Spatial Insight: How Data-Driven Regions of Interest Selection Enhances Single-Trial P300 Classification in EEG-Based BCIs

TL;DR

This paper introduces a novel EEG analysis framework that combines source localization and functional connectivity to identify stable brain regions, significantly improving single-trial P300 classification for neurodegenerative disease detection.

Contribution

It presents the first method to establish cross-subject ROI consensus via source-space connectivity, enhancing EEG-based P300 detection accuracy and robustness for clinical applications.

Findings

Phase-lagged connectivity isolates task-relevant hubs in deep brain structures.

Hybrid ROI-based classification outperforms whole-brain approaches in accuracy.

Framework maintains millisecond temporal precision and robustness to noise.

Abstract

EEG-based Brain-Computer Interfaces (BCIs) frequently face spatial specificity limitations in detecting single-trial P300 potentials, a neurophysiological hallmark leveraged for both BCI control and neurodegenerative disease diagnostics. We present a novel framework combining eLORETA source localization with cross-subject functional connectivity to identify stable regions of interest (ROIs) across sessions. Analyzing 62-channel EEG data from 31 subjects (63 sessions, 2,520 trials), we demonstrate that phase-lagged connectivity metrics can reliably isolate task-relevant hubs in deeper cortical-subcortical structures like the insula and parietal regions - critical for Alzheimer's disease biomarkers. By integrating spatially stable ROIs with dynamic temporal agreement, our hybrid classification systematically outperforms whole-brain approaches in different frequency bands (up to 5.4% depending on the connectivity method and the spectral range) while maintaining millisecond-level temporal precision. To the best of our knowledge, this is the first study to establish cross-subject ROI consensus through source-space connectivity, bypassing scalp EEG's depth constraints to probe Alzheimer's-relevant networks. The framework's robustness to noise and compatibility with portable systems offer significant potential for global deployment in early neurodegenerative disease detection. Future integration of individualized anatomical data or adaptive parameter optimization could refine this tool for clinical deployment, enhancing the current max accuracy of 81.57% in the 1-15 Hz range.