DeepTokenEEG
The selected encoder configuration uses three cascaded stages because it balanced accuracy, stability, and computational cost. DeepTokenEEG is composed of a tokenizer, an encoder, and a classifier. The tokenizer uses depthwise convolution…
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The selected encoder configuration uses three cascaded stages because it balanced accuracy, stability, and computational cost. DeepTokenEEG is composed of a tokenizer, an encoder, and a classifier. The tokenizer uses depthwise convolution to extract local temporal structure from each EEG channel independently. The tokenizer then uses pointwise convolution to mix channel information into a learned latent embedding while preserving temporal length. DeepTokenEEG is presented as suitable for portable EEG and edge deployment because it is compact and fast.