An Efficient Approach to EEG-Based Emotion Recognition using LSTM Network

Anubhav,; Nath, Debarshi; Singh, Mrigank; Sethia, Divyashikha; Indu, S.

doi:10.1109/cspa48992.2020.9068691

Cited by 30 publications

(12 citation statements)

References 13 publications

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“…The classification of the two classes with the attention LSTM showed an accuracy of 90.1% and 87.9% for valence analysis and arousal analysis under the four-fold cross validation method. The accuracy of the two-level classification emotion recognition in the previous studies was approximately 57.0–94.7% in terms of valance and 62.0–93.1% for arousal analysis [ 7 , 8 , 9 , 10 , 13 , 16 , 23 , 24 , 25 ]. The three-level emotion classification results were 53.4–60.7% for valance analysis and 46.0–62.33% for arousal analysis, which are lower than those obtained using the two-level classification [ 7 , 10 , 11 ].…”

Section: Resultsmentioning

confidence: 99%

“…They confirmed that the accuracy of the valence was 85.4% and that of the arousal was 85.6% in two-level classification. Anubhav et al proposed an LSTM-based emotion recognition approach that calculates the band power of the EEG signal and inputs the frequency characteristics [ 24 ]. The DEAP dataset was applied to the model, and the accuracy was 94.69% for valence recognition and 93.13% for arousal recognition in two-level classification.…”

Section: Introductionmentioning

confidence: 99%

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EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism

Kim

Choi

2020

Sensors

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Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak–end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism

Kim

Choi

2020

Sensors

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“…The LSTM deep learning model is used extensively in signal processing studies in recent years. Since LSTM has short and long term memory units, it shows more successful results in signal classification studies compared to other methods (Nath et al, 2020).…”

Section: Classification With Lstm Modelmentioning

confidence: 99%

Estimation of Pain Threshold From Eeg Signals of Subjects in Physical Therapy Using Long-Short-Term Memory Deep Learning Model

Güneç¹,

Kasım²,

Tosun³

et al. 2021

Uludağ University Journal of the Faculty of Engineering

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Pain is a natural stimulation to protect the whole body. An overreaction to pain can damage the tissues. Therefore, it is important to know the angle at which pain is felt when routinely measuring joint range of motion during the first examination. Detection of pain with the change in characteristics of electroencephalogram signals at the moments when pain occurs is the novelty of this study. The characteristics of the signal with power band changes were obtained by frequency analysis of the electroencephalogram signals. Pain was detected by classifying these characteristics with the Long Short Term Memory deep learning model. Validation of the model was performed with records obtained from 43 volunteer subjects with a 14-channel wireless Emotive brand electroencephalogram device. 96.1% success in binary classification as with pain or without pain and 89.6% success in multi-class classification as with high pain, low pain and without pain was achieved. This success is a quality that can support specialists in diagnosis and treatment by determining the threshold where pain occurs during the first physical therapy examination from the electroencephalogram signals.

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“…Soleymani et al [34] proposed a LSTM-RNN and continuous conditional random field algorithm to detect the emotional state of the subjects from their EEG signals and facial expressions. Anubhav et al [1] proposed a LSTM neural network, and which could efficiently learn the features from the the band power of EEG signals. Fourati et al [8] presented an Echo State Network (ESN), which applied recursive layer to perform the feature extraction step directly from the EEG raw.…”

Section: Eeg Feature Extractionmentioning

confidence: 99%

SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction

Deng

Zhu

Yang

2021

Preprint

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Emotion recognition based on EEG (electroencephalography) has been widely used in human-computer interaction, distance education and health care. However, the conventional methods ignore the adjacent and symmetrical characteristics of EEG signals, which also contain salient information related to emotion. In this paper, we present a spatial folding ensemble network (SFENet) for EEG feature extraction and emotion recognition. Firstly, for the undetected area between EEG electrodes, we employ an improved Bicubic-EEG interpolation algorithm for EEG channel information completion, which allows us to extract a wider range of adjacent space features. Then, motivated by the spatial symmetry mechanism of human brain, we fold the input EEG channel data with five different symmetrical strategies: the left-right folds, the right-left folds, the top-bottom folds, the bottom-top folds, and the entire double-sided brain folding, which enable the proposed network to extract the information of space features of EEG signals more effectively. Finally, 3DCNN based spatial and temporal extraction and multi voting strategy of ensemble learning are employed to model a new neural network. With this network, the spatial features of different symmetric folding signlas can be extracted simultaneously, which greatly improves the robustness and accuracy of feature recognition. The experimental results on DEAP and SEED data sets show that the proposed algorithm has comparable performance in term of recognition accuracy. CCS CONCEPTS• Computing methodologies → Neural networks; Shape representations; Object identification.

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An Efficient Approach to EEG-Based Emotion Recognition using LSTM Network

Cited by 30 publications

References 13 publications

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism

Estimation of Pain Threshold From Eeg Signals of Subjects in Physical Therapy Using Long-Short-Term Memory Deep Learning Model

SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction

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