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Author(s)
Sangin Park,
Jihyeon Ha,
Laehyun Kim
Publication year2023
Publication title
ieee transactions on neural systems and rehabilitation engineering
Resource typeJournals
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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