Decomposing delta, theta, and alpha time-frequency ERP activity from a visual oddball task using PCA

Abstract

Objective: Time-frequency (TF) analysis has become an important tool for assessing electrical and magnetic brain activity from event-related paradigms. In electrical potential data, theta and delta activities have been shown to underlie P300 activity, and alpha has been shown to be inhibited during P300 activity. Measures of delta, theta, and alpha activity are commonly taken from TF surfaces. However, methods for extracting relevant activity do not commonly go beyond taking means of windows on the surface, analogous to measuring activity within a defined P300 window in time-only signal representations. The current objective was to use a data driven method to derive relevant TF components from event-related potential data from a large number of participants in an oddball paradigm.

Methods: A recently developed PCA approach was employed to extract TF components [Bernat, E. M., Williams, W. J., and Gehring, W. J. (2005). Decomposing ERP time-frequency energy using PCA. Clin Neurophysiol, 116(6), 1314-1334] from an ERP dataset of 2068 17 year olds (979 males). TF activity was taken from both individual trials and condition averages. Activity including frequencies ranging from 0 to 14 Hz and time ranging from stimulus onset to 1312.5 ms were decomposed.

Results: A coordinated set of time-frequency events was apparent across the decompositions. Similar TF components representing earlier theta followed by delta were extracted from both individual trials and averaged data. Alpha activity, as predicted, was apparent only when time-frequency surfaces were generated from trial level data, and was characterized by a reduction during the P300.

Conclusions: Theta, delta, and alpha activities were extracted with predictable time-courses. Notably, this approach was effective at characterizing data from a single-electrode. Finally, decomposition of TF data generated from individual trials and condition averages produced similar results, but with predictable differences. Specifically, trial level data evidenced more and more varied theta measures, and accounted for less overall variance.

Fig. 1

Illustration of the decomposition process. In step 1, a dataset of waveforms is transformed into a dataset of time–freq surfaces, one for each waveform. In step 2, a principal components analysis is conducted. First the number of components is chosen from the singular values plot. The components are then extracted (and varimax rotated) and displayed graphically. Finally, descriptive labels are assigned.

Fig. 2

Decomposition of trial level activity across the full range of assessed frequencies (0–14 Hz). Grand average time and time–freq. plots are presented at the top (see Fig. 1 for surface and color keys). Two principal components analysis decompositions are presented below the grand averages. Scree plot contains singular values (units not relevant) for the largest 30 components, depicting the relative variance accounted for by each component. Within the scree plot, blue indicates the components extracted for decomposition of a lower number of components (6), and red the additional components extracted for decomposition of a higher number of components (14). Numbers in blue or red next to each component detail the order of variance explained for the unrotated solution, and thus correspond to the order in the scree plot. Numbers in black correspond to the order of variance explained for each component in the varimax rotated solution — i.e. the displayed component surfaces. Components are sorted by time.

Fig. 3

Decomposition of averaged activity below the alpha range (0–5.75 Hz). Grand average time and time–freq. plots are presented at the top (see Fig. 1 for surface and color keys). Two principal components analysis decompositions are presented below the grand averages. Scree plot contains singular values (units not relevant) for the largest 30 components, depicting the relative variance accounted for by each component. Within the scree plot, blue indicates the components extracted for decomposition of a lower number of components (5), and red the additional components extracted for decomposition of a higher number of components (11). Numbers in blue or red next to each component detail the order of variance explained for the unrotated solution, and thus correspond to the order in the scree plot. Numbers in black correspond to the order of variance explained for each component in the varimax rotated solution — i.e. the displayed component surfaces. Components are sorted by time.

Fig. 4

Decomposition of averaged activity below the alpha range (0–5.75 Hz). Grand average time and time–freq. plots are presented at the top (see Fig. 1 for surface and color keys). Two principal components analysis decompositions are presented below the grand averages. Scree plot contains singular values (units not relevant) for the largest 30 components, depicting the relative variance accounted for by each component. Within the scree plot, blue indicates the components extracted for decomposition of a lower number of components (5), and red the additional components extracted for decomposition of a higher number of components (16). Numbers in blue or red next to each component detail the order of variance explained for the unrotated solution, and thus correspond to the order in the scree plot. Numbers in black correspond to the order of variance explained for each component in the varimax rotated solution — i.e. the displayed component surfaces. Components are sorted by time.

Fig. 5

Cross-validation decompositions including each of the 6 presented solutions split into even and odd participant groups. The number of components extracted was chosen from the original decomposition for all cases (see Figs. 2–4). All components correspond between the two cross-validation sets, supporting the contention that the extracted components are stable.