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High-resolution event-related potentials (ERPs) were used to model the hemispherical representation of the transient cortical responses relating to the observation of movement during execution (right or left aimless finger extension). Subjects were seated in front of the observed person and looked at both their own and the observer's hand to receive similar visual feedback during the two conditions. In a visual control condition, a diode light moved at the observed person's hand. A first potential accompanying the movement execution peaked at about +110 msec over the contralateral somatomotor areas. It was followed by a potential (P300) peaking at about +350 msec over the central midline. In contrast, the potentials accompanying the movement observation peaked later over parietal-occipital other than somatomotor areas (N200 peak, +200 msec; P300 peak, +400 msec). Notably, the N200 was maximum in left parietal area whereas the P300 was maximum in right parietal area regardless the side of the movement. They markedly differed by the potentials following the displacement of the diode light. These results suggest a rapid time evolution (~200–400 msec) of the cortical responses characterizing the observation of aimless movements (as opposite to grasping or handling). The execution of these movements would mainly involve somatomotor cortical responses and would be scarcely founded on the visual feedback. In contrast, the observation of the same movements carried out by others would require dynamical responses of somatomotor and parietal-occipital areas (especially of the right hemisphere), possibly for a stringent visuospatial analysis of the motor event. Hum. Brain Mapping 20:148–157, 2003. © 2003 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. [ABSTRACT FROM AUTHOR]