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Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding
Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding
Shaorong Zhang
(Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China)
Shaorong Zhang
(School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China)
Qihui Wang
(School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China)
Benxin Zhang
(School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China)
Zhen Liang
(Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China)
Li Zhang
(Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China)
Linling Li
(Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China)
Gan Huang
(Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China)
Zhiguo Zhang
(Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China)
Bao Feng
(School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China)
Tianyou Yu
(School of Automation Science and Engineering, South China University of Technology, Guangzhou, China)
Journal: Frontiers in Neuroscience
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• Frontiers Media S.A.
• ISSN: 1662-453X
Abstract
IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.
Shaorong Zhang, Shaorong Zhang, Qihui Wang, et al. (2023). "Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding". Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2023.1292724