TY - GEN
T1 - EEG Feature Significance Analysis
AU - Zhang, Yuhao
AU - Yao, Yue
AU - Hossain, Zakir
AU - Rahman, Shafin
AU - Gedeon, Tom
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Electroencephalography (EEG), a recording of brain activities, is usually feature fused. Although such feature fusion characteristic makes many brain-computer interfaces (BCI) possible, it makes it hard to distinguish task-specific features. As a result, current works usually use the whole EEG signal or features for a specific task like classification, regardless of the fact that many of the features are not task-related. In this paper, we aim to analyze the task-specific significance of EEG features. Specifically, we extract the frequency domain features and perform classification on them. To ensure a generalized conclusion, we use various classification architectures like Multilayer Perceptron (MLP) and 2D convolutional neural network (Conv2D). Extensive experiments are conducted on the UCI EEG dataset. We find that the front part of the brain, namely channel Fpz, AFz, Fp1, and Fp2 contains the general distinct features. Besides, the beta frequency band of the EEG signal is the most significant in the alcoholism classification task.
AB - Electroencephalography (EEG), a recording of brain activities, is usually feature fused. Although such feature fusion characteristic makes many brain-computer interfaces (BCI) possible, it makes it hard to distinguish task-specific features. As a result, current works usually use the whole EEG signal or features for a specific task like classification, regardless of the fact that many of the features are not task-related. In this paper, we aim to analyze the task-specific significance of EEG features. Specifically, we extract the frequency domain features and perform classification on them. To ensure a generalized conclusion, we use various classification architectures like Multilayer Perceptron (MLP) and 2D convolutional neural network (Conv2D). Extensive experiments are conducted on the UCI EEG dataset. We find that the front part of the brain, namely channel Fpz, AFz, Fp1, and Fp2 contains the general distinct features. Besides, the beta frequency band of the EEG signal is the most significant in the alcoholism classification task.
KW - Alcoholism
KW - Deep learning
KW - EEG
KW - Significance analysis
UR - http://www.scopus.com/inward/record.url?scp=85121911673&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-92310-5_25
DO - 10.1007/978-3-030-92310-5_25
M3 - Conference contribution
SN - 9783030923099
T3 - Communications in Computer and Information Science
SP - 212
EP - 220
BT - Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
A2 - Mantoro, Teddy
A2 - Lee, Minho
A2 - Ayu, Media Anugerah
A2 - Wong, Kok Wai
A2 - Hidayanto, Achmad Nizar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Neural Information Processing, ICONIP 2021
Y2 - 8 December 2021 through 12 December 2021
ER -