Consistent with the notion that emotional stimuli receive preferential attention and perceptual processing, many event-related potential (ERP) components appear sensitive to emotional stimuli. In an effort to differentiate components that are sensitive to emotional versus neutral stimuli, the current study utilized temporospatial principal components analysis to analyze ERPs from a large sample (N=82) while pleasant, neutral, and unpleasant images were passively viewed. Several factors sensitive to emotional stimuli were identified-corresponding to the N1, early posterior negativity (EPN), and P3; multiple factors resembling the late positive potential (LPP) emerged. Results indicate that the N1 represents the earliest component modulated by emotional stimuli; the EPN and the LPP represent unique components; the scalp-recorded LPP appears to include a P3-like positivity as well as additional positivities at occipital and central recording sites.

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Event-related potential studies of reward processing have consistently identified the feedback negativity (FN), an early neural response that differentiates feedback indicating unfavorable versus favorable outcomes. Several important questions remain, however, about the nature of this response. In this study, the FN was recorded in response to monetary gains and losses during a laboratory gambling task, and temporospatial principal components analysis was used to separate the FN from overlapping responses. The FN was identified as a positive deflection at frontocentral recording sites that was enhanced for rewards compared with nonrewards. Furthermore, source localization techniques identified the striatum as a likely neural generator. These data indicate that this apparent FN reflects increased striatal activation in response to favorable outcomes that is reduced or absent for unfavorable outcomes, thereby providing unique information about the timing and nature of basal ganglia activity related to reward processing.

Principal components analysis (PCA) has attracted increasing interest as a tool for facilitating analysis of high-density event-related potential (ERP) data. While every researcher is exposed to this statistical procedure in graduate school, its complexities are rarely covered in depth and hence researchers are often not conversant with its subtleties. Furthermore, application to ERP datasets involves unique aspects that would not be covered in a general statistics course. This tutorial seeks to provide guidance on the decisions involved in applying PCA to ERPs and their consequences, using the ERP PCA Toolkit to illustrate the analysis process on a novelty oddball dataset.

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Principal components analysis (PCA) can facilitate analysis of event-related potential (ERP) components. Geomin, Oblimin, Varimax, Promax, and Infomax (independent components analysis) were compared using a simulated data set. Kappa settings for Oblimin and Promax were also systematically compared. Finally, the rotations were also analyzed in a two-step PCA procedure, including a contrast between spatiotemporal and temporospatial procedures. Promax was found to give the best overall results for temporal PCA, and Infomax was found to give the best overall results for spatial PCA. The current practice of kappa values of 3 or 4 for Promax and 0 for Oblimin was supported. Source analysis was meaningfully improved by temporal Promax PCA over the conventional windowed difference wave approach (from a median 32.9 mm error to 6.7 mm). It was also found that temporospatial PCA produced modestly improved results over spatiotemporal PCA.

In this study, we examined the relationship between the novelty P3 and the P300 components of the brain event-related potential (ERP). Fifteen subjects responded manually to the rare stimuli embedded either in a classical auditory oddball series or in a series in which "novel" stimuli were inserted. The electroencephalogram (EEG) was recorded with a dense array of 129 electrodes. The data were analyzed by using spatial Principal Components Analysis (PCA) to identify a set of orthogonal scalp distributions, "virtual electrodes" that account for the spatial variance. The data were then expressed as ERPs measured at each of the virtual electrodes. These ERPs were analyzed using temporal PCA, yielding a set of "virtual epochs." Most of the temporal variance of the rare events was associated with a virtual electrode with a posterior topography, that is, with a classical P300, which was active during the virtual epoch associated with the P300. The novel stimuli were found to elicit both a classical P300 and a component focused on a virtual electrode with a frontal topography. We propose that the term Novelty P3 should be restricted to this frontal component.

Falkenstein, Hohnsbein, and Hoorman (1994) suggested that common measures of P300 latency confound a "P-SR" component whose latency corresponds to stimulus evaluation time and a "P-CR" component whose latency varies with response-selection time, thus casting doubt on work in mental chronometry that relies on P300 latency. We report here a replication and extension of Falkenstein et al. (1994) using a high-density 129-electrode montage with 11 subjects. Spatiotemporal PCA was used to extract the components of the ERP. A centroid measure is also introduced for detecting waveform-timing changes beyond just peak latency. In terms of componentry, we argue that the P-SR and the P-CR, correspond to the P3a/Novelty P3 and the P300, respectively. Conceptually, we dispute the proposed distinction between stimulus evaluation and response selection. We suggest a four-stage ERP model of information processing and place the P3a and the P300 in this framework.