Know-When-Your-Attention-Slacks

Neural network to classify brainwaves to know whether a person is attentive or not

Prerequisites

This project uses Python 3.x and pip.

Installing python and pip

Debain based systems

apt install python3 python3-pip

Arch based systems

pacman -S python python-pip

Solus

eopkg it python3 python3-pip

Installing

External packages used in the python program are: Numpy, Pandas, PyEDFLib and TensorFlow. It is recommended to install these in a python virtual environment. The instructions for installing these packages from the project root folder with a python virtual environment are given below:-

python -m venv .
source bin/activate
pip install -r requirements.txt

To deactivate the virtual environment just execute the following command:-

deactivate

Dataset

The dataset used is 'EEG During Mental Arithmetic Tasks' which can be found at https://physionet.org/content/eegmat/1.0.0/.

The original publication for this resource : Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, Shpenkov O. Electroencephalograms during Mental Arithmetic Task Performance. Data. 2019; 4(1):14. https://doi.org/10.3390/data4010014

The dataset is downloaded and extracted to data/edf_inputs

Understanding the dataset

• The dataset is provided in a format with extension .edf.
• It contains the brainwave readings of 36 people labelled from Subject00 to Subject35.
• Readings taken before the arithmetic task is stored in files labelled with suffix _1 and readings taken during the arithmetic task is stored in files labelled with suffix _2.

Preprocessing the Dataset

Step 1

The edf format data has to be converted to csv.

The code for this is available in src/edf_to_csv.py. This can be run by executing the following commands from the project root folder:-

cd src/
python edf_to_csv.py

After running this program, the corresponding csv file for each edf file will be available in data/csv_inputs

Step 2

The data has to be separated into training set and test set.

The code for this is available in src/split_train_test.py This can be run by executing the following commands from the project root folder:-

cd src/
python split_train_test.py

After running this program, the training set will be stored in data/training_set.csv and the test_set will be stored in data/test_set.csv.

Note: Before running split_train_test.py, please make sure that there are no files named training_set.csv and test_set.csv in the folder data. If these files exist, then the data that you are processing will be appended at the end of these files.

After cloning the repo, if training_set.csv and test_set.csv is not downloaded along with it, you can get them from here. Since they are large files, they have been stored with git-lfs.

Classification

As of now, the following classifiers have been implemented:-

• Neural Network
• Naive Bayes Classifier
• Logistic Regression Classifier
• Support Vector Machine
• Decision Tree
• Random Forest Classifier

To build these classifiers and make predictions, head on to the respective folders and checkout the README

Evaluation of Classifier

To see how the above classifiers can be evaluated, head on to the src folder

For any bugs or enhancement requests, please create an issue 😄