Emotion Recognition with Short-Period Physiological Signals Using Bimodal Sparse Autoencoders

Lee, Yun-Kyu; Pae, Dong Sung; Hong, Dae-Ki; Lim, Myo–Taeg; Kang, Tae-Koo

doi:10.32604/iasc.2022.020849

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Therefore, we compared the performance of CNNs and models that combined a stacked autoencoder and a CNN or LSTM. Finally, the performance of the bimodal stacked sparse autoencoder [32] was compared. Table 5 summarizes the experimental results of emotion recognition.…”

Section: Classification Results On the Edpe Datasetmentioning

confidence: 99%

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Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

2023

Self Cite

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This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three stages: single-pulse extraction, a physiological coherence feature module, and a physiological common feature module. The experimental results demonstrate that the proposed method surpasses alternative approaches in terms of accuracy and the recognition interval. By extracting common features of the PPG and EMG signals, this approach achieves effective emotion recognition without mutual interference. The findings provide a significant advancement in real-time emotion analysis and offer a clear and concise framework for understanding individuals’ emotional states using physiological signals.

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Section: Classification Results On the Edpe Datasetmentioning

confidence: 99%

“…We required a dataset containing PPG and EMG signals; thus, among the available datasets, we chose the DEAP dataset [12]. Moreover, we created a dataset, EDPE, for more granular emotions (as used in a previous study [32]).…”

Section: Datasetsmentioning

confidence: 99%

Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

2023

Self Cite

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“…A self-supervised method for reliably obtaining representations from distinct physiological inputs was presented by Dissanayake et al [8]. A bimodal structure was employed by Lee et al [9] to enhance recognition performance, with noteworthy outcomes in the classi cation of arousal and valence. In order to extract emotion-related information from EEG data, Pusarla et al [10] created a deep learning-based system that performed better than earlier methods.…”

Section: Literature Surveymentioning

confidence: 99%

Towards non-invasive PTSD diagnosis: Utilising EEG based Emotion Recognition with the DEAP Database

Sudha,

2024

Preprint

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Post-Traumatic Stress Disorder (PTSD) poses a significant challenge in mental health diagnosis, necessitating innovative and non-invasive approaches. This paper explores the efficacy of emotion recognition through electroencephalography (EEG) as a potential diagnostic tool for PTSD. Leveraging the rich resource of the DEAP EEG database, this study focuses on employing statistical features, namely mean, standard deviation, kurtosis, and Hjorth parameters, to ascertain emotional states associated with PTSD. This work outlines the pressing need for effective and non-invasive PTSD diagnosis methods, emphasizing the potential of emotion recognition as a groundbreaking approach. EEG, with its ability to capture neural activity in real-time, emerges as a promising biomarker for decoding emotional responses associated with PTSD. The paper employs a 1D Convolutional Neural Network (1D CNN) as the classifier algorithm, demonstrating its efficacy in discriminating between valence, arousal, and liking associated with PTSD-related emotional responses. Results indicate a remarkable classification accuracy of 97.18%, highlighting the potential of the proposed approach for PTSD diagnosis. This research contributes a non-invasive diagnostic method, bridging the gap between neuroscience, emotion recognition, and mental health, ultimately paving the way for more effective and accessible PTSD assessment tools.

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“…To verify the effectiveness of the UGAN-GRUD model, three publicly available e-health datasets, Health-care [3], Perf-DS1 [26][27][28] and Perf-DS2 [28] were used. Those electronic medical records are the data on human physiological indicators [3,28].…”

Section: Experimental Datasets and Baseline Modelsmentioning

confidence: 99%

Electronic medical records imputation by temporal Generative Adversarial Network

Yin,

Yuan,

Tanvir

et al. 2024

BioData Mining

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The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

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Emotion Recognition with Short-Period Physiological Signals Using Bimodal Sparse Autoencoders

Cited by 9 publications

References 28 publications

Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

Towards non-invasive PTSD diagnosis: Utilising EEG based Emotion Recognition with the DEAP Database

Electronic medical records imputation by temporal Generative Adversarial Network

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