Design Of Brain Computer Interface System Research Articles

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.

Brain-Computer Integration: A Framework for the Design of Brain-Computer Interfaces from an Integrations Perspective

Brain-computer interface (BCI) systems hold the potential to foster human flourishing and self-actualization. However, we believe contemporary BCI system design approaches unnecessarily limit these potentialities as they are approached from a traditional interaction perspective, producing command-response experiences. This article proposes to go beyond “interaction” and toward a paradigm of human-computer integration. The potential of this paradigm is demonstrated through three prototypes: Inter-Dream, a system that integrates with the brain's autonomic physiological processes to drive users toward healthy sleep states; Neo-Noumena, a system that integrates with the user's affective neurophysiology to augment the interpersonal communication of emotion; and PsiNet, a system that integrates interpersonal brain activity to amplify human connection. Studies of these prototypes demonstrate the benefits of the integration paradigm in realizing the multifaceted benefits of BCI systems, and this work presents the brain-computer integration framework to help guide designers of future BCI integrations.

Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications.

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

The constantly developing biomedical engineering field and newer and more advanced BCI (brain–computer interface) systems require their designers to constantly develop and search for various innovative methods used in their creation. In response to practical requirements and the possibility of using the system in real conditions, the authors propose an advanced solution using EEG (electroencephalography) signal analysis. A BCI system design approach using artificial intelligence for the advanced analysis of signals containing facial expressions as control commands was used. The signals were burdened with numerous artifacts caused by simulated nervous tics. The proposed expert system consisted of two neural networks. The first one allowed for the analysis of one-second samples of EEG signals from selected electrodes on the basis of power spectrum estimation waveforms. Thus, it was possible to generate an appropriate control signal as a result of appropriate facial expression commands. The second of the neural networks detected the appearance and type of nervous tics in the signal. Additionally, the participants were affected by interference such as street and TV or radio sound, Wi-Fi and radio waves. The system designed in such a way is adapted to the requirements of the everyday life of people with disabilities, in particular those in wheelchairs, whose control is based on BCI technology.

Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology

Brain-computer interface (BCI) is a revolutionizing human-computer Interaction, which is developing towards the direction of intelligent brain-computer interaction and brain-computer intelligent integration. However, the practical application of BCI is facing great challenges. The maturity of BCI technology has not yet reached the needs of users. The traditional design method of BCI needs to be improved. It is necessary to pay attention to BCI human factors engineering, which plays an important role in narrowing the gap between research and practical application, but it has not attracted enough attention and has not been specifically discussed in depth. Aiming at BCI human factors engineering, this article expounds the design requirements (from users), design ideas, objectives and methods, as well as evaluation indexes of BCI with the human-centred-design. BCI human factors engineering is expected to make BCI system design under different use conditions more in line with human characteristics, abilities and needs, improve the user satisfaction of BCI system, enhance the user experience of BCI system, improve the intelligence of BCI, and make BCI move towards practical application.

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals.

A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

Sub‐band classification of decomposed single event‐related potential co‐variants for multi‐class brain–computer interface: a qualitative and quantitative approach

Subject dependent and co-variant nature of electroencephalography (EEG)/event-related potential (ERP) are still hurdles in the development of generalised EEG system such as clinical diagnoses and brain–computer interface (BCI) systems. Presently, classification of BCI classes is limited due to consideration of several trials of ERP as average case for generalisation or some time single-trial ERP detection is preferred for subject specific schemes. Present study tries to develop a methodology for utilisation of co-variant nature of ERP, based on the sub-band decomposed ERPs. Proposed idea also explores a new dimension in BCI system design, as multi-class classification for execution of multiple commands through single ERP. Sub-band ERPs and their power spectrum-based features are extracted and classified successfully with 70.64% accuracy using artificial neural network.

Feature selection for neutral vector in EEG signal classification

In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) representations can reveal features related to the transient nature of the EEG signals, the mDWT coefficients have been frequently used in EEG signal classification. In our previous work, we have proposed a super-Dirichlet distribution-based classifier. The proposed classifier performed better than the state-of-the-art support vector machine-based classifier. In this paper, we further study the neutrality of the mDWT coefficients. The mDWT coefficients have unit L1-norm and all the elements are nonnegative. Assuming the mDWT vector coefficients to be a neutral vector, we apply the proposed parallel nonlinear transformation (PNT) framework to transform them non-linearly into a set of independent scalar coefficients. Based on these scalar coefficients, feature selection strategy is proposed on the transformed feature domain. Experimental results show that the feature selection strategy helps improving the classification accuracy.

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.

