Decoding Perceived Speech from High-Frequency EEG Phase Patterns

Retrieving segments of continuous speech from scalp electroencephalography (EEG) identifies what a listener heard among candidate audio segments. Existing methods mainly exploit low-frequency EEG related to slower changes in speech. High-frequency EEG (>50 Hz) contains timing cues associated with the speech fundamental frequency and rapid envelope fluctuations, but its low signal-to-noise ratio has limited its use for this task. We ask whether these signals independently support multi-candidate retrieval, complement low-frequency decoding, and retain segment-discriminative information in their phase structure. We train separate matching branches for low- and high-frequency EEG with speech features at corresponding time scales, fuse their scores, and compare waveform, phase, two-bin phase, and power representations of the same high-frequency signal. For 17 unseen listeners, the high-frequency branch attains 5.62% Top-1 accuracy in five-second retrieval from an average of more than 1,000 candidates per listener. Score fusion raises full-pool Top-1 accuracy from 8.65% for the low-frequency branch to 15.39%. Continuous phase and even a one-bit phase representation retain substantial retrieval performance after discarding amplitude scale. These results establish the complementary value of high-frequency EEG and the segment-level utility of its rapid phase-related timing structure. Code is available at https://anonymous.4open.science/r/HFEEG-decoding-2203/.