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

arXiv: https://arxiv.org/abs/1707.03467 · HTML: https://mlsanity.com/paper/1707.03467/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 7 of 7)

- [Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models](https://mlsanity.com/paper/2109.01120/) (2021)
- [An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation](https://mlsanity.com/paper/2310.16867/) (2023)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Meta-learning on Spectral Images of Electroencephalogram of Schizophenics](https://mlsanity.com/paper/2101.12208/) (2021)
- [EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity](https://mlsanity.com/paper/2208.08901/) (2022)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models](https://mlsanity.com/paper/2410.19739/) (2024)

## Abstract (arXiv)

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).