Movie preference prediction based on EEG signal under high brain correlation

Yuanhao Yue; Xingcong Zhao; Wei Zhou; Guangyuan Liu

doi:10.1117/12.3012323

21 December 2023 Movie preference prediction based on EEG signal under high brain correlation

Yuanhao Yue, Xingcong Zhao, Wei Zhou, Guangyuan Liu

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701D (2023) https://doi.org/10.1117/12.3012323
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In this study, we recorded the electroencephalography (EEG) of human subjects when they watched a movie trailer. Based on the willingness preference from the subjects after finished the movie trailer watching, the EEG signals were analyzed to build regression models to predict the subjects' movie preferences. We proposed a new approach to rise the prediction accuracy. Specifically, the correlated component analysis (CCA) algorithm was used to calculate the inter-subject correlation (ISC) of each time segment in the movie trailer and the ISC of each channel in the EEG. These two states were selected to locate the moment when a specific area of the brain responds most strongly to the video stimulation, as an effective indicator for predicting individual movie preferences. The experimental results show that the method of selecting effective EEG signals at the level of ISC has achieved a better prediction effect on the GBDT regression model (R2 = 0.733, RMSE = 0.681). We also found that low-frequency EEG signals in the θ and δ bands of the parietal and occipital sites are more effective in predicting movie preferences than high-frequency EEG signals.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanhao Yue, Xingcong Zhao, Wei Zhou, and Guangyuan Liu "Movie preference prediction based on EEG signal under high brain correlation", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701D (21 December 2023); https://doi.org/10.1117/12.3012323

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