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Difference between revisions of "Fast and Accurate Multiclass Inference for Brain–Computer Interfaces"

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==Links==
 
==Links==
 
: [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]
 
: [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]
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: [https://github.com/MultiScale-BCI/IV-2a code]

Revision as of 16:50, 7 August 2018



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±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.

Keywords

EEG, motor imagery, brain–computer interfaces, multiclass classification, multiscale features, SVM

Status: Completed

Semester thesis by Michael Hersche and Tino Rellstab
Supervision: Abbas Rahimi, Lukas Cavigelli, Pasquale Davide Schiavone

Links

M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.
code