Distribution Based Learning Network for Motor Imagery Electroencephalogram Classification
In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), 2021-04-23
Online
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Zugriff:
The low Signal Noise Ratio (SNR) and nonstationarity of electroencephalogram (EEG) signals affect the classification accuracy badly in motor imagery electroencephalogram (MI-EEG) classification. In this paper, a Distribution Based Learning (DBL) framework based on deep learning is proposed to improve the accuracy. Firstly, the framework uses modified multi band Common Spatial Pattern (CSP) algorithm to pre-process the raw EEG signals. Secondly, a Distribution Based Learning Network (DBLN) is utilized to divide the dataset into two parts. After that, a two-step distribution based learning and testing strategy are conducted on the two parts separately. Experimental results on BCI Competition IV Dataset 2b indicate that accuracy of DBL is 3.84 % higher than the state-of-the-art, which proves the effectiveness of the algorithm.
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Distribution Based Learning Network for Motor Imagery Electroencephalogram Classification
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Autor/in / Beteiligte Person: | Gong, Ziyang ; Wang, Annan |
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Zeitschrift: | 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), 2021-04-23 |
Veröffentlichung: | IEEE, 2021 |
Medientyp: | unknown |
DOI: | 10.1109/icccs52626.2021.9449094 |
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