Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

The analysis of electroencephalographic and magnetoencephalographic recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a “map”) and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

Many socially significant biological stimuli are polymodal, and information processing is enhanced for polymodal over unimodal stimuli. The human superior temporal sulcus (STS) region has been implicated in processing socially relevant stimuli — particularly those derived from biological motion such as mouth movements. Single unit studies in monkeys have demonstrated that regions of STS are polysensory — responding to visual, auditory and somato-sensory stimuli, and human neuroimaging studies have shown that lip-reading activates auditory regions of the lateral temporal lobe. We evaluated whether concurrent speech sounds and mouth movements were more potent activators of STS than either speech sounds or mouth movements alone. In an event-related fMRI study, subjects observed an animated character that produced audiovisual speech and the audio and visual components of speech alone. Strong activation of the STS region was evoked in all three conditions, with greatest levels of activity elicited by audiovisual speech. Subsets of activated voxels within the STS region demonstrated overadditivity (audiovisual > audio + visual) and underadditivity (audiovisual < audio + visual). These results confirm the polysensory nature of STS region and demonstrate for the first time that polymodal interactions may both potentiate and inhibit activation.

Many functional neuroimaging studies of biological motion have used as stimuli point-light displays of walking figures and compared the resulting activations with those evoked by the same display elements moving in a random or noncoherent manner. Although these studies have established that biological motion activates the superior temporal sulcus (STS), the use of random motion controls has left open the possibility that coordinated and meaningful nonbiological motion might activate these same brain regions and thus call into question their specificity for processing biological motion. Here we used functional magnetic resonance imaging and an anatomical region-of-interest approach to test a hierarchy of three questions regarding activity within the STS. First, by comparing responses in the STS with animations of human and robot walking figures, we determined (1) that the STS is sensitive to biological motion itself, not merely to the superficial characteristics of the stimulus. Then we determined that the STS responds more strongly to biological motion (as conveyed by the walking robot) than to (2) a nonmeaningful but complex nonbiological motion (a disjointed mechanical figure) and (3) a complex and meaningful nonbiological motion (the movements of a grandfather clock). In subsequent whole-brain voxel-based analyses, we confirmed robust STS activity that was strongly right lateralized. In addition, we observed significant deactivations in the STS that differentiated biological and nonbiological motion. These voxel-based analyses also revealed regions of motion-related positive activity in other brain regions, including MT or V5, fusiform gyri, right premotor cortex, and the intraparietal sulci.