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Abstract
Evaluation of aesthetic design fulfills a pivotal function in product development, which urges for an efficacious objective method to measure customers’ experience. The stability and effectiveness of electroencephalography (EEG) make it a suitable tool for aesthetic experience measurement. Nevertheless, existing studies have several limitations, especially regarding the stimuli and the algorithm. The potential of an EEG-based deep learning model has not been verified in pinpointing subtle differences in physical product aesthetics. To fill the research gap in this issue, we recorded EEG signals in real-life scenarios when participants were presented with different types of physical smartphones, and asked participants to rate them from four dimensions of aesthetic experience (arousal, valence, likeness, and aesthetic evaluation). Then, the time–frequency data were fed into a spatial feature extraction network and an attention-based bidirectional long short-term memory (BiLSTM) optimized by the cross-entropy loss function. The result showed that at 16s window size, the four outcome models yielded the best joint recognition performance of aesthetic experience with an average accuracy of over 85% (arousal: 88.10%, valence: 87.97%, likeness: 85.99%, and aesthetic evaluation: 87.23%). It provides an objective cross-subject recognition method with multi-faceted evaluation results of aesthetic experience. Additionally, we verified the ability of EEG as a reliable and informative resource in terms of aesthetic experience evaluation, even with subtle differences. More practically, a future direction of incorporating EEG signals into subjective product aesthetics measurement could be given more credit.
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No potential conflict of interest was reported by the authors.
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Notes on contributors
Peishan Wang
Peishan Wang PhD student, Human Factors & Ergonomics Lab, Institute of Psychology, Chinese Academy of Sciences, Beijing, China. Her interests focus on human-machine interaction and stress. She utilizes multimodal data acquisition to capture variations in different emotional states and cognitive processes to develop principles that can guide the design of effective interfaces.
Haibei Feng
Haibei Feng, Master student, Beijing Key Laboratory of Human-Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, China. Her current research focuses on studying the neural mechanisms of trust in automated driving using electroencephalography (EEG) and EEG decoding algorithms related to emotion recognition.
Xiaobing Du
Xiaobing Du PhD student, State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China. Her research interests include affective computing and human-computer interaction.
Rui Nie
Rui Nie PhD student, School of Public Health, University of Michigan, Ann Arbor, MI, United States; research intern, Institute of Psychology, Chinese Academy of Sciences, Beijing, China. Her current research focus on clinical replicable design and statistical problems in wearable device data, with a broad interest in individualized healthcare.
Yudi Lin
Yudi Lin Master student, Department of Computer Science, University of Southern California; research intern, Beijing Key Laboratory of Human-Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, China. He current studies EEG decoding algorithms related to emotion recognition and Computer vision related to 3D reconstruction.
Cuixia Ma
Cuixia Ma Professor, Institute of Software, Chinese Academy of Sciences, Beijing, China. Her research interests include sketch interaction, multimodal interaction and cognitive computation. Focusing on natural user interfaces, she has made some breakthroughs in cognitive analysis of sketch presentation and interaction.
Liang Zhang
Liang Zhang Associate Professor, Institute of Psychology, Chinese Academy of Sciences, Beijing, China. She focuses on psychological evaluation and HCI design, grounded in the principles of human information processing. She adopts a comprehensive methodology to assess variations in stress, emotional and cognitive states, and utilized the findings to improve HCI.