<ul><li>Recent studies have shown that artificial neural network (ANN) representations can resemble cortical representations when exposed to the same auditory inputs.</li><li>This study proposes a new approach by using ANN representations as a supervisory signal to train recognition models for music identification using non-invasive brain recordings.</li><li>By training an EEG recognition model to predict ANN representations associated with music identification, significant improvement in classification accuracy is observed.</li><li>This research has potential implications for advancing brain-computer interfaces, neural decoding techniques, and our understanding of music cognition.</li></ul>

Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings

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