Song Minsu, Jeong Hojun, Kim Jongbum, Jang Sung-Ho, Kim Jonghyun
Department of Medical Device, Korea Institute of Machinery and Materials, Daegu, South Korea.
School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi-do, South Korea.
Front Neurorobot. 2022 Sep 12;16:971547. doi: 10.3389/fnbot.2022.971547. eCollection 2022.
Many studies have used motor imagery-based brain-computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.
许多研究已将基于运动想象的脑机接口（MI-BCI）系统用于中风康复，以诱导大脑可塑性。然而，这些研究主要集中于检测运动想象，并未考虑误报（FP）检测的影响。误报可能对中风患者构成威胁，因为它会诱导错误方向的大脑可塑性，从而产生不良影响。在本研究中，我们提出了一种侧重于排除误报的康复MI-BCI系统。为此，我们首先将大量脑电图（EEG）信号确定为误报的原因，并基于这些信号的特征，使用包括误报源在内的少量EEG通道设计了一种新颖的两阶段分类器。通过对八名健康参与者和九名中风患者进行实验，我们提出的MI-BCI系统在中风患者组中仅使用四个EEG通道时，显示出71.76%的选择性和13.70%的误报率。此外，我们的系统可以通过重新校准来补偿长时间会话间隔中的日常变化。结果表明，我们提出的系统作为一种适用于临床环境的实用方法，可以通过减少误报的不利影响来提高MI-BCI的治疗效果。
