Motor Imagery BCI Classification with Frequency and Time-Frequency Features by Using Different Dimensions of the Feature Space Using Autoencoders

• Published in: Intelligent Methods in Engineering Sciences
• Main Authors: Kaya, Esra, Saritas, Ismail
• Format: Journal Article
• Language: English
• Published: 29.12.2022
• ISSN: 2979-9236; 2979-9236
• DOI: 10.58190/imiens.2022.2

Brain-Computer Interfaces (BCIs) enable the users to directly communicate with machines based on various desired purposes through brain signals without moving any body parts. Thus, they have become very useful for prostheses, electric wheelchairs, virtual keyboards, and other studies like survey applications and emotion classifications. In this study, EEG signal processing was performed on the BCI Competition III-3a dataset, which contains motor imagery (MI) signals with four classes. Features of the non-stationary EEG signals belonging to three subjects were extracted using Power Spectral Density (PSD) with welch method, Wavelet Decomposition (WD), Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT). From extracted 900 features, feature space dimension reduction was realized using Autoencoder, an unsupervised learning algorithm. The average accuracy obtained with Artificial Neural Network (ANN) is 74.5% for all binary classifications, which is generally a good result because of the non-stationary nature of EEG signals. 801 features yielded the best classification performance, obtained using an autoencoder with 400 hidden layer neurons. Brain-Computer Interfaces (BCIs) enable the users to directly communicate with machines based on various desired purposes through brain signals without moving any body parts. Thus, they have become very useful for prostheses, electric wheelchairs, virtual keyboards, and other studies like survey applications and emotion classifications. In this study, EEG signal processing was performed on the BCI Competition III-3a dataset, which contains motor imagery (MI) signals with four classes. Features of the non-stationary EEG signals belonging to three subjects were extracted using Power Spectral Density (PSD) with welch method, Wavelet Decomposition (WD), Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT). From extracted 900 features, feature space dimension reduction was realized using Autoencoder, an unsupervised learning algorithm. The average accuracy obtained with Artificial Neural Network (ANN) is 74.5% for all binary classifications, which is generally a good result because of the non-stationary nature of EEG signals. 801 features yielded the best classification performance, obtained using an autoencoder with 400 hidden layer neurons.