期刊
PATTERN RECOGNITION
卷 105, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2020.107390
关键词
Motor imagery; Electroencephalography topographical representation; Convolutional neural network; Machine learning; Signal pre-processing
资金
- National Nature Science Foundation of China [61772440]
- Natural Science Foundation of the Science & Technology Bureau of Fujian Province in 2019 [2019j01601]
- Foundation of the Science & Technology Bureau of Xiamen Municipal Government in 2018 [3502Z20184058]
- State Grid Shaanxi Electric Power Company
- State Grid Shaanxi Information and Telecommunication Company [SGSNXTOOGCJS1900134]
- Foreign Cooperation Project of the Science & Technology Bureau of Fujian Province in 2018 [201810015]
- Science & Technology Bureau Project of Fujian Province in 2018 [2019C0021]
Electroencephalography (EEG) topographical representation (ETR) can monitor regional brain activities and is emerging as a successful technique for causally exploring cortical mechanisms and connections. However, it is a challenge to find a robust method supporting high-dimensional EEG data with low signal-to-noise ratios from multiple objects and multiple channels. To address this issue, a new ETR energy calculation method for learning the EEG patterns of brain activities using a convolutional neural network is reported. It is able to customize temporal ETR training and recognize multiple objects within a common learning model. Specifically, an open-access dataset from the 2008 Brain-Computer Interface (BCI) Competition IV-2a is used for classification of five classes containing four Motor Imagery actions and one relax action. The proposed classification framework outperforms the best state-of-the-art classification method by 10.11% in average subject accuracy. Furthermore, by studying the ETR parameter optimization, a user interface for BCI applications is obtained and a real-time method implemented. (C) 2020 Elsevier Ltd. All rights reserved.
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