Reduced Burden of Individual Calibration Process in Brain-Computer Interface by Clustering the Subjects based on Brain Activation

Electroencephalography (EEG) is the primary modality for estimating the user intention in brain-computer interface (BCI). However, the suppression of the inter-subject variability (ISV) remains as a major challenge in constructing a reliable EEG-based BCI model. Subject-specific classification models have been widely used to avoid ISV, however these inherently involve time-consuming individual calibration process. This study speculated that the calibration could be minimized via clustering BCI subjects into subgroups by their respective similarity in brain power distribution at the resting state and conducted a proof-of-concept investigation. EEG recordings of twenty-nine healthy subjects from open motor imagery (MI) dataset were used in this study. K-means clustering based on brain activation in $\alpha$, low $\beta$ and high $\beta$-band at resting state divided the subjects into three subgroups. The efficacy of band-clustering was evaluated by comparing its MI classification performance (left- or right-hand gripping) to subject-specific and general models. Among the subjects in a cluster, ISV was lower than that in twenty-nine subjects, especially in the $\alpha$-band. The MI classification accuracy using the cluster-specific model on the $\alpha$-band was marked high performance (median accuracy 68.8%). The cluster-specific model had significantly high accuracy compared to general model (median accuracy = 64.6%). Furthermore, the difference of MI classification accuracy between the cluster-specific model on the $\alpha$ band and subject-specific model is not significant (median accuracy = 69.3%). Consequently, establishing a model by grouping clusters using similar brain activation patterns was highly beneficial for the MI classification without individual calibration process.