Emotion Detection Using Physiological Signals

Dessai, Amita; Virani, H. G.

doi:10.1109/icecet52533.2021.9698729

Cited by 6 publications

(20 citation statements)

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“…The database contains recordings of 40 participants while they watched 16 short videos [18]. However, we only used the ECG and GSR signal recordings of participants while watching the videos numbered 1, 6, 8, and 12 in our work [18,19]. These short videos are less than 1.…”

Section: Databasementioning

confidence: 99%

“…Additionally, relevant information from the signal is extracted at this stage The variations in the intervals of the ECG signal can help classify emotions. For instance the skin conductance of GSR varies as per Arousal, with increased peaks indicating high Arousal [19]. The steps followed to carry out the preprocessing of ECG and GSR signal are explained further below.…”

Section: Preprocessingmentioning

confidence: 99%

“…The ECG waveform has a baseline that indicates no overall depolarization or repolar ization. The atrial depolarization is represented by the P wave, which lasts for 80-100 ms The ventricular depolarization is indicated by the QRS complex, which lasts for 80-12 ms [19]. The ventricular repolarization is specified by the T wave and lasts for 200 ms [14 19].…”

Section: Scenario 1: Ecg Signal Preprocessingmentioning

confidence: 99%

“…A low-pass Butterworth filter of 15 HZ is used to reduce electrical noise and muscle artifacts. In addition, Butter worth's high-pass filter with a cut-off frequency of 0.5 Hz is employed to minimize motion artifacts in the ECG signals [19].…”

Section: Scenario 1: Ecg Signal Preprocessingmentioning

confidence: 99%

“…However, raw ECG and GSR signals can be noisy and require suitable preprocessing techniques. Time and frequency domain features must be extracted from ECG and GSR signal recordings to obtain relevant information about different emotions [19]. Further, relevant features must be selected using various feature selection techniques before classification.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal and Multidomain Feature Fusion for Emotion Classification Based on Electrocardiogram and Galvanic Skin Response Signals

Dessai,

Virani

2024

Sci

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Emotion classification using physiological signals is a promising approach that is likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and the Galvanic Skin Response (GSR) are more reliable than facial and voice recognition signals because they are not influenced by the participant’s subjective perception. However, the precision of emotion classification with ECG and GSR signals is not satisfactory, and new methods need to be developed to improve it. In addition, the fusion of the time and frequency features of ECG and GSR signals should be explored to increase classification accuracy. Therefore, we propose a novel technique for emotion classification that exploits the early fusion of ECG and GSR features extracted from data in the AMIGOS database. To validate the performance of the model, we used various machine learning classifiers, such as Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives the highest accuracy for Valence and Arousal, with 69% and 70% for ECG and 96% and 94% for GSR, respectively. The mutual information technique of feature selection and KNN for classification outperformed the performance of other classifiers. Interestingly, the classification accuracy for the GSR was higher than for the ECG, indicating that the GSR is the preferred modality for emotion detection. Moreover, the fusion of features significantly enhances the accuracy of classification in comparison to the ECG. Overall, our findings demonstrate that the proposed model based on the multiple modalities is suitable for classifying emotions.

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Section: Databasementioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Scenario 1: Ecg Signal Preprocessingmentioning

confidence: 99%

Section: Scenario 1: Ecg Signal Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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