Tactile Sensation Assisted Motor Imagery Training for Enhanced BCI Performance: A Randomized Controlled Study

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Abstract

<p class="first" id="d2746245e59">Independent of conventional neurofeedback training, in this study, we propose a tactile sensation assisted motor imagery training (SA-MI Training) approach to improve the performance of MI-based BCI. </p>

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Most cited references 46

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BCI2000: a general-purpose brain-computer interface (BCI) system.

Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger … (2004)

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

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EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich … (2018)

Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.

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Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials

L.A. Farwell, E Donchin (1988)

This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., 'locked-in' patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min.

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Author and article information

Contributors

Lin Yao: (View ORCID Profile)

Yueming Wang: (View ORCID Profile)

Journal

Title: IEEE Transactions on Biomedical Engineering

Abbreviated Title: IEEE Trans. Biomed. Eng.

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

ISSN (Print): 0018-9294

ISSN (Electronic): 1558-2531

Publication date Created: February 2023

Publication date (Print): February 2023

Volume: 70

Issue: 2

Pages: 694-702

Affiliations

[1 ]MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University and College of Computer Science, Zhejiang University, China

[2 ]Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, and College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China

[3 ]Zhejiang Century Huatong Group Co., Ltd, China

[4 ]Qiushi Academy for Advanced Studies and the College of Computer Science, Zhejiang University, China