ArticleFrontiers in systems neuroscience2021
An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals.
Article in Frontiers in systems neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- Dynamical system modeling in schizophrenia: a narrative review of computational psychiatry frameworks.Dialogues in clinical neuroscience · 2026Review
- Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.BioData mining · 2026Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.PloS one · 2026Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.bioRxiv : the preprint server for biology · 2025Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.Journal of medical systems · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- NOVEL APPROACH EXPLAINS SPATIO-SPECTRAL INTERACTIONS IN RAW ELECTROENCEPHALOGRAM DEEP LEARNING CLASSIFIERS.bioRxiv : the preprint server for biology · 2023Article
- Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).Sovremennye tekhnologii v meditsine · 2022Review
- Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces.Frontiers in human neuroscience · 2022Article
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3 authors.
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Abstract
In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
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