An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training

• Published in: Frontiers in human neuroscience Vol. 15; p. 625983
• Main Authors: Duan, Xu, Xie, Songyun, Xie, Xinzhou, Obermayer, Klaus, Cui, Yujie, Wang, Zhenzhen
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 07.06.2021; Frontiers Media S.A
• Subjects: Brain; brain–computer interface; Calibration; Classification; Computer applications; Discriminant analysis; EEG; Electroencephalography; Experiments; Feedback; Geometry; Handedness; Implants; Learning algorithms; Machine learning; Mental task performance; motor imagery; Neuroscience; Riemannian geometry; Sensorimotor system; Skills; training protocol; Visualization
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2021.625983

Brain–computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect.