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Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.

Gran Badshah¹, Anurag Sinha², Himanshu Bansal³

¹Department of Computer Science, King Khalid University, Abha, 61413, Saudi Arabia.

|May 12, 2026

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This summary is machine-generated.

This study presents an adaptive deep learning framework for diagnosing schizophrenia using electroencephalography (EEG) signals. The novel approach ensures reliable diagnosis by adapting to changing EEG patterns over time, achieving 98.12% accuracy.

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Static deep learning models struggle with diagnostic reliability due to shifting electroencephalography (EEG) signal distributions.
Patient variability, noise, medication, and session changes can degrade the performance of traditional EEG-based diagnostic systems.

Purpose of the Study:

To introduce a novel adaptive deep learning framework for robust EEG-based schizophrenia diagnosis.
To address the limitations of static models in handling non-stationary EEG data.

Main Methods:

Developed a drift-aware framework integrating deep feature learning, concept-drift detection, and sliding-window analysis.
Combined convolutional neural representations with an adaptive drift-monitoring module for continuous evaluation of temporal EEG changes.

Keywords:

Attention networks Electroencephalography Long short-term memory (LSTM)Schizophrenia Signal processing

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Implemented autonomous parameter updates upon drift detection to ensure stable performance.

Main Results:

The proposed adaptive architecture demonstrated superior robustness, lower variance, and stronger generalization compared to conventional deep learning frameworks.
Achieved the highest accuracy (98.12%) and best mean rank (1.25) with the NEXUS model, outperforming HYPR-A and ZETA-G.
Validated the effectiveness of augmentation and enhancement methods like SMOTE and data-driven signal transformations.

Conclusions:

Integrating drift-adaptive learning with deep EEG feature extraction significantly enhances diagnostic precision for schizophrenia.
The proposed framework offers a clinically scalable solution for early and reliable schizophrenia detection, even under non-stationary conditions.