Group analysis and classification of working memory task conditions using electroencephalogram cortical currents during an n-back task

Abstract

Electroencephalographic studies of working memory have demonstrated cortical activity and oscillatory representations without clarifying how the stored information is retained in the brain. To address this gap, we measured scalp electroencephalography data, while participants performed a modified n-back working memory task. We calculated the current intensities from the estimated cortical currents by introducing a statistical map generated using Neurosynth as prior information. Group analysis of the cortical current level revealed that the current amplitudes and power spectra were significantly different between the modified n-back and delayed match-to-sample conditions. Additionally, we classified information on the working memory task conditions using the amplitudes and power spectra of the currents during the encoding and retention periods. Our results indicate that the representation of executive control over memory retention may be mediated through both persistent neural activity and oscillatory representations in the beta and gamma bands over multiple cortical regions that contribute to visual working memory functions.

Keywords: EEG; artifact; hierarchical Bayesian estimation; sparse logistic regression; working memory.

Figure 1

(A) Illustration of task design. (B) We extracted each trial from −0.5 to 8.0 s and calculated a grand average of the ERSP (event-related spectral perturbation) spectrogram of EEG signals across all channels (using wavelet analysis). During the retention period, the largest periodic change was observed between 6 and 7.5 s.

Figure 2

(A) Linear and non-linear transformation for meta-analysis fMRI data from MNI152 to individual T1 spaces. (B) Illustration of the extra-dipole method. (C) Illustration of classification of modified n-back and DMTS task conditions.

Figure 3

Cortical current distribution using a statistical map generated by Neurosynth (example of a typical subject).

Figure 4

Differences in magnitudes of estimated source currents and power spectral densities between modified n-back and DMTS conditions. (A) Number of significant current sources for each subperiod of encoding and retention. (B) Significant current source locations on the cortical surface map for the subperiods of encoding and retention.

Figure 5

(A) Mean values and standard errors of scores (accuracy, precision, recall, F-measure, and balanced accuracy) for each subperiod of encoding and retention using the weighted sparse logistic regression method. (B) Ratios of types of selected dipole numbers. We counted the number of times it was selected as a weighted SLR feature for each trial, calculated the mean ratio for each participant, and plotted the average ratios as a stacked bar chart. The rate of selected dipole for currents, low beta, high beta, and gamma waves are shown as red, green, brown, and yellow bars, respectively.