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open-access-imgOpen AccessImproving Performance of Motor Imagery-Based Brain–Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity
Author(s)
Sangin Park,
Jihyeon Ha,
Laehyun Kim
Publication year2023
Publication title
ieee transactions on neural systems and rehabilitation engineering
Resource typeMagazines
PublisherIEEE
The phenomena of brain-computer interface-inefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60± 21.62%, 71.25± 19.53%, and 84.09± 12.79% using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. In the poor performance group, the Hybrid-condition II paradigm achieved an accuracy of 81.82%, showing a significant increase of 38.86% and 21.04% in accuracy compared to the control-condition (42.96%) and Hybrid-condition I (60.78%), respectively. Conversely, the good performance group showed a pattern of increasing accuracy, with no significant difference between the three paradigms. The Hybrid-condition II paradigm provided high concentration and discrimination to poor performers in the motor imagery-based brain-computer interface and generated the enhanced event-related desynchronization pattern in three modalities corresponding to different types of somatosensory stimuli in motor and somatosensory regions compared to the Control-condition and Hybrid-condition I. The hybrid-imagery approach can help improve motor imagery-based brain-computer interface performance, especially for poorly performing users, thus contributing to the practical use and uptake of brain-computer interface.
Subject(s)bioengineering , communication, networking and broadcast technologies , computing and processing , robotics and control systems , signal processing and analysis
Keyword(s)Somatosensory, Training, Performance evaluation, Task analysis, Protocols, Electroencephalography, Brain-computer interfaces, BCI inefficient, brain-computer interface, motor imagery, motor imagery training, somatosensory stimuli
Language(s)English
SCImago Journal Rank1.093
H-Index140
eISSN1558-0210
pISSN1534-4320
DOI10.1109/tnsre.2023.3237583

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