This project aims to recognize the person as either alcoholic or non-alcoholic based on their EEG sample data
We began with some set of textual data which was collected from multiple people and classified as Alcoholic and Controlled
This dataset was extracted from the zip files and converted into csv format for each person
The dataset was then cleaned and converted into excel format for removing noise using Matlab
Then the multiple files for each person were combined into a single excel file
We then used a method called FORCe which is used to remove all the artifacts from the dataset which is basically non-human noise data in the EEG signal
Feature extraction using the discrete wavelet transform function was applied to each of the files for 15 epochs, where each epoch was of 4 seconds and sampling rate was 256 Hz.
Post creation of wavelet transform variables over the whole dataset for 15 epochs, we began feature extraction for each of the features
The following features were extracted :
- Wavelet energy
- Shannon entropy
- Mean
- Variance
- Median
The average value of each feature was taken over the 15 epochs for each wavelet coefficient.
The data was then converted to 5(features) * 6(wavelet coefficients) = 30 columns for classification
Classification was done using multiple ML and DL methods which try to match state of the art accuracies. The results will be published in this repository and in a research paper for pubic use.