(Thesis plus source code) DEAP-based EEG emotion recognition (four categories) (data enhancement and expansion) (five models for comparison: one-dimensional CNN, LSTM, two-dimensional and three-dimensional CNN and cascade CNN with LSTM) - Code World

(Thesis plus source code) DEAP-based EEG emotion recognition (four categories) (data enhancement and expansion) (five models for comparison: one-dimensional CNN, LSTM, two-dimensional and three-dimensional CNN and cascade CNN with LSTM)

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(Thesis plus source code) DEAP-based EEG emotion recognition (four categories) (data enhancement and expansion) (five models for comparison: one-dimensional CNN, LSTM, two-dimensional and three-dimensional CNN and cascade CNN with LSTM)

(Thesis plus source code) Code analysis of two-class EEG emotion recognition (pytorch deep neural network (DNN) and convolutional neural network (CNN)) based on DEAP and MABHOB data sets

(Thesis plus source code) Two-class EEG emotion recognition based on DEAP and MABHOB data sets (pytorch deep neural network (DNN) and convolutional neural network (CNN))

(Thesis plus source code) EEG emotion recognition based on DEAP EEG data set (using generative adversarial networks for feature extraction and data expansion)

Complete code analysis of DEAP-based EEG emotion recognition (models are SVM, KNN, decision tree, and random forest)

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(Thesis plus source code) EEG emotion recognition based on DEAP EEG data set (using generative adversarial networks for feature extraction and data expansion)

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