Deep Neural Network with Attention Mechanism for Classification of Motor Imagery EEG

Yen Cheng Huang, Jia Ren Chang, Li Fen Chen, Yong-Sheng Chen

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題9th International IEEE EMBS Conference on Neural Engineering, NER 2019
發行者IEEE Computer Society
頁面1130-1133
頁數4
ISBN(電子)9781538679210
DOIs
出版狀態Published - 16 5月 2019
事件9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
持續時間: 20 3月 201923 3月 2019

出版系列

名字International IEEE/EMBS Conference on Neural Engineering, NER
2019-March
ISSN(列印)1948-3546
ISSN(電子)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
國家/地區United States
城市San Francisco
期間20/03/1923/03/19

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