EEG Data Analysis and Visualization

Overview

This project focuses on the analysis and visualization of EEG (Electroencephalogram) data using Python. By leveraging powerful libraries such as Pandas, NumPy, and Matplotlib, the project aims to preprocess, transform, and visualize large EEG datasets to uncover significant patterns and trends in neural responses.

Key Features

• Data Preprocessing: Efficiently read and preprocess large EEG data files using Pandas, ensuring accurate and clean data for analysis.
• Data Transformation: Utilize NumPy to calculate precise time differences across extensive datasets, enhancing the temporal accuracy of EEG data interpretation.
• Data Visualization: Create compelling visualizations with Matplotlib to showcase EEG signal variations and event-related potentials (ERP), facilitating a deeper understanding of neural responses.

Detailed Description

1. Reading and Cleaning Data

   • Import EEG data from text files using Pandas' read_csv() function.
   • Clean the dataset by dropping irrelevant columns and handling missing values.

2. Data Transformation

   • Calculate time differences and other derived metrics using NumPy.
   • Transform the data to prepare it for detailed analysis and visualization.

3. Visualization

   • Generate detailed plots to visualize EEG signal variations over time.
   • Highlight event-related potentials (ERP) to illustrate neural responses to different conditions.
   • Customize visualizations by adjusting figure sizes, labels, and titles to ensure clarity and impact.