Individual working memory capacity traced from multivariate pattern classification of EEG spectral power

Abstract

Working Memory (WM) processing is central for human cognitive behavior. Using neurofeedback training to enhance the individual WM capacity is a promising technique but requires careful consideration when choosing the feedback signal. Feedback in terms of univariate spectral power (specifically theta and alpha power) has yielded questionable behavioral effects. However, a promising new direction for WM neurofeedback training is by using a measure of WM that is extracted by multivariate pattern classification. This study recorded EEG oscillatory activity from 15 healthy participants while they were engaged in the n-back task, n∈[1,2]. Univariate measures of the theta, alpha, and theta-over-alpha power ratio and a measure of WM extracted from multivariate pattern classification (of n-back task load conditions) was compared in relation to individual n-back task performance. Results show that classification performance is positively correlated to individual 2-back task performance while theta, alpha and thetaover-alpha power ratio is not. These results suggest that the discriminability of multivariate EEG oscillatory patterns between two WM load conditions reflects individual WM capacity.