(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)) - Code World

(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))

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(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)

(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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(Dissertation plus Quellcode) Zwei-Klassen-EEG-Emotionserkennung basierend auf DEAP- und MABHOB-Datensätzen (Pytorch Deep Neural Network (DNN) und Convolutional Neural Network (CNN))

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