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Classifying EEG Recordings of Rhythm Perception

Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. In this paper, we present first classification results using deep learning techniques on EEG data recorded within a rhythm perception study in Kigali, Rwanda. We tested 13 adults, mean age 21, who performed three behavioral tasks using rhythmic tone sequences derived from either East African or Western music. For the EEG testing, 24 rhythms ‐ half East African and half Western with identical tempo and based on a 2-bar 12/8 scheme ‐ were each repeated for 32 seconds. During presentation, the participants’ brain waves were recorded via 14 EEG channels. We applied stacked denoising autoencoders and convolutional neural networks on the collected data to distinguish African and Western rhythms on a group and individual participant level. Furthermore, we investigated how far these techniques can be used to recognize the individual rhythms.