Minimizing calibration time of single-trial recognition of error potentials in brain-computer interfaces

Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil
2011
Conference paper

Abstract

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users. Although it has been studied how to reduce calibration time for asynchronous signals, it is still an open issue for event- related potentials. Here, we analyze the differences between users for single-trial error-related potentials, and propose the design of classifiers based on inter-subject features to either remove or minimize the calibration time. The results show that it is possible to have a classifier with a high performance from the beginning of the experiment, which is able to adapt itself without the user noticing.

Official source

https://infoscience.epfl.ch/record/166743?ln=en

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EPFL2020

To Err Is Human: Learning from Error Potentials in Brain-Computer Interfaces

Ricardo Andres Chavarriaga Lozano, Pierre Ferrez

Several studies describe evoked EEG potentials elicited when a subject is aware of an erroneous decision either taken by him or by an external interface. This paper studies {\em Error-related potentials} (ErrP) elicited when a human user monitors an external system upon which he has no control whatsoever. In addition, the possibility of using the ErrPs as a learning signals to infer the user's intended strategy is also addressed. Experimental results show that single-trial recognition of correct and error trials can be achieved, allowing the fast learning of the user's strategy. These results may constitute the basis of a new kind of human-computer interaction where the former provides monitoring signals that can be used to modify the performance of the latter.This work has been supported by the Swiss National Science Foundation NCCR-IM2 and by the EC-contract number BACS FP6-IST-027140. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

IDIAP2007

To Err Is Human: Learning from Error Potentials in Brain-Computer Interfaces

Ricardo Andres Chavarriaga Lozano, Pierre Ferrez

2007

EPFL2020