TY - GEN
T1 - A Convolutional Neural Network for Artifacts Detection in EEG Data
AU - Boudaya, Amal
AU - Chaabene, Siwar
AU - Bouaziz, Bassem
AU - Batatia, Hadj
AU - Zouari, Hela
AU - Jemea, Sana ben
AU - Chaari, Lotfi
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022/4/21
Y1 - 2022/4/21
N2 - Electroencephalography (EEG) is an effective tool for neurological disorders diagnosis such as seizures, chronic fatigue, sleep disorders, and behavioral abnormalities. Various artifacts types may impact EEG signals regardless the used, resulting in an erroneous diagnosis. Various data analysis tools have therefore been developed in the biomedical engineering literature to detect and/or remove these artifacts. In this sense, deep learning (DL) is one of the most promising methods. In this paper, we develop a novel method based on artifacts detection using a convolutional neural network (CNN) architecture. The available EEG data was collected using 32 channels from the Nihon Kohden Neurofax EEG-1200. The data are preprocessed and analyzed using our CNN to perform binary artifact detection. The suggested method highlights the best classification results with a maximal accuracy up to 99.20%.
AB - Electroencephalography (EEG) is an effective tool for neurological disorders diagnosis such as seizures, chronic fatigue, sleep disorders, and behavioral abnormalities. Various artifacts types may impact EEG signals regardless the used, resulting in an erroneous diagnosis. Various data analysis tools have therefore been developed in the biomedical engineering literature to detect and/or remove these artifacts. In this sense, deep learning (DL) is one of the most promising methods. In this paper, we develop a novel method based on artifacts detection using a convolutional neural network (CNN) architecture. The available EEG data was collected using 32 channels from the Nihon Kohden Neurofax EEG-1200. The data are preprocessed and analyzed using our CNN to perform binary artifact detection. The suggested method highlights the best classification results with a maximal accuracy up to 99.20%.
UR - http://www.scopus.com/inward/record.url?scp=85129313239&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-7618-5_1
DO - 10.1007/978-981-16-7618-5_1
M3 - Conference contribution
AN - SCOPUS:85129313239
SN - 9789811676178
T3 - Lecture Notes in Networks and Systems
SP - 3
EP - 13
BT - Proceedings of International Conference on Information Technology and Applications. ICITA 2021
A2 - Ullah, Abrar
A2 - Gill, Steve
A2 - Rocha, Álvaro
A2 - Anwar, Sajid
PB - Springer
T2 - 15th International Conference on Information Technology and Applications 2021
Y2 - 13 November 2021 through 14 November 2021
ER -