Errare machinale est: The use of error-related potentials in brain-machine interfaces

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments towards this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches.

EEG-based Recognition of Covert Attentional Shifts

Catherine Brandner, Ricardo Andres Chavarriaga Lozano

Attentional shifts precede most of the perceptual processes, and therefore their recognition may constitute an interesting process for both the study of human perception as well as the design of EEG-based Brain Computer Interfaces (BCI). Several studies have focused on EEG modulations of spatial attention, pointing out to changes in both Alpha and Gamma bands (Thut et al. 2006 J. Neurosci 26:9494; Tallon-Baudry and Bertrand 1999 T Cogn Sci 3:151; Jensen et al. 2007 TINS 30:317). This work aims at further characterization of EEG correlates of attention shifts to evaluate their potential use in BCI applications. We recorded 32-channel scalp EEG while subjects covertly shift their attention towards one out of several spatial targets. Consistently with previous studies, characterization of the spatio-temporal patterns using time-frequency analysis (Morlet wavelet transformation), shows main modulations in the alpha and gamma frequency bands. Moreover, it is also shown that these modulations are characterized by intermittent episodes of activity (Palix et al. 2006 Perception 25:234). Furthermore, we assess the EEG-based recognition of spatial attention -time and target location of the shift- using a machine learning approach based on the detection of informative, episodic activity. The proposed approach is based on the hypothesis that recognition may be improved by identifying the moments where the underlying neural phenomena is more salient (e. g. the appearance of episodic oscillations related to attentional shifts), and performing the classification based mainly -if not exclusively- on the activity during these periods. Our results show accurate recognition of attentional shifts (>75%) based on the scalp EEG signal. Moreover, the proposed approach allow us to efficiently detect endogenous processes loosely synchronized to external events. These results provides evidence supporting the feasibility of using attentional processes in Brain-Computer Interfaces.

2008

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

EEG-based Recognition of Covert Attentional Shifts

Catherine Brandner, Ricardo Andres Chavarriaga Lozano

2008