Abstract
Nowadays, classifying electroencephalogram (EEG) signals based motor imagery tasks is extensively used to control brain-computer interface applications, as a communication bridge between humans and computers. In this paper, we propose signal-to-image transformation and feature extraction methods for the classification of motor imagery. Specifically, a continuous wavelet transform is applied to decompose EEG signals into five rhythms and generate time-frequency images. Then, a gray-level co-occurrence matrix is used to extract global texture features on time-frequency images. Finally, the SVM classification model is optimized by using a grid search algorithm to select optimal parameter pairs \((C,\sigma )\). To confirm the validity of the proposed methods, we experimented on self-collected data, which is obtained using a consumer-grade EEG device. The experimental result showed that this proposed method can achieve an acceptable classification accuracy of 90% for a two-class problem (left/right-hand motor imagery).
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Acknowledgment
This work was supported by the National Natural Science Foundation of China [U1809209, 61702376, 61972187, 61772254], the Major Project of Wenzhou Natural Science Foundation [ZY2019020], the Key Project of Zhejiang Provincial Natural Science Foundation [LSZ19F020001], Fujian Provincial Leading Project [2017H0030, 2019H0025], Government Guiding Regional Science and Technology Development [2019L3009], and Natural Science Foundation of Fujian Province [2017J01768, 2019J01756]. We acknowledge the efforts and constructive comments of respected editors and anonymous reviewers.
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Luo, Z., Hu, Z., Li, Z. (2020). Estimation of Motor Imagination Based on Consumer-Grade EEG Device. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_27
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DOI: https://doi.org/10.1007/978-3-030-62460-6_27
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