Signal Processing and Analysis of EEG Data Using Python
This project demonstrates various signal processing techniques, such as signal generation, window functions, filtering, downsampling, zero-padding, and the application of time-frequency analysis using the Short-Time Fourier Transform (STFT). The project uses Python and its libraries, such as NumPy, SciPy, and Matplotlib, to implement and visualize the methods. The project also analyzes an EEG signal sampled at a rate of 256 Hz and explores its time-domain, frequency-domain, and time-frequency characteristics.
Project Structure
The project consists of two main parts:
- Part 1: This part covers the basics of signal processing, such as generating a chirp signal, applying different window functions, and performing time-frequency analysis using the STFT. It also investigates how different parameters, such as window length, overlapping points, and number of DFT points, affect the time and frequency resolution of the spectrogram.
- Part 2: This part focuses on the analysis of an EEG signal, which is a type of biomedical signal that measures the electrical activity of the brain. It applies low-pass filtering, downsampling, zero-padding, and the DFT to the EEG signal and examines how these techniques influence the signal's representation and frequency content. It also uses the STFT to provide a comprehensive time-frequency analysis of the EEG signal.
Project Requirements
The project requires the following Python libraries:
- NumPy: A library for scientific computing and working with arrays.
- SciPy: A library for scientific and technical computing, such as signal processing, linear algebra, optimization, and statistics.
- Matplotlib: A library for creating plots and visualizations.
- Scipy.io: A module for reading and writing MATLAB files.
Project Files
The project files include:
- NewEEGSignal.mat: A MATLAB file that contains the EEG signal data.
- Analysis_of_EEG_Data.ipynb: A Jupyter notebook that contains the Python code and the results for both questions.