Recognizing the subject exposure from the EEG signals with artificial neural networks

Abstract

The paper presents the analysis of Electroencephalography (EEG) brain waves from the Emotiv Insight device with machine learning, more specifically neural networks. The captured EEG data represents the input data into a machine learning model, which was used to determine when and where the required patterns appear. The experiment of the developed method of capturing data and model usage was carried out by exposing the test subject to the alternating selected images and capturing the EEG brain waves with the Emotiv Insight device. The captured EEG data served as a dataset from which the artificial neural network classification model learnt to successfully recognize when a test subject was exposed to one type of image and when to another. Convolutional and recurrent neural network models were constructed and tested to evaluate the performance of recognition of subject exposal.

Sašo Pavlič

PhD student in computer science & informatics

My research interests include artificial inteligence, machine learning, neural architecture search, anomaly detection, …