CwA-T combines a channelwise CNN autoencoder with a single-head transformer to detect abnormal EEG, reporting 85.0% per-case accuracy on TUH Abnormal EEG Corpus with lower compute than standalone transformers.
Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the global burden of disease study 2016
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection
CwA-T combines a channelwise CNN autoencoder with a single-head transformer to detect abnormal EEG, reporting 85.0% per-case accuracy on TUH Abnormal EEG Corpus with lower compute than standalone transformers.