Effects of anxiety on the antisaccade task were assessed. Performance effectiveness on this task (indexed by error rate) reflects a conflict between volitional and reflexive responses resolved by inhibitory processes (Hutton, S. B., & Ettinger, U. (2006). The antisaccade task as a research tool in psychopathology: A critical review. Psychophysiology, 43, 302-313). However, latency of the first correct saccade reflects processing efficiency (relationship between performance effectiveness and use of resources). In two experiments, high-anxious participants had longer correct antisaccade latencies than low-anxious participants and this effect was greater with threatening cues than positive or neutral ones. The high- and low-anxious groups did not differ in terms of error rate in the antisaccade task. No group differences were found in terms of latency or error rate in the prosaccade task. These results indicate that anxiety affects performance efficiency but not performance effectiveness. The findings are interpreted within the context of attentional control theory (Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7 (2), 336-353).

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We investigated the electrophysiological markers of attentional bias for threat in anxiety. Low-anxiety and high-anxiety individuals performed a spatial-cueing task, in which an emotional facial expression (angry or happy) was presented alongside a neutral expression. Results revealed that angry expressions elicited an enhanced N2pc component, but that this was true only for those reporting high levels of trait anxiety. These results confirm the early capture of spatial attention by threat-related stimuli, and demonstrate that this early bias is modulated by trait anxiety. Enhanced P1 amplitudes to targets after presentations of angry expressions were also found; however, this effect was not modulated by trait anxiety levels. Our findings indicate that individual differences in temperament are an important determinant of the early neural response to threat.

Most of the methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These recordings require intensive surgical operations to implant the electrodes within the brain which are hazardous to the patient. Here, we have developed a novel approach to quantify the dynamical changes of the brain using the scalp EEG. The scalp signals are preprocessed by means of an effective block-based blind source separation (BSS) technique to separate the underlying sources within the brain. The algorithm significantly removes the effect of eye blinking artifacts. An overlap window procedure has been incorporated in order to mitigate the inherent permutation problem of BSS and maintain the continuity of the estimated sources. Chaotic behavior of the underlying sources has then been evaluated by measuring the largest Lyapunov exponent. For our experiments, we provided twenty sets of simultaneous intracranial and scalp EEG recordings from twenty patients. The above recordings have been compared. Similar results were obtained when the intracranial electrodes recorded the electrical activity of the epileptic focus. Our preliminary results show a great improvement when the epileptic focus is not captured by the intracranial electrodes.

A robust constrained blind source separation (CBSS) algorithm has been developed as an effective means to remove ocular artifacts (OAs) from electro-encephalograms (EEGs). Currently, clinicians reject a data segment if the patient blinked or spoke during the observation interval. The rejected data segment could contain important information masked by the artifact. In the CBSS technique, a reference signal was exploited as a constraint. The constrained problem was then converted to an unconstrained problem by means of non-linear penalty functions weighted by the penalty terms. This led to the modification of the overall cost function, which was then minimised with the natural gradient algorithm. The effectiveness of the algorithm was also examined for the removal of other interfering signals such as electrocardiograms. The CBSS algorithm was tested with ten sets of data containing OAs. The proposed algorithm yielded, on average, a 19% performance improvement over Parra's BSS algorithm for removing OAs.