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Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution

Abstract : This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density of the EEG signal in the central motor cortex. The associated three parameters are estimated using the maximum likelihood method. Based on these parameters, a simple and efficient linear classifier was designed to classify three types of events: imagery, movement, and resting. Preliminary results show that the proposed statistical model can be used in order to detect precisely the mu-suppression and distinguish different EEG events, with very good classification accuracy.
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https://hal.archives-ouvertes.fr/hal-03159930
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Submitted on : Thursday, March 4, 2021 - 7:04:30 PM
Last modification on : Tuesday, October 19, 2021 - 2:24:23 PM
Long-term archiving on: : Saturday, June 5, 2021 - 7:22:58 PM

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Antonio Quintero Rincón, Carlos D’giano, Hadj Batatia. Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution. International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, United Kingdom. pp.1-5, ⟨10.1109/IJCNN48605.2020.9206862⟩. ⟨hal-03159930⟩

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