Odor-induced emotion recognition based on average frequency band division of EEG signals

Hou, Huayan; Zhang, Xiao-Nei; Meng, Qing-Hao

doi:10.1016/j.jneumeth.2020.108599

Cited by 50 publications

(24 citation statements)

References 40 publications

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“…Relevant studies have proved that different frequencies of brain electricity reflect different brain states (Hou et al, 2020), in which alpha, theta, and gamma waves show stronger relevance with R-state. Cahn and Delorme found that the long-term training of Vipassana meditation could increase gamma power (Baruch et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes

Zhang

Hua

et al. 2021

Front. Neurosci.

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Increasing social pressure enhances the psychological burden on individuals, and the severity of depression can no longer be ignored. The characteristics of high immersion and interactivity enhance virtual reality (VR) application in psychological therapy. Many studies have verified the effectiveness of VR relaxation therapy, although a few have performed a quantitative study on relaxation state (R-state). To confirm the effectiveness of VR relaxation and quantitatively assess relaxation, this study confirmed the effectiveness of the VR sightseeing relaxation scenes using subjective emotion scale and objective electroencephalogram (EEG) data from college students. Moreover, some EEG features with significant consistent differences after they watched the VR scenes were detected including the energy ratio of the alpha wave, gamma wave, and differential asymmetry. An R-state regression model was then built using the model stacking method for optimization, of which random forest regression, AdaBoost, gradient boosting (GB), and light GB were adopted as the first level, while linear regression and support vector machine were applied at the second level. The leave-one-subject-out method for cross-validation was used to evaluate the results, where the mean accuracy of the framework achieved 81.46%. The significantly changed features and the R-state model with over 80% accuracy have laid a foundation for further research on relaxation interaction systems. Moreover, the VR relaxation therapy was applied to the clinical treatment of patients with depression and achieved preliminary good results, which might provide a possible method for non-drug treatment of patients with depression.

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Section: Introductionmentioning

confidence: 99%

Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes

Zhang

Hua

et al. 2021

Front. Neurosci.

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“…And Li et al (2019) conducted an experiment based on emotional face stimuli and recorded 28 subjects’ EEG data from 128-channel HydroCel Geodesic Sensor Net by Net Station software. In Hou et al (2020) , the Cerebus system (Blackrock Microsystems, United States) was used to collect EEG data at a 1 kHz sampling rate using a 32-channel EEG cap. In the same year, Maeng et al (2020) introduced a new multimodal dataset via Biopac’s M150 equipment called MERTI-Apps based on Asian physiological signals.…”

Section: Acquisition Of Electroencephalography Signals For Emotion Recognitionmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

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“…Each part corresponds to a filter group, and each filter group corresponds to a center frequency and bandwidth. The bandwidth is about 100 Mel [40,41].…”

Section: Features Extraction For Acoustic Signalsmentioning

confidence: 99%

Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks

Kuang

Wang

et al. 2020

Applied Soft Computing

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Facial expressions, verbal, behavioral, such as limb movements, and physiological features are vital ways for affective human interactions. Researchers have given machines the ability to recognize affective communication through the above modalities in the past decades. In addition to facial expressions, changes in the level of sound, strength, weakness, and turbulence will also convey affective. Extracting affective feature parameters from the acoustic signals have been widely applied in customer service, education, and the medical field. In this research, an improved AlexNet-based deep convolutional neural network (A-DCNN) is presented for acoustic signal recognition. Firstly, preprocessed on signals using simplified inverse filter tracking (SIFT) and short-time Fourier transform (STFT), Mel frequency Cepstrum (MFCC) and waveformbased segmentation were deployed to create the input for the deep neural network (DNN), which was applied widely in signals preprocess for most neural networks. Secondly, acoustic signals were acquired from the public Ryerson Audio-Visual Database of Affective Speech and Song (RAVDESS) affective speech audio system. Through the acoustic signal preprocessing tools, the basic features of the kind of sound signals were calculated and extracted. The proposed DNN based on improved AlexNet has a 95.88% accuracy on classifying eight affective of acoustic signals. By comparing with some linear classifications, such as decision table (DT) and Bayesian inference (BI) and other deep neural networks, such as AlexNet+SVM, recurrent convolutional neural network (R-CNN), etc., the proposed method achieves high effectiveness on the accuracy (A), sensitivity (S1), positive predictive (PP), and f1-score (F1). Acoustic signals affective recognition and classification can be potentially applied in industrial product design through measuring consumers' affective responses to products; by collecting relevant affective sound data to understand the popularity of the product, and furthermore, to improve the product design and increase the market responsiveness.

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Odor-induced emotion recognition based on average frequency band division of EEG signals

Cited by 50 publications

References 40 publications

Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes

Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks

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