EEG-Emotion-CNN

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EEG-Emotion-CNN

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This repository contains a Convolutional Neural Network (CNN) model for classifying emotions using Electroencephalography (EEG) signals from the DEAP dataset. The model is designed to classify emotions into different categories (e.g. happy, sad, neutral, etc.) based on the EEG signals.

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**Emotion Classification with CNN (DEAP Dataset)**
==============================================

**Introduction**
---------------

This repository contains a Convolutional Neural Network (CNN) model for classifying emotions using Electroencephalography (EEG) signals from the DEAP dataset. The goal of this project is to develop a machine learning model that can accurately classify emotions into different categories (e.g. happy, sad, neutral, etc.) based on EEG signals.

**Dataset**
------------

The DEAP dataset is a widely used benchmark for emotion recognition tasks. It consists of EEG recordings from 32 participants who watched 40 music videos, and self-reported their emotional states (valence, arousal, liking, and dominance).

**Model Architecture**
---------------------

The CNN model is designed to extract features from the EEG signals and classify them into different emotional categories. The model architecture consists of:

* Conv1D layers for feature extraction
* BatchNormalization layers for normalizing the activations
* MaxPooling1D layers for downsampling
* Flatten layer for feature flattening
* Dense layers for classification
* Dropout layers for regularization

**Hyperparameter Tuning**
-------------------------

To optimize the model's performance, we have experimented with different combinations of hyperparameters, including:

* Loss functions: Binary Cross Entropy (BCE), Categorical Cross Entropy (CCE)
* Optimizers: Adam, Adamax, Nadam, RMSProp, Stochastic Gradient Descent (SGD), Adagrad, Adadelta

**Dependencies**
-------------------

* Python 3.8+
* Keras 2.4+
* Scikit-learn 0.24+
* NumPy 1.20+
* Pandas 1.3+

**Results**
----------

The best-performing model achieved an accuracy of 85% on the test set using the Adamax optimizer and Binary Cross Entropy loss function.

**Future Work**
--------------

* Experiment with different CNN architectures and hyperparameters
* Incorporate additional features from other modalities (e.g. facial expressions, speech)
* Investigate the use of transfer learning and domain adaptation techniques
* Use my dataset for training and testing with EEG and EOG signals

**Acknowledgments**
----------------

I would like to thank the DEAP dataset creators for making the dataset publicly available.

EEG-Emotion-CNN

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