https://github.com/vidhi1290/deep-learning-for-eeg-emotion-classification

This repository contains a Python code script for performing emotion classification using EEG (Electroencephalogram) data. Emotion classification from EEG signals is an important application in neuroscience and human-computer interaction. The code leverages deep learning techniques to analyze EEG data and predict emotional states.
https://github.com/vidhi1290/deep-learning-for-eeg-emotion-classification

coorelation data-exploration data-preprocessing data-science data-visualization deep-learning deep-learning-algorithms eeg-emotion-recognition egg-signals emotion-distribution emotion-prediction feature-analysis heatmap human-emotions machine-learning machine-learning-algorithms pie-chart spectral-analysis time-series-visualization

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This repository contains a Python code script for performing emotion classification using EEG (Electroencephalogram) data. Emotion classification from EEG signals is an important application in neuroscience and human-computer interaction. The code leverages deep learning techniques to analyze EEG data and predict emotional states.

• Host: GitHub
• URL: https://github.com/vidhi1290/deep-learning-for-eeg-emotion-classification
• Owner: Vidhi1290
• Created: 2023-09-04T10:28:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-04T10:33:22.000Z (over 1 year ago)
• Last Synced: 2024-01-29T11:24:16.542Z (about 1 year ago)
• Topics: coorelation, data-exploration, data-preprocessing, data-science, data-visualization, deep-learning, deep-learning-algorithms, eeg-emotion-recognition, egg-signals, emotion-distribution, emotion-prediction, feature-analysis, heatmap, human-emotions, machine-learning, machine-learning-algorithms, pie-chart, spectral-analysis, time-series-visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 1.79 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# EEG Emotion Classification with Deep Learning

## Overview

This repository contains Python code for performing emotion classification using EEG (Electroencephalogram) data. Emotion classification from EEG signals is an important application in neuroscience and human-computer interaction. The code leverages deep learning techniques to analyze EEG data and predict emotional states.

## Key Features

1. **Data Loading and Preprocessing:**

   - Loads EEG data from a CSV file containing features extracted from EEG signals.

   - Converts emotion labels (e.g., 'NEGATIVE', 'NEUTRAL', 'POSITIVE') to numerical values (0, 1, 2) for classification.

2. **Data Visualization:**

   - Utilizes various visualization techniques to gain insights into the EEG data:

     - Pie chart to display the distribution of emotions in the dataset.

     - Time-series plot to visualize EEG signals over time.

     - Power Spectral Density (PSD) plot for spectral analysis.

     - Correlation heatmap to understand feature correlations.

     - t-SNE (t-Distributed Stochastic Neighbor Embedding) visualization for dimensionality reduction.

3. **Feature Significance Analysis:**

   - Conducts statistical tests (t-tests) to identify significant and non-significant features for each emotion category.

   - Presents results through bar charts, making it easy to understand which features contribute most to emotion prediction.

4. **Advanced Preprocessing:**

   - Normalizes feature data using z-score normalization.

   - Splits the dataset into training and testing sets.

5. **Deep Learning Model:**

   - Builds a deep neural network model for emotion classification.

   - The model architecture consists of multiple dense layers with ReLU activation functions and dropout layers.

   - Compiles the model using the Adam optimizer and sparse categorical cross-entropy loss.

   - Trains the model on the training data with validation, tracking accuracy during training.

6. **Model Evaluation:**

   - Evaluates the trained model on the testing dataset, reporting the accuracy of emotion classification.

   - Generates a confusion matrix and a classification report for a comprehensive performance assessment.

7. **Random Sample Visualization:**

   - Selects a random EEG sample from the testing dataset and predicts its emotion label.

   - Plots EEG signals from the selected sample for visualization.

## Getting Started

For detailed usage instructions and explanations, please refer to the code comments and accompanying documentation in the repository.

## Customization

Feel free to customize and extend this code for your specific EEG emotion classification projects.