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Related Experiment Video

Updated: May 8, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Predicting musically induced emotions from physiological inputs: linear and neural network models.

Frank A Russo¹, Naresh N Vempala, Gillian M Sandstrom

¹SMART Lab, Department of Psychology, Ryerson University Toronto, ON, Canada ; Communication Team, Toronto Rehabilitation Institute Toronto, ON, Canada.

Frontiers in Psychology

|August 22, 2013

Summary

Physiological responses during music listening can predict listener emotions. Advanced neural networks accurately decode "felt" emotion from heart rate and other signals, outperforming linear models, especially for valence.

Keywords:

computational modeling emotion music cognition neural networks physiological responses

Related Experiment Videos

Last Updated: May 8, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

Psychology
Neuroscience
Musicology

Background:

Music listening elicits measurable physiological responses.
The degree to which these responses reflect subjective emotional experience is not fully understood.

Purpose of the Study:

To determine if physiological responses alone can predict "felt" emotion during music listening.
To compare the efficacy of linear and non-linear models in this prediction task.

Main Methods:

Measured five peripheral physiological signals: heart rate, respiration, galvanic skin response, and facial muscle activity (corrugator supercilii, zygomaticus major).
Participants rated their felt emotion using valence and arousal dimensions after listening to 12 classical music excerpts.
Employed multiple linear regression and a neural network model to predict emotion ratings from physiological data.

Main Results:

Linear models significantly predicted arousal but not valence.
A neural network model demonstrated that physiological responses alone can predict musically induced emotion, particularly valence.
Non-linear modeling via neural networks proved more accurate than linear regression.

Conclusions:

Peripheral physiological responses contain sufficient information to infer musically induced emotions.
Non-linear relationships are crucial for accurately predicting emotional valence from physiological data.
This study offers a novel method for understanding the link between psychophysiology and music-induced affect.