Towards a Robust BCI: Error Potentials and Online Learning

Recent advances in the field of Brain-Computer Interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful laboratory demonstrations, there are still issues that need to be addressed before BCIs can be used by non-experts outside the laboratory. At IDIAP we have been investigating several areas that we believe will allow us to improve the robustness, flexibility and reliability of BCIs. One area is recognition of cognitive error states, that is, identifying errors through the brain's reaction to mistakes. The production of these error potentials (ErrP) in reaction to an error made by the user is well established. We have extended this work by identifying a similar but distinct ErrP that is generated in response to an error made by the interface, (a misinterpretation of a command that the user has given). This ErrP can be satisfactorily identified in single trials and can be demonstrated to improve the theoretical performance of a BCI. A second area of research is online adaptation of the classifier. BCI signals change over time, both between sessions and within a single session, due to a number of factors. This means that a classifier trained on data from a previous session will probably not be optimal for a new session. In this paper we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase. We also discuss the future direction of this research, including the combination of these two currently separate issues to create a potentially very powerful BCI.

Online Classifier Adaptation in Brain-Computer Interfaces

Anna Buttfield

Brain-computer interfaces (BCIs) aim to provide a new channel of communication by enabling the subject to control an external systems by using purely mental commands. One method of doing this without invasive surgical procedures is by measuring the electrical activity of the brain on the scalp through electroencephalography (EEG). A major obstacle to developing complex EEG-based BCI systems that provide a number of intuitive mental commands is the high variability of EEG signals. EEG signals from the same subject vary considerably within a single session and between sessions on the same or different days. To deal with this we are investigating methods of adapting the classifier while it is being used by the subject. By keeping the classifier constantly tuned to the EEG signals of the current session we hope to improve the performance of the classifier and allow the subject to learn to use the BCI more effectively. This paper discusses preliminary offline and online experiments towards this goal, focusing on the initial training period when the task that the subject is trying to achieve is known and thus supervised adaptation methods can be used. In these experiments the subjects were asked to perform three mental commands (imagination of left and right hand movements, and a language task) and the EEG signals were classified with a Gaussian classifier.

IDIAP2006

Discriminative Channel Selection Method for the Recognition of Anticipation related Potentials from CCD estimated Cortical Activity

Ricardo Andres Chavarriaga Lozano, Gangadhar Garipelli

Recognition of brain states and subject's intention from electroencephalogram (EEG) is a challenging problem for brain-computer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from very diverse brain regions. However, un-mixing methods such as Cortical Current Density (CCD) can be used for estimating activity of different brain regions. These methods not only improve spatial resolution but also signal to noise ratio, hence the classifiers computed using this activity may ameliorate recognition performances. However, these methods lead to a multiplied number of channels, leading to the question -- ``How to choose relevant and discriminant channels from a large number of channels?''. In the current paper we present a channel selection method and discuss its application to the recognition of anticipation related potentials from surface EEG channels and CCD estimated cortical potentials. We compare the classification accuracies with previously reported performances obtained using Cz electrode potentials of 9 subjects (3 experienced + 6 naive). As hypothesised, we observed improvements for most subjects with channel selection method applied to CCD activity as compared to surface-EEG channels and baseline performances. This improvement is particularly significant for subjects who are naive and did not show a clear pattern on ERP grand averages.

2009

Adaptation in Brain-Computer Interfaces

Anna Buttfield

One major challenge in Brain-Computer Interface (BCI) research is to cope with the inherent nonstationarity of the recorded brain signals caused by changes in the subjects brain processes during an experiment. Online adaptation of the classifier embedded into the BCI is a possible way of tackling this issue. In this chapter we investigate the effect of adaptation on the performance of the classifier embedded in three different BCI systems, all of them based on non-invasive electroencephalogram (EEG) signals. Through this adaptation we aim to keep the classifier constantly tuned to the EEG signals it is receiving in the current session. Although the experimental results reported here show the benefits of online adaptation, some questions need still to be addressed. The chapter ends discussing some of these open issues.

The MIT Press2007