Asynchronous detection and classification of oscillatory brain activity

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.

Ricardo Andres Chavarriaga Lozano

IDIAP2008

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

Analysis of Anticipation Related Potentials for Brain Computer Interaction

Gangadhar Garipelli

Anticipation is a mental process during which a person actively engages in a phase required for the sensory perception and execution of the optimal actions at the arrival of the relevant future events. Since this process occurs before the execution of an intended action, it may be used as a control signal for Brain Computer Interface (BCI) applications. Recognition of neural correlates of this process can enhance the performance of a BCI and in turn reduce mental workload of its users. To this end, it is vital to understand the neural correlates involved in this process and to design robust methods for its recognition in single trials. The analysis of these correlates may also contribute to the basic knowledge of the mechanisms underlying this behavior. The thesis provides three major contributions: (i) it reports methods for the robust recognition of anticipation related Electroencephalogram (EEG) potentials (ii) it provides insights into the selection of appropriate preprocessing steps required for enhancing the Signal-to-Noise Ratio (SNR) of anticipatory slow cortical potentials (SCPs) and (iii) it identifies scalp area specific oscillatory activity related to different aspects of anticipatory behavior. First, we focus on methods for the single trial recognition of anticipatory SCPs using the widely known classical contingent negative variation (CNV) paradigm. Using this paradigm, we demonstrate the feasibility of recognizing the anticipatory SCPs (CNV potentials) using features thatmodel its temporal pattern. We propose a Bayesian approach that exploits temporal evolution and redundancy to quickly classify (e.g., within half of the anticipatory period), without compromising classification accuracy. We then improve upon these recognition rates by using a source localization technique based on the biophysical model of the human head. We further validate the feasibility of recognizing CNV potentials in an online experiment, and report for the first time that, under controlled conditions, these potentials can be reproduced and recognized in realistic interaction scenarios (assistive technology web-browsing) with high accuracies. Second, the thesis provides insights into the selection of appropriate preprocessing stages required for improving the SNR of SCPs. The CNV potentials are characterized by low frequencies that are usually recorded with full-DC, and hence suffer from task-irrelevant high amplitude fluctuations and spatial noise. To account for this, we identified appropriate spectral and spatial filters to improve the SNR. We demonstrate the potential of these preprocessing stages by using fusing multiple electrode specific linear classifiers, which achieve recognition performances of 90±2% (area under curve of receiver operating characteristic), where the classifiers are trained using recordings from one day and tested on the recordings from several days apart. Finally, the thesis identifies different facets of anticipatory behavior. Apart from the widely known CNV potentials, it is not clear which other spectral bands could be related to anticipatory behavior. Using recordings from an experiment (i.e., the assistive technology web browser) where multiple warning stimuli predicted an imperative stimulus, we explored the phase and amplitude response of various oscillatory sub-bands for the identification of markers that could be associated with different aspects of anticipation. From this study we report that: (i) there are duration (4-10 seconds) specific changes in Electroencephalography (EEG) activity in the range 0-1 Hz in the central electrodes correlate with reaction time, (ii) there exist slow oscillations (0.1-1 Hz) in the central electrodes that exhibit phase tuning up to 4 seconds before the onset of a target cue, (iii) there are delta oscillations (1.5-3 Hz), which are entrained to predictive rhythmic warning cues, (iv) there is a selective modulation (increase or decrease) in the amplitude of occipital alpha band (8-12 Hz) based on the relevance of forthcoming visual cue, and (v) there exist a reduction in the beta band (14-30 Hz) amplitude in the sensory motor and association areas lasting up to 10 seconds. The phase tuning and entrainment resulted in a low variance of phase values at the arrival of the imperative stimulus, which may be required for its optimal processing. The amplitude modulation of alpha band activity is likely to be a resultant of sensory suppression and attention. The reduction of beta-band activity over long periods of time suggests holding of sensory-motor association areas until the execution of a planned action. We believe that these observations are the consequence of the endogenous drive on the ongoing oscillations to enhance the processing of the forthcoming stimuli and preparation for an intended action. In summary, the thesis provides methods for the recognition of anticipatory SCPs by exploiting spectral and spatio-temporal characteristics with high performances. By exploring various other oscillatory sub-bands, the spectral characteristics of different aspects of anticipatory behavior are also identified. Further, methods modeling these characteristics can bring forth more robust and faster techniques for BCI systems.