Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams

TL;DR

This study develops deep learning-based methods for individual recognition using resting state EEG data in schizophrenia, achieving high classification accuracy and introducing novel neural network modifications with Random Forest and voting layers.

Contribution

The paper introduces new deep learning architectures with integrated Random Forest and voting layers for EEG-based individual recognition in schizophrenia, improving classification performance.

Findings

Achieved 81.6% accuracy for high risk individuals

Achieved 96.7% accuracy for first episode schizophrenia patients

Achieved 99.2% accuracy for healthy controls

Abstract

Recently, there has been a growing interest in monitoring brain activity for individual recognition system. So far these works are mainly focussing on single channel data or fragment data collected by some advanced brain monitoring modalities. In this study we propose new individual recognition schemes based on spatio-temporal resting state Electroencephalography (EEG) data. Besides, instead of using features derived from artificially-designed procedures, modified deep learning architectures which aim to automatically extract an individual's unique features are developed to conduct classification. Our designed deep learning frameworks are proved of a small but consistent advantage of replacing the $softmax$ layer with Random Forest. Additionally, a voting layer is added at the top of designed neural networks in order to tackle the classification problem arisen from EEG streams. Lastly, various experiments are implemented to evaluate the performance of the designed deep learning architectures; Results indicate that the proposed EEG-based individual recognition scheme yields a high degree of classification accuracy: $81.6\%$ for characteristics in high risk (CHR) individuals, $96.7\%$ for clinically stable first episode patients with schizophrenia (FES) and $99.2\%$ for healthy controls (HC).