Journal
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Volume 18, Issue 2, Pages 541-551Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2020.3021456
Keywords
Feature extraction; Electroencephalography; Time-frequency analysis; Machine learning; Task analysis; Robustness; Brain modeling; Brain– computer interfaces (BCIs); convolutional neural network (CNN); motor imagery (MI); sparse representation; spectral decomposition
Categories
Funding
- National Natural Science Foundation of China [61971303, 81971660, 61675039]
- National Key Research and Development Program of China [2017YFB1300301]
- Beijing Major Science and Technology Project [Z191100010618004]
- Tianjin Special Branch Plan High Level Innovation Team Grant
- Tianjin Key Projects of Natural Science Foundation [18JCZDJC32700]
- CAMS Innovation Fund for Medical Sciences [2016-I2M-3-023]
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This study introduced a deep learning framework called SSD-SE-CNN for MI-EEG classification, which utilizes sparse spectrotemporal decomposition and convolutional neural network with squeeze-and-excitation blocks to improve accuracy and robustness, suitable for long-term MI-EEG applications.
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks raises a big challenge in the design and development of brain-computer interfaces (BCIs). In view of the characteristics of nonstationarity, time-variability, and individual diversity of EEG signals, a deep learning framework termed SSD-SE-convolutional neural network (CNN) is proposed for MI-EEG classification. The framework consists of three parts: 1) the sparse spectrotemporal decomposition (SSD) algorithm is proposed for feature extraction, overcoming the drawbacks of conventional time-frequency analysis methods and enhancing the robustness to noise; 2) a CNN is constructed to fully exploit the time-frequency features, thus outperforming traditional classification methods both in terms of accuracy and kappa value; and 3) the squeeze-and-excitation (SE) blocks are adopted to adaptively recalibrate channelwise feature responses, which further improves the overall performance and offers a compelling classification solution for MI-EEG applications. Experimental results on two datasets reveal that the proposed framework outperforms state-of-the-art methods in terms of both classification quality and robustness. The advantages of SSD-SE-CNN include high accuracy, high efficiency, and robustness to cross-trial and cross-session variations, making it an ideal candidate for long-term MI-EEG applications. Note to Practitioners-Motor imagery-based brain-computer interfaces (MI-BCIs) are widely used to allow a user to control a device using only his or her neural activity. This article proposed a new framework to classify two-class MI tasks based on electroencephalography (EEG) signals. In this framework, a new sparse spectrotemporal decomposition method is used to extract time-frequency features from EEG signals. A convolutional neural network with squeeze-and-excitation blocks is then constructed to classify the MI tasks. We show the superiority of our method on two datasets and prove its feasibility for long-term MI-BCI applications.
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