NDLI: Expliciting SSVEP misclassifications with extra-brain activities using time-frequency EEG analysis

Content Provider	IEEE Xplore Digital Library
Author	Daachi, B. Gergondet, P. Boubchir, L. Kheddar, A.
Copyright Year	2015
Description	Author affiliation: CRT, AIST Joint Robot. Lab. (JRL), Tsukuba, Japan (Daachi, B.; Gergondet, P.; Kheddar, A.) \|\| LIASD Res. Lab., Univ. of Paris 8, St. Denis, France (Boubchir, L.)
Abstract	In order to use brain physiological signals to control a robotic system in the task space, it is mandatory to distinguish as quickly as possible and very reliably bad choices due to wrong brain signals classifications. This allows one to: (i) eventually recover non-desired resulting (robotic) actions, while in the same time, (ii) improve the classifier/controller parameters in order to interpret more precisely the brain signals for the next actions. Instead of using EEG error potential identification (ErrP), we instruct the users to explicit misclassifications using one of the two following extra-brain activities: briefly clenching teeth or closing the eyes. The experiments conducted on three healthy subjects, show that these two extra-brain activities are detectable by EEG time-frequency analysis and in less than one second if the user is focused. Indeed, associated potentials are clearly distinguished after they are made following a bad classification result is revealed to the user. Our analysis and results are based on a brain machine interface (BMI) using the Steady State Visual Evoked Potentials technique (SSVEP).
Starting Page	1020
Ending Page	1025
File Size	817574
Page Count	6
File Format	PDF
ISSN	19457901
e-ISBN	9781479918089
DOI	10.1109/ICORR.2015.7281338
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-11
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Electric potential Time-frequency analysis Visualization Electroencephalography Robots
Content Type	Text
Resource Type	Article
Subject	Rehabilitation Control and Systems Engineering Electrical and Electronic Engineering

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