README

Finite Impulse Response (FIR) Band-Pass Filter for EEG Data Processing

This code demonstrates the implementation of a Finite Impulse Response (FIR) band-pass filter for processing time-series data obtained from brain parcellation. It utilizes various Python libraries for signal processing, machine learning, and data visualization.

Requirements

• Python 3.x
• Required libraries: numpy, matplotlib, mne, scipy, sklearn

Instructions

1. Data Loading:

   • Ensure that you have EEG data in the BrainVision format (.vhdr file).
   • Uncomment and modify the vhdr_file variable to specify the path to your .vhdr file.
   • Uncomment the relevant code to load the EEG data using mne.io.read_raw_brainvision().

2. Data Visualization:

   • Visualize the EEG signals using raw.plot() after loading the data.

3. Synthetic EEG Data Generation:

   • Synthetic EEG data is generated for demonstration purposes.
   • You can adjust parameters such as sampling frequency, duration, and frequency components of the synthetic signal.

4. Filter Design:

   • Define the sinc function to create filter coefficients using sinc_filter().
   • Specify the desired frequency band using low_cutoff and high_cutoff.
   • Number of filter taps (numtaps) can be adjusted for desired filter characteristics.

5. Signal Filtering:

   • Apply the band-pass filter to the synthetic EEG signal using np.convolve().

6. Feature Extraction:

   • Extract features from the filtered signal, such as mean, standard deviation, skewness, and kurtosis of the Power Spectral Density (PSD) values.

7. Model Training and Evaluation:

   • Train and evaluate machine learning models (Logistic Regression, Random Forest, SVM, Neural Network) using extracted features.
   • Perform hyperparameter tuning for the Random Forest classifier.
   • Evaluate model performance using accuracy metrics and cross-validation.

8. Plotting:

   • Visualize the original synthetic EEG signal, the filtered signal, and the impulse response of the band-pass FIR filter.

Usage

• Ensure that all required libraries are installed (numpy, matplotlib, mne, scipy, sklearn).
• Modify the code as necessary to adapt it to your specific EEG data and analysis requirements.
• Execute the code in a Python environment.

Note

• This code serves as a demonstration and may require modifications for use with actual EEG data.
• Ensure proper understanding of the algorithms and methods employed before applying them to real-world data.