Skip to Main content Skip to Navigation
Journal articles

A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection

Saugat Bhattacharyya 1 Amit Konar 2 Dewaki Nanddan Tibarewala 2 Mitsuhiro Hayashibe 1
1 CAMIN - Control of Artificial Movement and Intuitive Neuroprosthesis
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.
Document type :
Journal articles
Complete list of metadata

Cited literature [44 references]  Display  Hide  Download
Contributor : Isabelle Gouat Connect in order to contact the contributor
Submitted on : Thursday, January 25, 2018 - 10:31:12 AM
Last modification on : Friday, September 11, 2020 - 11:32:02 AM
Long-term archiving on: : Thursday, May 24, 2018 - 8:15:16 PM


Publisher files allowed on an open archive




Saugat Bhattacharyya, Amit Konar, Dewaki Nanddan Tibarewala, Mitsuhiro Hayashibe. A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection. Frontiers in Neuroscience, Frontiers, 2017, 11, pp.#226. ⟨10.3389/fnins.2017.00226⟩. ⟨lirmm-01692441⟩



Record views


Files downloads