Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-B...

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Autores principales:	Sangin Park, Jihyeon Ha, Da-Hye Kim, Laehyun Kim
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	motor imagery brain-computer interface (BCI) sensory stimulation training (SST) somatosensory attentional orientation (SAO) poor performer Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/8830b28f995249108ed65f8ea2340ed3
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spelling	oai:doaj.org-article:8830b28f995249108ed65f8ea2340ed32021-11-05T10:31:58ZImproving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users1662-453X10.3389/fnins.2021.732545https://doaj.org/article/8830b28f995249108ed65f8ea2340ed32021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.732545/fullhttps://doaj.org/toc/1662-453XThe motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.e., hard and rough balls), with a focus on poorly performing users. The proposed method is a hybrid of training and imagery, combining motor execution and somatosensory sensation from a ball-type stimulus. Fourteen healthy participants participated in the somatosensory-motor imagery (SMI) experiments (within-subject design) involving EEG data classification with a three-class system (signaling with left hand, right hand, or right foot). In the scenario of controlling a remote robot to move it to the target point, the participants performed MI when faced with a three-way intersection. The SMI condition had a better classification performance than did the MI condition, achieving a 68.88% classification performance averaged over all participants, which was 6.59% larger than that in the MI condition (p < 0.05). In poor performers, the classification performance in SMI was 10.73% larger than in the MI condition (62.18% vs. 51.45%). However, good performers showed a slight performance decrement (0.86%) in the SMI condition compared to the MI condition (80.93% vs. 81.79%). Combining the brain signals from the motor and somatosensory cortex, the proposed hybrid MI-BCI system demonstrated improved classification performance, this phenomenon was predominant in poor performers (eight out of nine subjects). Hybrid MI-BCI systems may significantly contribute to reducing the proportion of BCI-inefficiency users and closing the performance gap with other BCI systems.Sangin ParkJihyeon HaJihyeon HaDa-Hye KimLaehyun KimLaehyun KimFrontiers Media S.A.articlemotor imagerybrain-computer interface (BCI)sensory stimulation training (SST)somatosensory attentional orientation (SAO)poor performerNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	motor imagery brain-computer interface (BCI) sensory stimulation training (SST) somatosensory attentional orientation (SAO) poor performer Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	motor imagery brain-computer interface (BCI) sensory stimulation training (SST) somatosensory attentional orientation (SAO) poor performer Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Sangin Park Jihyeon Ha Jihyeon Ha Da-Hye Kim Laehyun Kim Laehyun Kim Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
description	The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.e., hard and rough balls), with a focus on poorly performing users. The proposed method is a hybrid of training and imagery, combining motor execution and somatosensory sensation from a ball-type stimulus. Fourteen healthy participants participated in the somatosensory-motor imagery (SMI) experiments (within-subject design) involving EEG data classification with a three-class system (signaling with left hand, right hand, or right foot). In the scenario of controlling a remote robot to move it to the target point, the participants performed MI when faced with a three-way intersection. The SMI condition had a better classification performance than did the MI condition, achieving a 68.88% classification performance averaged over all participants, which was 6.59% larger than that in the MI condition (p < 0.05). In poor performers, the classification performance in SMI was 10.73% larger than in the MI condition (62.18% vs. 51.45%). However, good performers showed a slight performance decrement (0.86%) in the SMI condition compared to the MI condition (80.93% vs. 81.79%). Combining the brain signals from the motor and somatosensory cortex, the proposed hybrid MI-BCI system demonstrated improved classification performance, this phenomenon was predominant in poor performers (eight out of nine subjects). Hybrid MI-BCI systems may significantly contribute to reducing the proportion of BCI-inefficiency users and closing the performance gap with other BCI systems.
format	article
author	Sangin Park Jihyeon Ha Jihyeon Ha Da-Hye Kim Laehyun Kim Laehyun Kim
author_facet	Sangin Park Jihyeon Ha Jihyeon Ha Da-Hye Kim Laehyun Kim Laehyun Kim
author_sort	Sangin Park
title	Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
title_short	Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
title_full	Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
title_fullStr	Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
title_full_unstemmed	Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
title_sort	improving motor imagery-based brain-computer interface performance based on sensory stimulation training: an approach focused on poorly performing users
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/8830b28f995249108ed65f8ea2340ed3
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_version_	1718444300795117568

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

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