Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis

We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, “like (positive)” and “dislike (negative)”, according to the preference score given b...

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Bibliographic Details
Published in:	PloS one 2022-11, Vol.17 (11), p.e0274203-e0274203
Main Authors:	Kim, Jieun, Hwang, Dong-Uk, Son, Edwin J, Oh, Sang Hoon, Kim, Whansun, Kim, Youngkyung, Kwon, Gusang
Format:	Article
Language:	eng
Subjects:	Accuracy Analysis Biology and Life Sciences Brain research Classification Computer and Information Sciences Consumer behavior Cosmetics Deep learning Electroencephalography Emotion recognition Emotional intelligence Emotions Feature extraction Frequencies Frequency dependence Influence Machine learning Medicine and Health Sciences Methods Multimedia Musical performances Research and Analysis Methods Social Sciences Spatial data Wavelet transforms
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Summary:	We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, “like (positive)” and “dislike (negative)”, according to the preference score given by the subject. We extracted frequency features using well-known frequency bands, i.e., alpha, beta and low and high gamma bands, and then we created a matrix including frequency and spatial information of the EEG data. We developed seven CNN-based models: (1) inception-like CNN with four-band merged input, (2) stacked CNN with four-band merged input, (3) stacked CNN with four-band parallel input, and stacked CNN with single-band input of (4) alpha, (5) beta, (6) low gamma, and (7) high gamma. The models were evaluated by the Leave-One-Subject-Out Cross-Validation method. In like/dislike two-class classification, the average accuracies of all subjects were 73.2%, 75.4%, 73.9%, 68.8%, 68.0%, 70.7%, and 69.7%, respectively. We found that the classification performance is higher when using multi-band features than when using single-band feature. This is the first study to apply a CNN-based deep learning method based on EEG data to evaluate preference for cosmetic creams.
ISSN:	1932-6203 1932-6203