Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy

Li, Wei; Zhang, Zhen; Song, Aiguo

doi:10.1016/j.measurement.2020.108747

Cited by 61 publications

(21 citation statements)

References 103 publications

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“…We selected electrodermal activity (EDA), heart rate (HR), and skin temperature (SKT) as representative of the activation of the autonomic nervous system (Hui and Sherratt, 2018). These physiological signals and the related features have been extensively used in the literature in the field of emotion recognition (Egger et al, 2019;Li et al, 2021). Specifically, they have been used in association with stress and pain (Campbell et al, 2019;Chen et al, 2021;Chesnut et al, 2021) and to evaluate depressive and anxious symptoms (Sarchiapone et al, 2018;Chesnut et al, 2021).…”

Section: Physiological Parametersmentioning

confidence: 99%

Virtual Reality in Home Palliative Care: Brief Report on the Effect on Cancer-Related Symptomatology

et al. 2021

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Virtual reality (VR) has been used as a complementary therapy for managing psychological and physical symptoms in cancer patients. In palliative care, the evidence about the use of VR is still inadequate. This study aims to assess the effect of an immersive VR-based intervention conducted at home on anxiety, depression, and pain over 4days and to evaluate the short-term effect of VR sessions on cancer-related symptomatology. Participants were advanced cancer patients assisted at home who were provided with a VR headset for 4days. On days one and four, anxiety and depression were measured by the Hospital Anxiety and Depression Scale (HADS) and pain by the Brief Pain Inventory (BPI). Before and after each VR session, symptoms were collected by the Edmonton Symptom Assessment Scale (ESAS). Participants wore a smart wristband measuring physiological signals associated with pain, anxiety, and depression. Fourteen patients (mean age 47.2±14.2years) were recruited. Anxiety, depression (HADS), and pain (BPI) did not change significantly between days one and four. However, the ESAS items related to pain, depression, anxiety, well-being, and shortness of breath collected immediately after the VR sessions showed a significant improvement (p<0.01). A progressive reduction in electrodermal activity has been observed comparing the recordings before, during, and after the VR sessions, although these changes were not statistically significant. This brief research report supports the idea that VR could represent a suitable complementary tool for psychological treatment in advanced cancer patients assisted at home.

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Section: Physiological Parametersmentioning

confidence: 99%

Virtual Reality in Home Palliative Care: Brief Report on the Effect on Cancer-Related Symptomatology

et al. 2021

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“…It is noteworthy that SVM, k-NN, LR, ANN, and NB belong to the classifier type of machine learning algorithms, whereas RF and GBT belong to the ensemble type of machine learning algorithms. Whether classifier (7) or ensemble is suitable for physiological signal-based emotion recognition is contentious. While most of the previous studies claimed that their proposed method was superior compared with other competitive methods, drawbacks of some studies included the aforementioned small sample sizes and lack of proper cross-validations.…”

Section: Discussionmentioning

confidence: 99%

“…However, the previous studies applied only a limited number of machine learning algorithms among many widely used algorithms. For example, Li et al (7) provided a comprehensive overview of physiological signal-based emotion recognition. In this study, the authors enumerated various studies on physiological signal types and various machine learning algorithms for emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

Ono

Warita

et al. 2022

Front. Psychiatry

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In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including “happy,” as a positive emotion and “anxiety,” “sad,” “frustrated,” as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

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“…Affective computing focuses on how to actively learn, reason, and perceive the surrounding world, as well as realize a certain level of brain-inspired cognitive intelligence by simulating people’s psychological cognitive processes ( Aranha et al, 2019 ; Samsonovich, 2020 ). Researchers in psychology and neurobiology investigate the changes and relationships in the human physiological systems that occur during various emotional states and activities ( Li et al, 2020 ). More and more evidences show that with the progress of neuroscience research, there is a connection between human emotional activity and the activity of specific areas of the brain, especially the cerebral cortex and central nervous system.…”

Section: Introductionmentioning

confidence: 99%

Multi-Frequent Band Collaborative EEG Emotion Classification Method Based on Optimal Projection and Shared Dictionary Learning

Zhu

Shen

2022

Front. Aging Neurosci.

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Affective computing is concerned with simulating people’s psychological cognitive processes, of which emotion classification is an important part. Electroencephalogram (EEG), as an electrophysiological indicator capable of recording brain activity, is portable and non-invasive. It has emerged as an essential measurement method in the study of emotion classification. EEG signals are typically split into different frequency bands based on rhythmic characteristics. Most of machine learning methods combine multiple frequency band features into a single feature vector. This strategy is incapable of utilizing the complementary and consistent information of each frequency band effectively. It does not always achieve the satisfactory results. To obtain the sparse and consistent representation of the multi-frequency band EEG signals for emotion classification, this paper propose a multi-frequent band collaborative classification method based on optimal projection and shared dictionary learning (called MBCC). The joint learning model of dictionary learning and subspace learning is introduced in this method. MBCC maps multi-frequent band data into the subspaces of the same dimension using projection matrices, which are composed of a common shared component and a band-specific component. This projection method can not only make full use of the relevant information across multiple frequency bands, but it can also maintain consistency across each frequency band. Based on dictionary learning, the subspace learns the correlation between frequency bands using Fisher criterion and principal component analysis (PCA)-like regularization term, resulting in a strong discriminative model. The objective function of MBCC is solved by an iterative optimization algorithm. Experiment results on public datasets SEED and DEAP verify the effectiveness of the proposed method.

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Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy

Cited by 61 publications

References 103 publications

Virtual Reality in Home Palliative Care: Brief Report on the Effect on Cancer-Related Symptomatology

Virtual Reality in Home Palliative Care: Brief Report on the Effect on Cancer-Related Symptomatology

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

Multi-Frequent Band Collaborative EEG Emotion Classification Method Based on Optimal Projection and Shared Dictionary Learning

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