A comprehensive feature and data mining study on musician memory processing using EEG signals

Abstract

There has been much interest in the beneficial effects of musical training on cognition. Previous studies have indicated that musical training was related to better working memory and that these behavioral differences were associated with differences in neural activity in the brain. However, it was not clear whether musical training impacts memory in general, beyond working memory. By recruiting professional musicians with extensive training, we investigated if musical training has a broad impact on memory with corresponding electroencephalography (EEG) signal changes, by using working memory and long-term memory tasks with verbal and pictorial items. Behaviorally, musicians outperformed on both working memory and long-term memory tasks. A comprehensive EEG pattern study has been performed, including various univariate and multivariate features, time-frequency (wavelet) analysis, power-spectra analysis, and deterministic chaotic theory. The advanced feature selection approaches have also been employed to select the most discriminative EEG and brain activation features between musicians and non-musicians. High classification accuracy (more than 95%) in memory judgments was achieved using Proximal Support Vector Machine (PSVM). For working memory, it showed significant differences between musicians versus non-musicians during the delay period. For long-term memory, significant differences on EEG patterns between groups were found both in the pre-stimulus period and the post-stimulus period on recognition. These results indicate that musicians memorial advantage occurs in both working memory and long-term memory and that the developed computational framework using advanced data mining techniques can be successfully applied to classify complex human cognition with high time resolution.