Deep Learning-based Classification for Brain-Computer Interfaces

[jinglescode]

Paper

Link: https://ieeexplore.ieee.org/abstract/document/8122608
Year: 2017

Summary

Brain-computer interface (BCI) is an emerging area of research that aims to improve the quality of human-computer applications. It has enormous scope in biomedical applications, neural rehabilitation, biometric authentication, educational programmes, and entertainment applications. A BCI system has four major components: signal acquisition, signal preprocessing, feature extraction, and classification. In this study, we provide a comparison of various traditional classification algorithms to the newer methods of deep learning. We explore two different types of deep learning methods, namely, convolutional neural networks (CNN) and recurrent neural networks (RNN) with long short-term memory (LSTM) architecture. We test the classification accuracies on a recent 5-dass steady-state visual evoked potential (SSVEP) dataset. The results prove the superiority of deep learning methods in comparison with the traditional classification algorithms. Amongst the traditional classifiers, support vector machine (SVM) with Gaussian kernel employing sequential forward selection (SFS) of features provided a better classification accuracy of 66.09%, while CNN provided the highest classification accuracy of 69.03%.

Methods

• 5 classes SSVEP
• CNN: 1 conv (filters 16) -> FC (size 2000)
• LSTM: 1 bidirectional LSTM

Comments

• a large part of the paper describing trivial concepts like. structure of LSTM cell
• only 1 para of results, with trivial results (DL methods outperform traditional classifiers)
• simple model