EEG Preprocessing
Cross-source consensus on EEG Preprocessing from 1 sources and 6 claims.
1 sources · 6 claims
Preparation
Comparisons
Highlighted claims
- All datasets were standardized to a 19-channel international 10-20 montage. — DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
- Signals were filtered from 0.5 to 45 Hz and resampled to 128 Hz. — DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
- Stationary wavelet transform with symlet-4 was used for frequency-band decomposition. — DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
- Each rhythm-specific signal was segmented into 1-second windows with 50% overlap. — DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
- Each segment was normalized with per-channel z-scores computed within the segment. — DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
- SWT was chosen because it preserves translation invariance and temporal localization better than alternatives cited in the article. — DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features