Comparing BCI performance using scalp EEG-and inverse solution-based features

Several studies have proposed the use of inverse solutions based features to improve the decoding performance of brain-computer interfaces. Most of these studies have compared the performance of inverse solutions features over scalp activity in a small set of electrodes. However, the estimated sources are indeed a linear combination of scalp-wide activity. Therefore, this comparison may be biased against surface EEG. Performance comparison in three ERP-based protocols show that classifiers combining larger sets of EEG electrodes may perform comparably, and previous reports may have overestimated the advantages of using inverse solution based features.

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

Asynchronous detection and classification of oscillatory brain activity

Ricardo Andres Chavarriaga Lozano

The characterization and recognition of electrical signatures of brain activity constitutes a real challenge. Applications such as Brain-Computer Interfaces (BCI) are based on the accurate identification of mental processes in order to control external devices. Traditionally, classification of brain activity patterns relies on the assumption that the neurological phenomena that characterize mental states is continuously present in the signal. However, recent evidence shows that some mental processes are better characterized by episodic activity that is not necessarily synchronized with external stimuli. In this paper, we present a method for classification of mental states based on the detection of this episodic activity. Instead of performing classification on all available data, the proposed method identifies informative samples based on the class sample distribution in a projected canonical feature space. Classification results are compared to traditional methods using both artificial data and real EEG recordings.

2008

Asynchronous detection and classification of oscillatory brain activity

Ricardo Andres Chavarriaga Lozano

IDIAP2008

EEG-based Recognition of Covert Attentional Shifts

Catherine Brandner, Ricardo Andres Chavarriaga Lozano

2008