Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity

TL;DR

This study demonstrates that using expectation and acoustic neural network representations as teacher signals enhances EEG-based music identification, advancing neural decoding and predictive music cognition.

Contribution

It introduces a novel approach of guiding neural network training with expectation-related representations derived from raw signals, improving EEG recognition performance.

Findings

Pretrained models outperform non-pretrained baselines.

Combining acoustic and expectation representations yields superior results.

Expectation representations reflect predictive structure beyond basic features.

Abstract

During music listening, cortical activity encodes both acoustic and expectation-related information. Prior work has shown that ANN representations resemble cortical representations and can serve as supervisory signals for EEG recognition. Here we show that distinguishing acoustic and expectation-related ANN representations as teacher targets improves EEG-based music identification. Models pretrained to predict either representation outperform non-pretrained baselines, and combining them yields complementary gains that exceed strong seed ensembles formed by varying random initializations. These findings show that teacher representation type shapes downstream performance and that representation learning can be guided by neural encoding. This work points toward advances in predictive music cognition and neural decoding. Our expectation representation, computed directly from raw signals without manual labels, reflects predictive structure beyond onset or pitch, enabling investigation of multilayer predictive encoding across diverse stimuli. Its scalability to large, diverse datasets further suggests potential for developing general-purpose EEG models grounded in cortical encoding principles.