TY - GEN
T1 - Deep Neural Network with Attention Mechanism for Classification of Motor Imagery EEG
AU - Huang, Yen Cheng
AU - Chang, Jia Ren
AU - Chen, Li Fen
AU - Chen, Yong-Sheng
PY - 2019/5/16
Y1 - 2019/5/16
N2 - This paper presents a deep neural network architecture for the classification of motor imagery electroencephalographic recordings. This classification task usually encounters difficulties such as data with poor signal-to-noise ratio, contamination from muscle activity, body movements, and external interferences, and both intra-subject and inter-subject variability. Through the spatiotemporal features automatically learned from training data, deep neural networks continue to demonstrate their good performance, versatility, and adaptation capability. In this work, we developed a novel neural network model which can extract signal features from multiple electrodes in a manner similar to that of conventional signal processing methods, such as common spatial patterns and common temporal patterns. The proposed neural network model comprises an attention mechanism, which calculates the importance of each electrode, and a spatial convolution layer. Compared to the results obtained using a variety of state-of-the-art deep learning techniques, the proposed scheme represents a considerable advancement in classification accuracy when applied to the BCI competition IV dataset 2a. By training with data of all subjects, the proposed universal neural network model outperforms state-of-the-art methods in terms of classification accuracy.
AB - This paper presents a deep neural network architecture for the classification of motor imagery electroencephalographic recordings. This classification task usually encounters difficulties such as data with poor signal-to-noise ratio, contamination from muscle activity, body movements, and external interferences, and both intra-subject and inter-subject variability. Through the spatiotemporal features automatically learned from training data, deep neural networks continue to demonstrate their good performance, versatility, and adaptation capability. In this work, we developed a novel neural network model which can extract signal features from multiple electrodes in a manner similar to that of conventional signal processing methods, such as common spatial patterns and common temporal patterns. The proposed neural network model comprises an attention mechanism, which calculates the importance of each electrode, and a spatial convolution layer. Compared to the results obtained using a variety of state-of-the-art deep learning techniques, the proposed scheme represents a considerable advancement in classification accuracy when applied to the BCI competition IV dataset 2a. By training with data of all subjects, the proposed universal neural network model outperforms state-of-the-art methods in terms of classification accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85066764679&partnerID=8YFLogxK
U2 - 10.1109/NER.2019.8717058
DO - 10.1109/NER.2019.8717058
M3 - Conference contribution
AN - SCOPUS:85066764679
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 1130
EP - 1133
BT - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PB - IEEE Computer Society
T2 - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Y2 - 20 March 2019 through 23 March 2019
ER -