Deep Learning Approach for EEG Artifact Identification and Classification

Electroencephalography (EEG) signals are normally susceptible to various artifacts and noises from different sources. In this paper, firstly the existence of artifacts will be identified on the recorded EEG signals and then the origin of the detected artifact will be determined among 7 different sources. Due to the nature of EEG signals, almost no specialist can determine artifact source through eye inspection. This paper introduces the utilization of 1-D Convolutional Neural Network (CNN) in multi-class EEG artifact classification. Proposed CNN models were kept as simple as possible to have the best operation time but in the meantime, models were selected adequately deep to extract appropriate artifact features from applied EEG signals. Obtained results prove that proposed architectures are able to classify artifacts with high accuracy.