A time domain compound attention neural network for direction perception with vestibular model verification

Vestibular perception is essential for human spatial navigation, providing vital information about motion and orientation. However, existing vestibular research overlooks how the brain dynamically interprets self-motion. We present the Time Domain Compound Attention (TDCA) network, a model that decodes directional states from electroencephalography (EEG). TDCA employs multiscale temporal convolutions to capture both transient and sustained neural dynamics. A self-attention module highlights informative spatial-feature representations, while a temporal convolution module integrates them over time. Using a dataset of vestibular direction perception with synchronized EEG and inertial measurement unit (IMU) recordings from 20 participants performing five motion states (left, right, forward, backward, and stationary), TDCA achieved 93.97% accuracy under subject-independent tenfold cross-validation. Beyond its high decoding accuracy, TDCA’s temporal predictions exhibit strong alignment with an IMU-driven vestibular model, providing biophysically grounded, dual-path validation and supporting its physiological plausibility. These findings advance brain-inspired navigation research and demonstrate the feasibility of online brain-computer interfaces under natural vestibular stimulation.