Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG

no code implementations • 4 Aug 2022 • Wei Yan Peh, Yuanyuan Yao, Justin Dauwels

Next, for each of these five types of artifacts, we combine the output of these channel-wise detectors to detect artifacts in multi-channel EEG segments.

Artifact Detection Binary Classification +2

Six-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in EEG

no code implementations • 29 Jul 2022 • Wei Yan Peh, Prasanth Thangavel, Yuanyuan Yao, John Thomas, Yee Leng Tan, Justin Dauwels

In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG).

EEG Seizure Detection

Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features

no code implementations • 28 Sep 2020 • Wei Yan Peh, John Thomas, Elham Bagheri, Rima Chaudhari, Sagar Karia, Rahul Rathakrishnan, Vinay Saini, Nilesh Shah, Rohit Srivastava, Yee-Leng Tan, Justin Dauwels

The TDS, SLDS, and DLDS performs prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level.

EEG