self-initiated-attention-shifts-eeg

name

self-initiated-attention-shifts-eeg

description

Subject-specific analysis of self-initiated attention shifts from EEG with controlled internal and external attention conditions. Machine learning + SHAP feature attribution reveals that higher-frequency bands and frontal regions carry subject-specific discriminative information for distinguishing self-initiated vs externally-cued attention shifts (arXiv:2605.18251). Use for EEG attention decoding, self-initiated attention research, voluntary attention neural correlates, SHAP-based EEG interpretation.

Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG

Research methodology from paper: Zeng, Hou, Zhang, Sun, Huang, Tseng, Shioiri (May 2026). arXiv:2605.18251

Overview

Self-initiated attention shifts are critical for voluntary behavior but difficult to study due to absence of explicit temporal markers. This study investigates whether preparatory EEG activity can distinguish self-initiated shifts from externally instructed shifts under identical visual stimulation.

Key Contributions

Experimental Paradigm

• Controlled comparison between task-constrained self-initiated shifts and externally instructed shifts
• Identical visual stimulation for both conditions
• Enables isolation of neural correlates of self-initiation

Analytical Approach

• Performance-oriented assessment: Frequency-specific topographic patterns across multi-dimensional EEG features
• Model-based feature attribution: SHAP (SHapley Additive exPlanations) analysis for interpretable feature importance
• Structured view of how spectral features across regions of interest contribute to model behavior

Key Findings

• Reliable within-subject classification: Preparatory EEG contains subject-specific discriminative information
• Higher-frequency bands (beta, gamma) contribute strongly to model decisions
• Frontal regions show strongest discriminative power
• Subject-specific patterns require individualized models

Methodology

1. EEG recording during attention shift task (self-initiated vs externally-cued)
2. Spectral feature extraction across frequency bands and regions
3. Within-subject classification pipeline
4. SHAP-based feature attribution analysis

Activation

• self-initiated attention, EEG attention decoding, voluntary attention, SHAP EEG, frontal EEG attention, preparatory EEG, attention shift classification, subject-specific EEG, frequency-specific topography, EEG feature attribution

References

• arXiv:2605.18251 - https://arxiv.org/abs/2605.18251