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

Frontiers in Neuroscience
Saugat BhattacharyyaMitsuhiro Hayashibe

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 feature...Continue Reading

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Citations

Dec 6, 2017·Cognitive, Affective & Behavioral Neuroscience·Ruolei GuLucas S Broster
Feb 16, 2021·Journal of Neural Engineering·Hadar Levi-Aharoni, Naftali Tishby
Aug 13, 2021·Journal of Neural Engineering·Akshay KumarQiang Fang
Mar 30, 2021·Journal of Neural Engineering·Catarina Lopes-DiasGernot R Müller-Putz

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Methods Mentioned

BETA
Extraction
feature extraction

Software Mentioned

ErrP

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