A Deep Learning Approach Based on CSP for EEG Analysis

Wenchao Huang,Jinchuang Zhao,Wenli Fu

doi:10.1007/978-3-030-00828-4_7

Abstract

Deep learning approaches have been used successfully in computer vision, natural language processing and speech processing. However, the number of studies that employ deep learning on brain-computer interface (BCI) based on electroencephalography (EEG) is very limited. In this paper, we present a deep learning approach for motor imagery (MI) EEG signal classification. We perform spatial projection using common spatial pattern (CSP) for the EEG signal and then temporal projection is applied to the spatially filtered signal. The signal is next fed to a single-layer neural network for classification. We apply backpropagation (BP) algorithm to fine-tune the parameters of the approach. The effectiveness of the proposed approach has been evaluated using datasets of BCI competition III and BCI competition IV.

Highlights

Brain-computer interface (BCI) is a communication system that is established between the human brain and computers or external devices without relying on the regular brain peripheral nerve and muscle systems [1]
We propose a framework based on common spatial pattern (CSP) and backpropagation algorithm for motor imagery (MI)-EEG analysis
We propose a deep learning approach for MI-EEG analysis

Summary

A Deep Learning Approach Based on CSP for EEG Analysis

To cite this version: Wenchao Huang, Jinchuang Zhao, Wenli Fu. A Deep Learning Approach Based on CSP for EEG Analysis. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.62-70, 10.1007/978-3-030-00828-4_7. hal-02197785. To cite this version: Wenchao Huang, Jinchuang Zhao, Wenli Fu. A Deep Learning Approach Based on CSP for EEG Analysis. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés

Introduction

Methods

CSP algorithm

Joint optimization using backpropagation

BCI competition II, dataset III

BCI competition IV, dataset 2a

Findings

Conclusion