TY - GEN
T1 - Deep Learning Approach for EEG Artifact Identification and Classification
AU - Rajabiour, Ramin
AU - Akyurek, Ali Ozen
AU - Sezer, Ebru Akcapinar
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Artifact Classification
KW - Automated Feature Extraction
KW - Convolutional Neural Network (CNN)
KW - Deep Learning
KW - Electroencephalography (EEG)
UR - https://www.scopus.com/pages/publications/85125867110
U2 - 10.1109/UBMK52708.2021.9558979
DO - 10.1109/UBMK52708.2021.9558979
M3 - Conference contribution
AN - SCOPUS:85125867110
T3 - Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
SP - 320
EP - 325
BT - Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Computer Science and Engineering, UBMK 2021
Y2 - 15 September 2021 through 17 September 2021
ER -