Emotion Recognition Based on Physiological Signals Using Convolution Neural Networks

Song, Tongshuai; Lu, Guanming; Yan, Jun

doi:10.1145/3383972.3384003

Cited by 22 publications

(13 citation statements)

References 23 publications

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“…They achieved 76% accuracy for arousal and 75% for valence. Song et al utilized CNN to perform 3-level arousal and 3-level valence classification using EEG, ECG, EDA, Resp, and SKT physiological signals [43]. They attained 62% and 58% accuracy for arousal and valence, respectively.…”

Section: Convolutional Neural Network and Fully Convolutional Networkmentioning

confidence: 99%

Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning

Saganowski

2022

Electronics

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Bringing emotion recognition (ER) out of the controlled laboratory setup into everyday life can enable applications targeted at a broader population, e.g., helping people with psychological disorders, assisting kids with autism, monitoring the elderly, and general improvement of well-being. This work reviews progress in sensors and machine learning methods and techniques that have made it possible to move ER from the lab to the field in recent years. In particular, the commercially available sensors collecting physiological data, signal processing techniques, and deep learning architectures used to predict emotions are discussed. A survey on existing systems for recognizing emotions in real-life scenarios—their possibilities, limitations, and identified problems—is also provided. The review is concluded with a debate on what challenges need to be overcome in the domain in the near future.

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Section: Convolutional Neural Network and Fully Convolutional Networkmentioning

confidence: 99%

Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning

Saganowski

2022

Electronics

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“…Deep learning methods have also been utilized to analyze and perform emotion recognition using EEG signals. In [ 30 ], two different convolutional neural network techniques were used, performing accuracy scores of 61.5% and 58.01% in arousal, and 58% and 56.28% in valence estimation. In the work of Wang et al [ 11 ], a connectivity analysis on EEG signals was performed by computing the phase-locking value (PLV) between each pair of electrodes.…”

Section: Related Workmentioning

confidence: 99%

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition

Xefteris

Tsanousa

Georgakopoulou

et al. 2022

Sensors

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Emotion recognition is a key attribute for realizing advances in human–computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.

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“…Additionally, CNNs were used on the MAHNOB-HCI dataset to achieve better accuracies than those found using the methods based on feature extraction [ 43 ].…”

Section: Related Workmentioning

confidence: 99%

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

Dzieżyc

Gjoreski

Kazienko

et al. 2020

Sensors

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To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging—smartphones can count steps and compute heart rate, but they cannot recognize emotions and related affective states. This study analyzes the possibility of using end-to-end multimodal deep learning (DL) methods for affect recognition. Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. The DL architectures specialized for time-series classification were enhanced to simultaneously facilitate learning from multiple sensors, each having their own sampling frequency. To enable fair comparison among the different DL architectures, Bayesian optimization was used for hyperparameter tuning. The experimental results showed that the performance of the models depends on the intensity of the physiological response induced by the affective stimuli, i.e., the DL models recognize stress induced by the Trier Social Stress Test more successfully than they recognize emotional changes induced by watching affective content, e.g., funny videos. Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors.

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Emotion Recognition Based on Physiological Signals Using Convolution Neural Networks

Cited by 22 publications

References 23 publications

Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning

Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

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