Representation Learning for Motor Imagery Recognition with Deep Neural Network

Xu, Fangzhou; Rong, Fenqi; Miao, Yunjing; Sun, Yanan; Dong, Gege; Li, Han; Li, Jincheng; Wang, Yuandong; Leng, Jiancai

doi:10.3390/electronics10020112

Open AccessArticle

Representation Learning for Motor Imagery Recognition with Deep Neural Network

by

Fangzhou Xu

^1,*,†

,

Fenqi Rong

^2,†,

Yunjing Miao

²,

Yanan Sun

²,

Gege Dong

¹,

Han Li

¹,

Jincheng Li

¹,

Yuandong Wang

¹ and

Jiancai Leng

^1,*

¹

Department of Physics, School of Electronic and Information Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

²

School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

^*

Authors to whom correspondence should be addressed.

^†

Fangzhou Xu and Fenqi Rong have made equal contributions to this work and should be regarded as joint first authors.

Electronics 2021, 10(2), 112; https://doi.org/10.3390/electronics10020112

Submission received: 17 December 2020 / Revised: 29 December 2020 / Accepted: 1 January 2021 / Published: 7 January 2021

(This article belongs to the Special Issue Intelligent Learning and Health Diagnosis Technologies)

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Abstract

This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems.

Keywords: electrocorticogram (ECoG); motor imagery (MI); brain–computer interface (BCI); convolution neural network (CNN); gradient boosting (GB)

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MDPI and ACS Style

Xu, F.; Rong, F.; Miao, Y.; Sun, Y.; Dong, G.; Li, H.; Li, J.; Wang, Y.; Leng, J. Representation Learning for Motor Imagery Recognition with Deep Neural Network. Electronics 2021, 10, 112. https://doi.org/10.3390/electronics10020112

AMA Style

Xu F, Rong F, Miao Y, Sun Y, Dong G, Li H, Li J, Wang Y, Leng J. Representation Learning for Motor Imagery Recognition with Deep Neural Network. Electronics. 2021; 10(2):112. https://doi.org/10.3390/electronics10020112

Chicago/Turabian Style

Xu, Fangzhou, Fenqi Rong, Yunjing Miao, Yanan Sun, Gege Dong, Han Li, Jincheng Li, Yuandong Wang, and Jiancai Leng. 2021. "Representation Learning for Motor Imagery Recognition with Deep Neural Network" Electronics 10, no. 2: 112. https://doi.org/10.3390/electronics10020112

APA Style

Xu, F., Rong, F., Miao, Y., Sun, Y., Dong, G., Li, H., Li, J., Wang, Y., & Leng, J. (2021). Representation Learning for Motor Imagery Recognition with Deep Neural Network. Electronics, 10(2), 112. https://doi.org/10.3390/electronics10020112

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Representation Learning for Motor Imagery Recognition with Deep Neural Network

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