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Emotion recognition based on DEAP dataset using One-Dimensional CNN, dan RNN (GRU, and LSTM).
EEG-based emotion classification using DEAP dataset
Simply emotion analyse and classify using EEG data based on DEAP dataset, using python and sklearn(SVM,KNN,Tree). 简单的EEG脑电数据情感分析,使用python和DEAP数据集。
Implementation of Thesis: Multimodal Emotion Recognition using Deep Learning Techniques: A novel system for real-world Emotion Recognition
Code to deploy a CNN to classify emotions using EEG Signals, based off the DEAP Dataset
EEG data processing and it's convolution using AutoEncoder + CNN + RNN
This repository compares typical and advanced modeling approaches for EEG Emotion Recognition.
EEG Emotion classification using the DEAP pre-processed data
Emotion recognition from EEG and physiological signals using deep neural networks
Emotional recognition using electroencephelography (EEG)
Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying the emotion as valance, likeness/dislike, arousal, dominance. We have used LSTM and CNN classifier which gives 88.60 % accuracy to predict the model successfully.
This repository contains information on emotion recognition from eeg signals using DEAP data set
Final year project 2021
It is an emotion detection with EEG and ECG signals.
This is a project of combining facial expression and EEG for emotion recognition using python in emotion experiment
Multi-modal Emotion detection from IEMOCAP on Speech, Text, Motion-Capture Data using Neural Nets.
Keras Temporal Convolutional Network.
Multimodal emotion recognition system of attention based vision network + audio network
TensorFlow implementation of "Multimodal Speech Emotion Recognition using Audio and Text," IEEE SLT-18
scikit-learn: machine learning in Python
Chinese BERT classification with tf2.0 and audio classification with mfcc
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