  It is well known that electroencephalograms (EEGs) often contain artifacts
due to muscle activity, eye blinks, and various other causes. Detecting such
artifacts is an essential first step toward a correct interpretation of EEGs.
Although much effort has been devoted to semi-automated and automated artifact
detection in EEG, the problem of artifact detection remains challenging. In
this paper, we propose a convolutional neural network (CNN) enhanced by
transformers using belief matching (BM) loss for automated detection of five
types of artifacts: chewing, electrode pop, eye movement, muscle, and shiver.
Specifically, we apply these five detectors at individual EEG channels to
distinguish artifacts from background EEG. 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. These segment-level classifiers can
detect specific artifacts with a balanced accuracy (BAC) of 0.947, 0.735,
0.826, 0.857, and 0.655 for chewing, electrode pop, eye movement, muscle, and
shiver artifacts, respectively. Finally, we combine the outputs of the five
segment-level detectors to perform a combined binary classification (any
artifact vs. background). The resulting detector achieves a sensitivity (SEN)
of 60.4%, 51.8%, and 35.5%, at a specificity (SPE) of 95%, 97%, and 99%,
respectively. This artifact detection module can reject artifact segments while
only removing a small fraction of the background EEG, leading to a cleaner EEG
for further analysis.
