An Unsupervised Clustering and Markov Chain-Based Approach for Assessing Performance During Online User Training for Mental Imagery EEG-BCIs

Brain-computer interfaces (BCIs) have many potential applications for individuals with physical disabilities; however, their usage is limited due to unreliable performance. While users can improve performance via training, the effectiveness of current training approaches may be limited by inaccurate performance assessments and confusing feedback. Herein, we render recently proposed user performance assessments for mental imagery electroencephalography (EEG)-BCIs conducive to online deployment. The approach uses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex>-means clustering to segment the EEG signal space into pattern states and then models transitions between these states using Markov chains. A metric, taskDistinct , uses the Markov chain steady-state distributions to measure user ability to produce task-specific EEG patterns. The objective of this work was twofold: first, to assess the sensitivity of the adjusted metrics to performance variations throughout a session; and second, to examine the influence of the number of pattern states in the models on this sensitivity. To meet these objectives, we performed pseudo-online analyses where the taskDistinct metric was computed with various numbers of pattern states throughout simulated data collection sessions. Analysis revealed significant positive correlations between the adapted taskDistinct metric and other performance metrics. Additionally, the metric sensitivity to performance changes was not significantly affected by the number of pattern states. The results indicate that the adapted Markov chain-based metrics could be used for assessing performance in online user training for mental imagery EEG-BCIs.