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Classifying Songs with EEG
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This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired by the notion of musical processing as resonance, we hypothesize that the intensity of an aesthetic experience is based on the degree to which a participants EEG entrains to the perceptual input. To test this and other hypotheses, we have built an EEG dataset from 20 subjects listening to 12 two minute-long songs in random order. After preprocessing and feature construction, we used this dataset to train and test multiple machine learning models.
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Cited by 1 Pith paper
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Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings
Training EEG encoders with an auxiliary InfoNCE loss that predicts a co-trained music encoder's representation improves 10-song EEG identification accuracy on the NMED-T dataset.
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