Emotion Recognition from Physiological Signals Using Continuous Wavelet Transform and Deep Learning

Jalal, Lana; Peer, Angelika

doi:10.1007/978-3-031-17618-0_8

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…Also, the proposed method outperforms the recently introduced methods. [37] EEG + EOG 0.866 (two classes) Hatipoglu Yilmaz and Kose [38] EEG + EOG 0.915 (two classes) Ma et al [39] EEG + PPS 0.923 (two classes) Qiu et al [40] EEG + PPS 0.856 (two classes) Li et al [41] EEG + PPS 0.949 (two classes) Zhang et al [7] EEG + PPS 0.847 (two classes) Zhang et al [8] EEG + PPS 0.901 (two classes) Jalal and Peer [12] PPS 0.842 (four classes) Cimtay et al [19] EEG, GSR, facial 0.915 (four classes)…”

Section: Discussionmentioning

confidence: 99%

“…Te multiple-fusion-layer-based ensemble classifer of stacked autoencoder was proposed in [11] to recognize the emotions from the DEAP dataset. PPSs such as galvanic skin response (GSR), respiration patterns, and blood volume pressure were utilized in [12]. Tis method combines some continuous wavelet transforms (CWTs) and classifes them using a CNN.…”

Section: Related Workmentioning

confidence: 99%

“…It is observed that DEAP is the most used dataset. Also, most works focusing on time-domain and timefrequency analyses were adopted in [8,12]. Te feature concatenation was considered after feature extraction from each modal, and cross-modal correlation was not considered in the feature extraction process.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Semisupervised Deep Features of Time‐Frequency Maps for Multimodal Emotion Recognition

Zali-Vargahan,

Charmin,

Kalbkhani

et al. 2023

International Journal of Intelligent Systems

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Traditional approaches for emotion recognition utilize unimodal physiological signals. The effectiveness of such systems is affected by some limitations. To overcome them, this paper proposes a new method based on time-frequency maps that extract the features from multimodal biological signals. At first, the fusion of electroencephalogram (EEG) and peripheral physiological signal (PPS) is performed, and then, the two-dimensional discrete orthonormal Stockwell transform (2D-DOST) of the multimodal signal matrix is calculated to obtain time-frequency maps. A convolutional neural network (CNN) is then utilized to extract the local deep features from the absolute output of the 2D-DOST. Since there are uninformative deep features, the semisupervised dimension reduction scheme reduces them by balancing the generalization and discrimination. Finally, the classifier recognizes the emotion. The Bayesian optimizer finds the proper SSDR and classifier parameter values to maximize the recognition accuracy. The performance of the proposed method is evaluated on the DEAP dataset considering the two- and four-class scenarios through extensive simulations. This dataset consists of electroencephalograph (EEG) signals in 32 channels and peripheral physiological signals (PPSs) in eight channels from 32 subjects. The proposed method reaches the accuracy of 0.953 and 0.928 for two- and four-class scenarios, respectively. The results indicate the efficiency of the multimodal signals for detecting emotions compared to that of unimodal signals. Also, the results indicate that the proposed method outperforms the recently introduced ones.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Semisupervised Deep Features of Time‐Frequency Maps for Multimodal Emotion Recognition

Zali-Vargahan,

Charmin,

Kalbkhani

et al. 2023

International Journal of Intelligent Systems

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Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform

2023

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Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthcare, sports, and human activity monitoring. Convolutional neural networks (CNN) are becoming a popular approach for addressing HAR problems. However, this method requires extensive training datasets to perform adequately on new data. This paper proposes a novel deep learning model pre-trained on scalograms generated using the continuous wavelet transform (CWT). Nine popular CNN architectures and different CWT configurations were considered to select the best performing combination, resulting in the training and evaluation of more than 300 deep learning models. On the source KU-HAR dataset, the selected model achieved classification accuracy and an F1 score of 97.48% and 97.52%, respectively, which outperformed contemporary state-of-the-art works where this dataset was employed. On the target UCI-HAPT dataset, the proposed model resulted in a maximum accuracy and F1-score increase of 0.21% and 0.33%, respectively, on the whole UCI-HAPT dataset and of 2.82% and 2.89%, respectively, on the UCI-HAPT subset. It was concluded that the usage of the proposed model, particularly with frozen layers, results in improved performance, faster training, and smoother gradient descent on small HAR datasets. However, the use of the pre-trained model on sufficiently large datasets may lead to negative transfer and accuracy degradation.

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Emotion Recognition from Physiological Signals Using Continuous Wavelet Transform and Deep Learning

Cited by 2 publications

References 28 publications

Semisupervised Deep Features of Time‐Frequency Maps for Multimodal Emotion Recognition

Semisupervised Deep Features of Time‐Frequency Maps for Multimodal Emotion Recognition

Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform

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