Spectra-based Classification of Audiovisual and Visuospatial Face-name Associative Memories using EEG

In social interactions, we remember faces and associate names that we hear to the faces. Existing research has studied single-item memory tasks such as names, face, colors, shapes, and classified their associations using spectral analysis, event-related potentials(ERPs), and non-ensemble machine learning techniques. However, the impact of multiple stimuli presentation modalities in the associative memory of face-name stimuli has not been extensively investigated. In this research, we conducted two experiments on audiovisual face-name and visuospatial face-name memory tasks. We recorded EEG data from 15 healthy participants and extracted the average oscillatory band power of the alpha, theta, beta, and gamma bands and the signal entropy. Adaptive Logistic boosting was applied for classification, and t-test statistical analyses were applied to investigate participants’ behavioral performance. We found that people did better on visuospatial face-name memory association than audiovisual face-name memory association. Adaptive boosting performed best in comparison to other existing algorithms with the average classification accuracy of 76.37% at the parietal electrode P
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