scholarly journals Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)

2014 ◽  
Vol 8 ◽  
Author(s):  
Eva M. Hammer ◽  
Tobias Kaufmann ◽  
Sonja C. Kleih ◽  
Benjamin Blankertz ◽  
Andrea Kübler
Keyword(s):  
Motor Coordination ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Coordination Ability ◽  
Sensorimotor Rhythms

Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces

2011 ◽  
Vol 23 (3) ◽  
pp. 791-816 ◽  
Author(s):  
Carmen Vidaurre ◽  
Claudia Sannelli ◽  
Klaus-Robert Müller ◽  
Benjamin Blankertz
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Computer Application ◽  
Brain Computer Interfaces ◽  
Significant Control ◽  
Guided Learning ◽  
Computer Interfaces ◽  
Calibration Measurement ◽  
Online Feedback ◽  
Sensorimotor Rhythms

Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.

scholarly journals Communications Methods to patients with locked-in syndrome

2019 ◽  
Vol 4 (1) ◽  
pp. 21
Author(s):  
Maria Tzogia ◽  
Dimitrios Papageorgiou
Keyword(s):  
Cerebral Cortex ◽  
Present Review ◽  
Brain Computer Interfaces ◽  
Surgical Operation ◽  
New Methods ◽  
Computer Interfaces ◽  
Study Results ◽  
Sensorimotor Rhythms ◽  
Communication Methods ◽  
Sciverse Scopus

ntroduction: Brain-computer interfaces (BCIs) that promote communication with individuals suffering from locked-in syndrome (LIS), are variously superior to the classic methods. These interfaces, whether intrusive or not, have evolved and are now accessible to patients, thus contributing mainly to the production of written speech, to the control of personal computers, and to the management of the patient’s environment. Aim: The aim of the present review was to evaluate the effectiveness with LIS patients of communication methods using technology. Methodology: The search of the Greek and international bibliography involved the databases: Pubmed, Cinahl, Sciverse Scopus Proquest, Researchgate, Cochranelibrary, etc. 1,652 items were found and 15 were judged appropriate for study. Results: There is a wide variety of available BCIs, depending on the minimum demands made on the user, the needs served and the time the user takes to learn them. Communication remains a time-consuming process and thus a source of great anxiety to patients. Furthermore, there is a slight superiority in the possibilities offered by intrusive BCIs. However, they are often not preferred because they require a surgical operation. Conclusions: There is an identified need to find new methods, or to modify already existing ones, for the more effective communication with patients who suffer from all forms of LIS. However, the dysfunctions in the control of the sensorimotor rhythms (due to alterations or damage to the cerebral cortex) may adversely impact the perfection of BCI technology.  

scholarly journals Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

2015 ◽  
Vol 103 (6) ◽  
pp. 907-925 ◽  
Author(s):  
Bin He ◽  
Bryan Baxter ◽  
Bradley J. Edelman ◽  
Christopher C. Cline ◽  
Wenjing W. Ye
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Sensorimotor Rhythms

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals Privacy and brain-computer interfaces

2016 ◽  
Vol 46 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Kirsten Wahlstrom ◽  
N. Ben Fairweather ◽  
Helen Ashman
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces
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