Abstract
The study of the electroencephalogram signals recorded from subjects during an experience is a way to understand the brain processes that underlie their physical and emotional involvement. Such signals have the form of time series, and their analysis could benefit from applying techniques that are specific to this kind of data. Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments, such as liking or disliking a painting. Starting from a proprietary dataset of 248 trials from 16 subjects exposed to art paintings, using a real ecological context, this paper analyses the application of a novel symbolic machine learning technique, specifically designed to extract information from unstructured data and to express it in form of logical rules. Our purpose is to extract qualitative and quantitative logical rules, to relate the voltage at specific frequencies and in specific electrodes, and that, within the limits of the experiment, may help to understand the brain process that drives liking or disliking experiences in human subjects.
Publisher
Public Library of Science (PLoS)
Reference53 articles.
1. Neuroaesthetics;A Chatterjee;Trends in Cognitive Sciences,2014
2. What is an unconscious emotion? (The case for unconscious “liking”);K Berridge;Cognition and Emotion,2003
3. Review on Emotion Recognition Based on Electroencephalography;H Liu;Frontiers on Computational Neuroscience,2021
4. Recognition of human emotions using EEG signals: A review;MM Rahman;Computers in Biology and Medicine,2021
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献