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

Sangin Park; Jihyeon Ha; Da-Hye Kim; Laehyun Kim

doi:10.3389/fnins.2021.732545

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

Front Neurosci. 2021 Nov 5:15:732545. doi: 10.3389/fnins.2021.732545. eCollection 2021.

Authors

Sangin Park¹, Jihyeon Ha^{1

2}, Da-Hye Kim¹, Laehyun Kim^{1

3}

Affiliations

¹ Center for Bionics, Korea Institute of Science and Technology, Seoul, South Korea.
² Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
³ Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea.

Abstract

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.

Keywords: brain-computer interface (BCI); motor imagery; poor performer; sensory stimulation training (SST); somatosensory attentional orientation (SAO).