DeepTokenEEG

Uses

How it works

Comparisons

Highlighted claims

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