BCI Appliance

Event Abstract Back to Event BCI Appliance Tomasz Spustek1*, Mateusz Kruszyński2, Dawid Laszuk1, Piotr Milanowski1, Maciej Łabęcki1, Karol Augustin1, Anna Duszyk3, Rafał Kuś1, Jarosław Żygierewicz1 and Piotr Durka1 1 University of Warsaw, Faculty of Physics, ul. Hoza 69, 00-681 Warszawa, Poland, Poland 2 University of Warsaw, Faculty of Psychology, ul. Stawki 5/7, 00-183 Warszawa, Poland, Poland 3 University of Social Sciences and Humanities, ul. Chodakowska 19/31, 03-815 Warszawa, Poland, Poland Brain Computer-Interface (BCI) is a system that allows communication through direct measures of neural activity of the brain without muscle involvement. Such systems appear to be the only method of communication for people with severe neuromuscular disorders. There has been rapid development of BCI-systems for the last twenty years, but mostly in respect to accuracy and bit rate, rather than user convenience and ease of use. BCI Appliance, pioneering user-centered design of BCI systems, has been developped since 2009 at the University of Warsaw. The current prototype is a compact, tablet sized box with on/off button, which connects wirelessly to domotic and EEG acquisition devices. Presented hardware implements a unique design providing dynamic menus and stable delivery of SSVEP stimuli. The generation of stimuli is based upon an array of LEDs backlighting an LCD screen, where arbitrary symbols can be rendered within the designated fields. Each LED is independently driven by a micro-controller, which enables a precise control of its flickering frequency. The same system of fields can be used to deliver stimuli for the P300 based BCI. Software controlling the Appliance is based upon the OpenBCI framewok, developped at the University of Warsaw and released on terms of the GPL (http://openbci.pl). While the framework is multisystem and multilanguage, it is developped mostly in GNU/Linux which allows for a lightweight implementation of "Just Enough Operating System". Using the flexible structure and centralized data flow of the OpenBCI, we implemented universal user interface, which can be operated with SSVEP or P300 BCI, as well as other assistive technologies like switches and eyetrackers, with the possibility of unassisted switching between modalities by the user. It is beneficial for these users whose abilities degrade with time or those who would like to switch between the paradigms to reduce fatigue. Calibration procedures are fully automated, thus there is no need for manual fine-tuning of the parameters. This is due to advanced signal processing for feature extraction like Blind Source Separation, Canonical Correlation Analysis and classifiers based on machine learning algorithms. In this sense the software minimizes the need for a highly train personnel assisting the end-user to operate the Appliance. Keywords: brain machine interface, neuromuscular disorders, Neuroscience, brain computer-interfaces, Hardware Design Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Demo Topic: Neuroinformatics Citation: Spustek T, Kruszyński M, Laszuk D, Milanowski P, Łabęcki M, Augustin K, Duszyk A, Kuś R, Żygierewicz J and Durka P (2014). BCI Appliance. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00051 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: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Tomasz Spustek, University of Warsaw, Faculty of Physics, ul. Hoza 69, 00-681 Warszawa, Poland, Warsaw, Poland, tspus@fuw.edu.pl 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 Tomasz Spustek Mateusz Kruszyński Dawid Laszuk Piotr Milanowski Maciej Łabęcki Karol Augustin Anna Duszyk Rafał Kuś Jarosław Żygierewicz Piotr Durka Google Tomasz Spustek Mateusz Kruszyński Dawid Laszuk Piotr Milanowski Maciej Łabęcki Karol Augustin Anna Duszyk Rafał Kuś Jarosław Żygierewicz Piotr Durka Google Scholar Tomasz Spustek Mateusz Kruszyński Dawid Laszuk Piotr Milanowski Maciej Łabęcki Karol Augustin Anna Duszyk Rafał Kuś Jarosław Żygierewicz Piotr Durka PubMed Tomasz Spustek Mateusz Kruszyński Dawid Laszuk Piotr Milanowski Maciej Łabęcki Karol Augustin Anna Duszyk Rafał Kuś Jarosław Żygierewicz Piotr Durka 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.

Brain-computer music interface for composition and performance

This paper introduces a new brain-computer interface (BCI) system that uses electroencephalogram (EEG) information to steer generative rules to compose and perform music. It starts by noting the various attempts at the design of BCI systems, including systems for music. Then it presents a short technical introduction to EEG sensing and analysis. Next, it introduces the generative music component of the system, which employs our own adapted version of a machine-learning technique based on ATNs (Augmented Transition Networks) for the computer-replication of musical styles. The system constantly monitors the EEG of the subject and activates generative rules associated with the activity of different frequency bands of the spectrum of the EEG signal. The system also measures the complexity of the EEG signal to modulate the tempo (beat) and dynamics (loudness) of the performance. Subjects can be trained to select between different classes of generative rules to produce original pieces of music.