Emotion Recognition from EEG Signals Using Recurrent Neural Networks

Chowdary, M. Kalpana; Anitha, J.; Hemanth, D. Jude

doi:10.3390/electronics11152387

Cited by 43 publications

(18 citation statements)

References 28 publications

(25 reference statements)

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“…However, CNN's bene ts also place restrictions on its capacity to analyze data that can be organized using graphs but is irregular or from non-Euclidean domains. By fusing spectrum theory and convolutional neural networks, graph convolutional neural network (GCNN) extends the capabilities [13] of the conventional convolutional neural network (CNN). The graph convolutional neural network has an advantage over the standard convolutional neural network when it comes to extracting discriminative features from signals in the discrete spatial domain.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Emotional State Detection Using EEG Data With A Multi-Model Deep Learning Approach

immanuel,

SKB

2024

Preprint

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Human emotion is the mind's reaction to external stimuli. Since human emotions are dynamic and hard to predict in the real world, studies focusing on this area have gained a lot of importance. Emotion recognition using EEG(electroencephalogram) signals has recently seen prevalent use of many deep learning and machine learning techniques.In this paper, we have used a real time dataset which includes 15 subjects (7 Males and 8 Females) and their EEG signals are recorded using video stimuli. The real time data is preprocessed and features are extracted from the preprocessed data using different feature extraction methods. The accuracy and loss of model are calculated and compared with raw and preprocessed data. The proposed model - EEGEM (Electroencephalogram Ensemble Model) is compared with other machine and deep learning techniques. EEGEM is a ensemble model with the combination of LSTM and CNN together to achieve the desired output. The accuracy achieved using this model is 95.56% and it has outperformed other existing models.

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Section: Related Workmentioning

confidence: 99%

Enhancing Emotional State Detection Using EEG Data With A Multi-Model Deep Learning Approach

immanuel,

SKB

2024

Preprint

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“…Before the prediction model is constructed, a model must be assessed using several evaluation criteria [31]. To date, we have evaluated our prediction models using means and accuracy scores.…”

Section: E Performance Evaluationsmentioning

confidence: 99%

Classification of Psychological Disorders by Feature Ranking and Fusion using Gradient Boosting

Tahseen¹,

Danti²

2023

IJACSA

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Negative emotional regulation is a defining element of psychological disorders. Our goal was to create a machinelearning model to classify psychological disorders based on negative emotions. EEG brainwave dataset displaying positive, negative, and neutral emotions. However, negative emotions are responsible for psychological health. In this paper, research focused solely on negative emotional state characteristics for which the divide-and-conquer approach has been applied to the feature extraction process. Features are grouped into four equal subsets and feature selection has been done for each subset by feature ranking approach based on their feature importance determined by the Random Forest-Recursive Feature Elimination with Cross-validation (RF-RFECV) method. After feature ranking, the fusion of the feature subset is employed to obtain a new potential dataset. 10-fold cross-validation is performed with a grid search created using a set of predetermined model parameters that are important to achieving the greatest possible accuracy. Experimental results demonstrated that the proposed model has achieved 97.71% accuracy in predicting psychological disorders.

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“…al. [40] examined three different architectures: recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU). The experiment utilized the EEG Brain Wave Dataset: Feeling Emotions and yielded impressive results.…”

Section: Related Workmentioning

confidence: 99%

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

Gupta

Sharma

Bhardwaj

et al. 2023

Preprint

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In this study, a hybrid architecture combining a Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU) is proposed for multi-class emotion recognition using EEG signals.Emotion recognition using EEG signals is a challenging task due to the ever-changing nature of EEG signals and the high dimensionality of the feature space. The proposed approach aims to address these challenges by utilizing a hybrid architecture that combines the strengths of both 1D-CNN and GRU. The 1D-CNN is used to retrieve relevant spatial features from the EEG signals, while the GRU is employed to capture the temporal dependencies in the signals. The models were used to classify multi-class emotions: four and sixteen emotions based on the valence-arousal and valence-arousal-liking-dominance planes, respectively, using the benchmark DEAP dataset. The experiment results showed that the proposed models achieved high accuracy in classifying emotions for both four and sixteen emotions as compared to state of art methods. The results of this research have significant implications for the development of affective computing systems in various fields, including healthcare, human-computer interaction, and education. In conclusion, this study demonstrates the potential of deep learning models in affective computing and provides a foundation for future research in this field. The use of reliable physiological signals and the combination of different architectures have shown to be effective in accurately classifying emotions.

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Emotion Recognition from EEG Signals Using Recurrent Neural Networks

Cited by 43 publications

References 28 publications

Enhancing Emotional State Detection Using EEG Data With A Multi-Model Deep Learning Approach

Enhancing Emotional State Detection Using EEG Data With A Multi-Model Deep Learning Approach

Classification of Psychological Disorders by Feature Ranking and Fusion using Gradient Boosting

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

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