Emotion Recognition using Deep Convolutional Neural Network on Temporal Representations of Physiological Signals

Singh, Gurwinder; Verma, Kanupriya; Sharma, Neha; Kumar, Ashok; Mantri, Archana

doi:10.1109/icmlant50963.2020.9355990

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Researchers have studied selfsupervised representation learning for physiological signals. However, most of them are targeted for high-frequency signals such as EEG and ECG [8,9], and little research has been done on using representation learning techniques for lowfrequency signals such as heart rate, electrodermal activity that are generated from widely available wearable devices.…”

Section: Introductionmentioning

confidence: 99%

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

et al. 2022

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Extracting emotions from physiological signals has become popular over the past decade. Recent advancements in wearable smart devices have enabled capturing physiological signals continuously and unobtrusively. However, signal readings from different smart wearables are lossy due to user activities, making it difficult to develop robust models for emotion recognition. Also, the limited availability of data labels is an inherent challenge for developing machine learning techniques for emotion classification. This paper presents a novel self-supervised approach inspired by contrastive learning to address the above challenges. In particular, our proposed approach develops a method to learn representations of individual physiological signals, which can be used for downstream classification tasks. Our evaluation with four publicly available datasets shows that the proposed method surpasses the emotion recognition performance of state-of-the-art techniques for emotion classification. In addition, we show that our method is more robust to losses in the input signal.

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Section: Introductionmentioning

confidence: 99%

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

et al. 2022

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A Fuzzy Ensemble-Based Deep learning Model for EEG-Based Emotion Recognition

2023

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Emotion recognition from EEG signals is a major field of research in cognitive computing. The major challenges involved in the task are extracting meaningful features from the signals and building an accurate model. This paper proposes a fuzzy ensemble-based deep learning approach to classify emotions from EEG-based models. Three individual deep learning models have been trained and combined using a fuzzy rank-based approach implemented using the Gompertz function. The model has been tested on two benchmark datasets: DEAP and AMIGOS. Our model has achieved 90.84% and 91.65% accuracies on the valence and arousal dimensions, respectively, for the DEAP dataset. The model also achieved accuracy above 95% on the DEAP dataset for the subject-dependent approach. On the AMIGOS dataset, our model has achieved state-of-the-art accuracies of 98.73% and 98.39% on the valence and arousal dimensions, respectively. The model achieved accuracies of 99.38% and 98.66% for the subject-independent and subject-dependent cases, respectively. The proposed model has provided satisfactory results on both DEAP and AMIGOS datasets and in both subject-dependent and subject-independent setups. Hence, we can conclude that this is a robust model for emotion recognition from EEG signals.

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Emotion Recognition using Deep Convolutional Neural Network on Temporal Representations of Physiological Signals

Cited by 2 publications

References 11 publications

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

A Fuzzy Ensemble-Based Deep learning Model for EEG-Based Emotion Recognition

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