Next Article in Journal
Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring
Next Article in Special Issue
Quality Assessment of Single-Channel EEG for Wearable Devices
Previous Article in Journal
Electromagnetic Modeling and Structure Optimization of a Spherical Force Sensing System
Previous Article in Special Issue
Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework

1,2, 1,2,*,†, 1,2, 3,*,† and 1,2
1
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
2
Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100191, China
3
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(3), 551; https://doi.org/10.3390/s19030551
Received: 30 November 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 29 January 2019
(This article belongs to the Special Issue EEG Electrodes)
  |  
PDF [1901 KB, uploaded 29 January 2019]
  |     |  

Abstract

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art. View Full-Text
Keywords: EEG; deep learning; short-time Fourier transform; convolutional neural network; variational autoencoder EEG; deep learning; short-time Fourier transform; convolutional neural network; variational autoencoder
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Dai, M.; Zheng, D.; Na, R.; Wang, S.; Zhang, S. EEG Classification of Motor Imagery Using a Novel Deep Learning Framework. Sensors 2019, 19, 551.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top