Research on EEG Emotional Recognition Based on LSTM

Jiang, Huiping; Jia, Junjia

doi:10.1007/978-981-15-3415-7_34

Cited by 4 publications

(8 citation statements)

References 11 publications

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“…b) Frequency domain feature. This is based on the frequency domain of a signal, and several features have been reviewed in previous studies such as power spectral density (PSD) [62], band power using wavelet transform [59], [63], mel-frequency cepstral coefficients (MFCCs) technique [64], and differential entropy (DE) [14], [15], [40], [65], [66]. c) Time-frequency domain feature.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The differential entropy (DE) method has, however, been discovered to have the ability to distinguish high energy and low energy patterns from EEG frequencies [14] and also to characterize spatial information from EEG signals [15]. The features generated from the DE method are the most accurate and stable in emotion recognition compared to several others such as autoregressive parameters, fractal dimension, power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM), asymmetry (ASM), differential caudality (DCAU), wavelet features, and sample entropy [40], [65], [66]. The DE formula usually used to characterize an EEG signal is defined as (4) [40].…”

Section: Feature Extractionmentioning

confidence: 99%

“…a) Machine learning approach: Some of the methods usually applied include decision tree (DT) [71], naïve bayes (NB) [72], quadratic discriminant analysis (QDA) [73], k-nearest neighbors (kNN) [58], [74], [75], linear discriminant analysis (LDA) [14], relevance vector machines (RVM) [67], xtreme gradient boosting (XGBoost) [76], support vector machine (SVM) [77]- [79], AdaBoost [80], logistic regression via variable splitting and augmented lagrangian (LORSAL) [81], random forest (RF) [56], [82], and graph regularized extreme learning machine (GELM) [83]. b) Neural network approach: This method include artificial neural network (ANN) [61], [63], [84] deep belief networks [70], [85], convolutional neural network (CNN) [40], [46], [86], [87], long short-term memory (LSTM) [66], generative adversarial networks (GAN) [88], capsule network (CapsNet) [45], [62], and hybrid methods [4], [44], [69].…”

Section: Classification Processmentioning

confidence: 99%

See 2 more Smart Citations

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Classification Processmentioning

confidence: 99%

See 1 more Smart Citation

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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“…In addition to the EEG data oversampling process, there are several vital processes in emotion recognition, such as the feature extraction and the emotion classification processes using the Differential Entropy (DE) method. According to [42], the method is capable of characterizing spatial data from EEG signals with the highlights feature comprising foremost exact and steady features [28,[43][44][45][46]. The classification process in this study compares two methods, namely the Decision Tree and the Convolutional Neural Network.…”

Section: -Literature Reviewmentioning

confidence: 99%

Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

2022

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Several studies related to emotion recognition based on Electroencephalogram signals have been carried out in feature extraction, feature representation, and classification. However, emotion recognition is strongly influenced by the distribution or balance of Electroencephalogram data. On the other hand, the limited data obtained significantly affects the imbalance condition of the resulting Electroencephalogram signal data. It has an impact on the low accuracy of emotion recognition. Therefore, based on these problems, the contribution of this research is to propose the Radius SMOTE method to overcome the imbalance of the DEAP dataset in the emotion recognition process. In addition to the EEG data oversampling process, there are several vital processes in emotion recognition based on EEG signals, including the feature extraction process and the emotion classification process. This study uses the Differential Entropy (DE) method in the EEG feature extraction process. The classification process in this study compares two classification methods, namely the Decision Tree method and the Convolutional Neural Network method. Based on the classification process using the Decision Tree method, the application of oversampling with the Radius SMOTE method resulted in the accuracy of recognizing arousal and valence emotions of 78.78% and 75.14%, respectively. Meanwhile, the Convolutional Neural Network method can accurately identify the arousal and valence emotions of 82.10% and 78.99%, respectively. Doi: 10.28991/ESJ-2022-06-02-013 Full Text: PDF

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“…Regarding feature extraction, the Differential Entropy (DE) method is used to detect low amplitudes [15], as well as characterize the time series of EEG signals [16]. The most accurate and stable features for emotional recognition are generated by this method [17][18][19]. Although the feature extraction process executed using the DE method tends to consider the characteristics of EEG signals, different participant characteristics significantly affect the emotional responses [12,20].…”

Section: -Introductionmentioning

confidence: 99%

Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

Wirawan

Wardoyo²,

Lelono³

et al. 2022

Emerg Sci J

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The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PDF

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Research on EEG Emotional Recognition Based on LSTM

Cited by 4 publications

References 11 publications

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

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