Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Siirtola, Pekka; Tamminen, Satu; Chandra, Gunjan; Ihalapathirana, Anusha; Röning, Juha

doi:10.3390/s23031598

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…The prediction performances of the RF and LSTM models were similar. These results deviate from those of previous studies that indicated better predictive capabilities of LSTM than the RF on time-series data [ 31 ]. However, these results agree with the findings of other investigations that reported comparable predictive performance between these two ML models [ 32 , 33 ].…”

Section: Discussioncontrasting

confidence: 99%

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

Kawamura,

Sato,

Shimokawa

et al. 2024

Sensors

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Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective–physiological association.

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Section: Discussioncontrasting

confidence: 99%

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

Kawamura,

Sato,

Shimokawa

et al. 2024

Sensors

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“…Long short-term memory (LSTM) [ 22 , 23 ] was utilized to identify muscle fatigue state. The number of layers of the LSTM model is set as input layer, hidden layer, and output layer through the query of relevant research literature, and the unit is 100.…”

Section: Methodsmentioning

confidence: 99%

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

Wang

2023

PLoS ONE

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Objective This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI). Methods 20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured. Results 1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05). Conclusion Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.

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“…At a glance, Valence and Arousal classifications must be performed through two independent binary classification tasks. By combining Valence and Arousal, human emotions (e.g., Angry, Happy, Sad) can be expressed; often, these are visualized using Russell’s circumplex model of emotions 54 .…”

Section: Eeg-based Emotion Recognition Through Information Enhancemen...mentioning

confidence: 99%

Improved EEG-based emotion recognition through information enhancement in connectivity feature map

Akhand,

Maria,

Kamal

et al. 2023

Sci Rep

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Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs’ measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods.

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Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Cited by 9 publications

References 40 publications

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

Improved EEG-based emotion recognition through information enhancement in connectivity feature map

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