Joint Independent Component Analysis for Enhanced Preprocessing in Collaborative Multi-Brain Motor Imagery BCIs

Abstract

Collaborative brain–computer interfaces (cBCIs) extend single-user BCIs to multi-brain recordings, enabling the investigation of inter-brain synchrony and cooperative neural processing. Motor imagery (MI) paradigms are of particular interest but face challenges such as low signal-to-noise ratio, inter-subject variability, and artifact contamination in electroencephalography (EEG) data. Independent component analysis (ICA), though widely applied in single-user MI-BCIs, is suboptimal in cBCIs since it is performed separately for each participant, thereby overlooking shared neural dynamics and potentially distorting inter-brain coupling. To address these limitations, this work proposes a joint ICA-based preprocessing framework that jointly decomposes EEG data from paired participants to enhance artifact suppression while preserving cross-brain synchrony. The performance of joint ICA is evaluated against subject-wise ICA from two perspectives: (i) motor imagery decoding accuracy, examined using spatial filtering with subsequent linear discriminant classification and a convolutional neural network architecture, and (ii) inter-brain synchrony quantified by the phase locking value (PLV) across homologous electrode pairs. Experimental results show that joint ICA improves decoding accuracy by 4.8% and 3.67% in the respective pipelines, while also yielding significantly stronger inter-brain PLV. These findings demonstrate that joint ICA offers an effective preprocessing strategy to improve both neural fidelity and classification robustness in MI-driven cBCI systems.