Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions

2026-05-18

AI Summary

This study addresses the challenge of identifying neural signatures of spontaneous attentional shifts from multivariate electroencephalography (EEG) signals in the absence of explicit temporal markers. Leveraging a controlled experimental paradigm that differentiates internally driven and externally cued attention, the research focuses on EEG activity during the preparatory phase preceding attentional transitions. By integrating machine learning classification, SHAP-based interpretability analysis, and frequency-band-specific topographic mapping, the work distinguishes spontaneous from externally guided attentional shifts. Results demonstrate reliable within-subject classification performance, with high-frequency bands (e.g., beta and gamma) and frontal regions contributing most significantly to discrimination. These findings reveal that preparatory EEG activity encodes individual-specific neural representations of attentional transitions, offering a novel foundation for personalized brain–computer interfaces.

Abstract

Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it remains unclear how multi-dimensional electroencephalography (EEG) features contribute to their characterization within an interpretable computational framework. In this study, we build on an experimental paradigm developed in our previous work, which enables controlled comparison between task-constrained self-initiated shifts and externally instructed shifts under identical visual stimulation. Within this setting, we investigate whether preparatory EEG activity can distinguish these two types of attention shifts. We adopt a machine learning-based approach and conduct two complementary analyses: (1) a performance-oriented assessment of frequency-specific topographic patterns, and (2) a model-based feature attribution analysis using SHapley Additive exPlanations (SHAP). These analyses provide a structured view of how spectral features across regions of interest contribute to model behavior. Our results demonstrate reliable within-subject classification performance, indicating that preparatory EEG activity contains subject-specific discriminative information within this paradigm. The analysis shows that higher-frequency bands and frontal regions contribute strongly to model decisions, although such contributions should be interpreted cautiously due to the potential influence of non-neural artifacts in high-frequency EEG signals. Overall, this work highlights the value of interpretable machine learning for analyzing subject-specific EEG signal patterns in a controlled experimental setting, with potential applications in personalized and asynchronous brain-machine interface systems.

Problem

• self-initiated attention shifts
• EEG
• subject-specific analysis
• controlled attention conditions
• neural correlates

Innovation

• interpretable machine learning
• subject-specific EEG
• self-initiated attention shifts
• SHAP
• frequency-specific topography