Project Goals

Our objective is to capture and preprocess EEG signals, build learning models that can detect epileptic seizures with an accuracy greater than 80%, and build a software architecture around it to showcase our results.

Hardware

Emotiv Insight is a consumer graded product, designed for Brain Computer Interface (BCI) [1]. The device can measure EEG signals from 5 different channels – AF3, AF4, T7, T8 and Pz. Besides having an adequate number of channels, this headset gives us a better signal quality compared to other alternatives.
EMOTIV Insight headset

Photo 1: EMOTIV Insight headset

Models & results

K-means clustering

The K-means clustering algorithm is implemented using tslearn.TimeSeriesKMeans with metrics Dynamic Time Warping specifically used for time series comparison.

The model returned prediction results of 94% accuracy with the following centroids and confusion matrix. As proposed from the confusion matrix, 377 out of 400 time series were classified correctly with 0 type I error and 23 out of 400 time series were misclassified as type II error.

Photo 1. Centroids of the model.

Photo 2.Confusion matrix from testing the model.

Pyramidal 1-Dimensional Convolutional neural network (P1D-CNN)

P1D-CNN is implemented through tensorFlow by adding layers to model.Sequential and later was tested with fragments of EEG signal to determine whether patient has epilepsy or not. One side not from the architecture below is that the second fully connected layer has the activated sigmoid function.

Results from training and testing with data are gathered after splitting testing signal to a fixed window sized(with overlap) and plugging in optimized parameters.

Photo 4: Callback the model with highest validation accuracy during training

Photo 5: 10-fold cross validation with the given parameters average accuracy and loss

Software

Overall software architecture

Photo 6: Software architecture

Back end

The backend is built in Python and connects to a Heroku PostgreSQL database using FastAPI with SQLAlchemy. We chose Python because we were familiar with it, and PostgreSQL because it’s a decent solution to store time-series and relational data that provides stability and speed out of the box.

Database

The database contains a table for storing Patient details, multiple dynamic time-series tables for storing individual session data for each patient, and one table for storing the results generated by our AI models for each session.

Front end

The web application is written based on Vue.js, a Javascript framework for building user interfaces. For the project, we aim for a simple interface where users can add new patient information to the database, delete existing patient data, and view data from all the patients. Analytic results from the 2 models can be accessed within the view of any individual patient.