Machine learning analysis of preparatory EEG activity shows subject-specific patterns that distinguish self-initiated from externally cued attention shifts, with strong contributions from higher-frequency bands and frontal regions.
Time courses of attentional modulation in neural amplification and synchronization measured with steady-state visual-evoked potentials,
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Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Machine learning analysis of preparatory EEG activity shows subject-specific patterns that distinguish self-initiated from externally cued attention shifts, with strong contributions from higher-frequency bands and frontal regions.