SOURCE-LINKED INTELLIGENCE

Beyond Flattened Tokens: Structure-Preserving EEG Decoding with Reusable TriDim Blocks

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

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. The

Read original source ↗ Open in workspace

recordType

paper

region

Global