Effective EEG decoding requires representations that preserve organization among channels, local waveform dynamics, and long-range temporal context.
Existing EEG architectures often capture these structures using separate specialized modules or collapse them into a single token sequence, making it difficult to maintain their distinct roles and coordinate their interactions throughout the backbone.
We propose TriDim, a reusable block that preserves the representation shape and keeps three EEG axes explicit: channel, sample position within each patch, and patch position across the recording.
These axes correspond to spatial, short-term temporal, and long-term temporal information, respectively.
Each TriDim block applies feed-forward transformations along individual axes and cross-axis attention to coordinate information exchange among them.
By stacking TriDim blocks with a multi-level tri-axis readout, we construct TriDimEEG, a standalone EEG decoder.
Under strict cross-subject evaluation on eight datasets spanning clinical diagnosis, sleep staging, motor imagery, and emotion recognition, TriDimEEG achieves the best overall performance among fifteen evaluated models, with a 4.3% relative improvement in average accuracy over the second-best model.
Replacing Transformer blocks in three EEG foundation models with TriDim blocks yields an average relative improvement of 7.4% in downstream accuracy while reducing parameter counts by 17.0% to 47.3%.
These results establish TriDim as an effective and reusable building block and TriDimEEG as a strong standalone EEG decoder.
Code and parameters of TriDimEEG are available at https://github.com/ncclab-sustech/TriDim_model.