Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

TL;DR

This paper introduces enhanced multiscale temporal and spectral features for EEG-based motor imagery classification, significantly improving accuracy and speed over existing methods using SVM classifiers.

Contribution

It extends CSP and Riemannian covariance feature extractors to multiscale temporal and spectral domains, achieving state-of-the-art accuracy and faster execution in MI-BCI classification.

Findings

Multiscale CSP features achieve 73.70% accuracy, surpassing previous methods.

Riemannian covariance features reach 74.27% accuracy and are 9x faster to train.

Using more temporal windows improves accuracy to 75.47% with faster testing.

Abstract

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.