scholarly journals Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

2014 ◽  
Vol 61 (5) ◽  
pp. 1425-1435 ◽  
Author(s):  
Han Yuan ◽  
Bin He
Keyword(s):  
Brain Computer Interfaces ◽  
Future Perspectives ◽  
Computer Interfaces ◽  
Current State ◽  
Sensorimotor Rhythms

scholarly journals The current state of electrocorticography-based brain–computer interfaces

2020 ◽  
Vol 49 (1) ◽  
pp. E2 ◽  
Author(s):  
Kai J. Miller ◽  
Dora Hermes ◽  
Nathan P. Staff
Keyword(s):  
Functional Limitations ◽  
Brain Computer Interfaces ◽  
Brain Surface ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Temporal And Spatial ◽  
Direct Feedback ◽  
The Brain ◽  
Lateral Sclerosis

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

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

scholarly journals The human-computer connection: An overview of brain-computer interfaces

2018 ◽  
Author(s):  
José del R. Millán
Keyword(s):  
State Of The Art ◽  
Brain Computer Interfaces ◽  
Motor Rehabilitation ◽  
Ongoing Research ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Active Motor ◽  
Research Questions ◽  
Main Components

This article introduces the field of brain-computer interfaces (BCI), which allows the control of devices without the generation of any active motor output but directly from the decoding of the user’s brain signals. Here we review the current state of the art in the BCI field, discussing the main components of such an interface and illustrating ongoing research questions and prototypes for controlling a large variety of devices, from virtual keyboards for communication to robotics systems to replace lost motor functions and even clinical interventions for motor rehabilitation after a stroke. The article concludes with some insights into the future of BCI.

scholarly journals Brain–Computer Interfaces for Augmentative and Alternative Communication: A Tutorial

2018 ◽  
Vol 27 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Jonathan S. Brumberg ◽  
Kevin M. Pitt ◽  
Alana Mantie-Kozlowski ◽  
Jeremy D. Burnison
Keyword(s):  
Clinical Application ◽  
Augmentative And Alternative Communication ◽  
Scope Of Practice ◽  
Brain Computer Interfaces ◽  
Alternative Communication ◽  
Access Methods ◽  
Computer Interfaces ◽  
Current State ◽  
Starting Point ◽  
Areas Of Interest

Purpose Brain–computer interfaces (BCIs) have the potential to improve communication for people who require but are unable to use traditional augmentative and alternative communication (AAC) devices. As BCIs move toward clinical practice, speech-language pathologists (SLPs) will need to consider their appropriateness for AAC intervention. Method This tutorial provides a background on BCI approaches to provide AAC specialists foundational knowledge necessary for clinical application of BCI. Tutorial descriptions were generated based on a literature review of BCIs for restoring communication. Results The tutorial responses directly address 4 major areas of interest for SLPs who specialize in AAC: (a) the current state of BCI with emphasis on SLP scope of practice (including the subareas: the way in which individuals access AAC with BCI, the efficacy of BCI for AAC, and the effects of fatigue), (b) populations for whom BCI is best suited, (c) the future of BCI as an addition to AAC access strategies, and (d) limitations of BCI. Conclusion Current BCIs have been designed as access methods for AAC rather than a replacement; therefore, SLPs can use existing knowledge in AAC as a starting point for clinical application. Additional training is recommended to stay updated with rapid advances in BCI.

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.

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