Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
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Date
2018
Publication Type
Conference Paper
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yes
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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 9× faster in training and 4× faster in testing. Using more temporal windows for Riemannian features results in 75.47±12.8% accuracy with 1.6× faster testing than CSP.
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published
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Book title
2018 26th European Signal Processing Conference (EUSIPCO)
Journal / series
Volume
Pages / Article No.
1690 - 1694
Publisher
IEEE
Event
26th European Signal Processing Conference (EUSIPCO 2018)
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Methods
Software
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Date collected
Date created
Subject
EEG; Motor imagery; Brain-computer interfaces; Multiclass classification; Multiscale features; SVM
Organisational unit
03996 - Benini, Luca / Benini, Luca
Notes
Funding
608881 - ETH Zurich Postdoctoral Fellowship Program II (EC)