCI in receiving and contributing to the design of BCIs.

Selective attention in an overcrowded auditory scene: Implications for auditory-based brain-computer interface design

Listeners are good at attending to one auditory stream in a crowded environment. However, is there an upper limit of streams present in an auditory scene at which this selective attention breaks down? Here, participants were asked to attend one stream of spoken letters amidst other letter streams. In half of the trials, an initial primer was played, cueing subjects to the sound configuration. Results indicate that performance increases with token repetitions. Priming provided a performance benefit, suggesting that stream selection, not formation, is the bottleneck associated with attention in an overcrowded scene. Results' implications for brain-computer interfaces are discussed.