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2nd IEEE Advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Wu, Shifu Liu, Guangyuan Li, Qiang Yuan, Guangjie Southwest University College of Electronic and Information Engineering Chongqing China Institute of Affective Computing and Information Processing Southwest University Chongqing China Chongqing Brain Science Collaborative Innovation Center Chongqing China

ISBN: (纸本)9781538618035

T415 article distinguished the different frequencies of sound by the feature of electroencephalogram (EEG). EEGs were evoked by low-frequency (200 Hz) and high-frequency (6400 Hz) pure-tone. The optimal feature set was selected using a sequential backward selection (SBS) algorithm from a primary feature set. The best feature had average amplitude, power spectrum and low-frequency energy of wavelet packet 4 decompose. Statistic test shows that power spectrum and low-frequency energy of wavelet packet 4 decompose of the ERPs with 200 Hz were significantly larger than those with 6400 Hz. The back-propagation artificial neural network (BPANN) algorithm and support vector machine (SVM) were used for binary classification. SVM is better, and the correct recognition rate of the 200 Hz and 6400 Hz are 79.17% and 70.83%. The experimental results has potential value for the brain-computer interface (BCI) and the tone audiometry. © 2018 IEEE.

关键词： Electroencephalography

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Recognition of Sound Frequency Based on EEG

Recognition of Sound Frequency Based on EEG

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IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Shifu Wu Guangyuan Liu Qiang Li Guangjie Yuan Institute of Affective Computing and Information Processing Southwest University Chongqing China

This article distinguished the different frequencies of sound by the feature of electroencephalogram (EEG). EEGs were evoked by low-frequency (200 Hz) and high-frequency (6400 Hz) pure-tone. The optimal feature set was selected using a sequential backward selection (SBS) algorithm from a primary feature set. The best feature had average amplitude, power spectrum and low-frequency energy of wavelet packet 4 decompose. Statistic test shows that power spectrum and low-frequency energy of wavelet packet 4 decompose of the ERPs with 200 Hz were significantly larger than those with 6400 Hz. The back-propagation artificial neural network (BPANN) algorithm and support vector machine (SVM) were used for binary classification. SVM is better, and the correct recognition rate of the 200 Hz and 6400 Hz are 79.17% and 70.83%. The experimental results has potential value for the brain-computer interface (BCI) and the tone audiometry.

关键词： Electroencephalography Feature extraction Wavelet packets Support vector machines Electrodes Classification algorithms Task analysis

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Movie preference prediction based on EEG signal under high brain correlation 4

Movie preference prediction based on EEG signal under high b...

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4th International Conference on Signal processing and Computer Science, SPCS 2023

作者： Yue, Yuanhao Zhao, Xingcong Zhou, Wei Liu, Guangyuan School of Electronic and Information Engineering Southwest University Tiansheng Road Beibei Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Tiansheng Road Beibei Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Tiansheng Road Beibei Chongqing400715 China

ISBN: (纸本)9781510672505

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 SPIE.

关键词： Electroencephalography

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

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3rd International Conference on Bioinformatics and Intelligent computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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Emotion Recognition of Virtual Reality Scenes Under Olfactory Stimulation: An ECG Study

SSRN

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SSRN 2023年

作者： Liu, Wanqing Zhong, Xinyue Feng, Weibin Xie, Jialan Luo, Yutong Guangyuan, Liu College of Electronic and Information Engineering Southwest University Chongqing400715 China Institute of Affective Computing and Intelligent Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

Virtual Reality (VR) technology enhanced by olfactory stimulation has introduced novel dimensions to human-computer interaction (HCI). While there have been studies utilizing electrocardiogram (ECG) signals to explore the impact of olfaction on the emotional experiences of traditional multimedia users, little research has delved into the influence of heightened olfactory stimulation on emotion recognition within virtual reality scenarios. To explore this issue in depth, we first initiated an olfactory stimulation experiment within a VR environment, collecting ECG data from 65 participants as they interacted with VR scenes and olfactory-enhanced virtual reality (OVR) scenes, each characterized by varying emotional valence and levels of emotional arousal. Subsequently, we extracted time-domain, frequency-domain, and nonlinear features from the ECG data. Finally, we employed three widely used machine learning classification models, namely, light gradient boosting machine, SVM, and adaptive enhancement algorithm, to discern the emotional valence and arousal levels in OVR and VR states, respectively. The results indicate that the classification recognition rates of all three classifiers are significantly higher (> 4%) within the OVR environment than in the VR context, both in terms of valence and arousal dimensions. The results are consistent with the analysis and comparison of behavioral data. This finding underscores the capacity of ECG data to effectively capture distinctions in emotional responses between OVR and VR experiences and highlights the contributory role of olfactory enhancement in VR-based emotion recognition. Our discoveries will contribute to the advancement of OVR emotion recognition research and offer fresh insights for enhancing the overall HCI emotional experience. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

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Comparative Analysis of Personality Recognition in Response to Virtual Reality and Two-Dimensional Emotional Stimulus Using ECG Signals

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ACM Transactions on Interactive Intelligent Systems 1000年

作者： Jialan Xie Ping Lan Zhaonian Hu Guangyuan Liu College of Electronic and Information Engineering Southwest University Chongqing China Institute of Affective Computing and Information Processing Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing China

Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have used two-dimensional (2D) emotional stimulus materials. Virtual reality (VR) has been utilized in many fields, and its superiority over 2D in emotion recognition has been proven. However, relevant research on VR scenes is lacking in the field of personality recognition. In this study, based on the psychological principle that emotional arousal can expose an individual’s personality, we attempt to explore the feasibility and effect of using electrocardiogram (ECG) signals in response to VR emotional stimuli for personality identification. For this purpose, a VR-2D emotion-induction experiment was conducted in which ECG signals were collected, and physiological datasets of emotional personalities were constructed through preprocessing and feature extraction. Statistical analysis of the emotion scale scores and ECG features of the participants showed that the VR group had a higher number of significantly correlated features. Meanwhile, VR- and 2D-based personality recognition models were constructed using machine learning algorithms. The results showed that the VR-based personality recognition model achieved better results for the four personality dimensions, with a maximum accuracy of 79.76%. These findings indicate that VR not only enhances the physiological correlation between emotion and personality but also improves the classification accuracy of personality recognition.

关键词： Big Five personality Electrocardiogram Emotion Personality recognition Virtual reality

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