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

arXiv: https://arxiv.org/abs/1806.06823 · HTML: https://mlsanity.com/paper/1806.06823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals](https://mlsanity.com/paper/2303.05798/) (2023)
- [Cross Task Neural Architecture Search for EEG Signal Classifications](https://mlsanity.com/paper/2210.06298/) (2022)
- [DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection](https://mlsanity.com/paper/2202.10034/) (2022)
- [Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces](https://mlsanity.com/paper/2102.11221/) (2021)

## Abstract (arXiv)

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±15.90% (mean± standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6±14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27±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±12.8% accuracy with 1.6x faster testing than CSP.