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Nigerian Schizophrenia EEG Dataset (NSzED) Towards Data-Driven Psychiatry in Africa

E. O. Olateju, K. P. Ayodele, S. K. Mosaku

TL;DR

The NSzED project tackles the scarcity of geographically diverse schizophrenia EEG data by providing Nigeria-based, multimodal EEG recordings from patients and controls across rest, arithmetic, and auditory tasks. It details a dual-device acquisition with procedure-tailored cues, enabling computation of MMN, SSR, and entropy biomarkers and discusses data quality controls and a structured, device-specific data organization. The dataset supports cross-device, multi-biomarker analyses to advance data-driven psychiatry in Africa and to benchmark models across diverse populations. Overall, NSzED establishes a scalable framework for multimodal EEG-based schizophrenia research in underrepresented regions, with immediate utility for biomarker discovery and methodological benchmarking.

Abstract

This work has been carried out to improve the dearth of high-quality EEG datasets used for schizophrenia diagnostic tools development and studies from populations of developing and underdeveloped regions of the world. To this aim, the presented dataset contains international 10/20 system EEG recordings from West African subjects of Nigerian origin in restful states, mental arithmetic task execution states and while passively reacting to auditory stimuli, the first of its kind from the region and continent. The subjects are divided into patients and healthy controls and recorded from 37 patients and 22 healthy control subjects identified by the Mini International Schizophrenia Interview (MINI) and also assessed by the Positive and Negative Symptoms Scale (PANSS) and the World Health Organization Disability Assessment Schedule (WHODAS). All patients are admitted schizophrenia patients of the Mental Health Ward, Medical Outpatient Department of the Obafemi Awolowo University Teaching Hospital Complex (OAUTHC, Ile-Ife) and its subsidiary Wesley Guild Hospital Unit (OAUTHC, Ilesa). Controls are drawn from students and clinicians who volunteered to participate in the study at the Mental Health Ward of OAUTHC and the Wesley Guild Hospital Unit. This dataset is the first version of the Nigerian schizophrenia dataset (NSzED) and can be used by the neuroscience and computational psychiatry research community studying the diagnosis and prognosis of schizophrenia using the electroencephalogram signal modality.

Nigerian Schizophrenia EEG Dataset (NSzED) Towards Data-Driven Psychiatry in Africa

TL;DR

The NSzED project tackles the scarcity of geographically diverse schizophrenia EEG data by providing Nigeria-based, multimodal EEG recordings from patients and controls across rest, arithmetic, and auditory tasks. It details a dual-device acquisition with procedure-tailored cues, enabling computation of MMN, SSR, and entropy biomarkers and discusses data quality controls and a structured, device-specific data organization. The dataset supports cross-device, multi-biomarker analyses to advance data-driven psychiatry in Africa and to benchmark models across diverse populations. Overall, NSzED establishes a scalable framework for multimodal EEG-based schizophrenia research in underrepresented regions, with immediate utility for biomarker discovery and methodological benchmarking.

Abstract

This work has been carried out to improve the dearth of high-quality EEG datasets used for schizophrenia diagnostic tools development and studies from populations of developing and underdeveloped regions of the world. To this aim, the presented dataset contains international 10/20 system EEG recordings from West African subjects of Nigerian origin in restful states, mental arithmetic task execution states and while passively reacting to auditory stimuli, the first of its kind from the region and continent. The subjects are divided into patients and healthy controls and recorded from 37 patients and 22 healthy control subjects identified by the Mini International Schizophrenia Interview (MINI) and also assessed by the Positive and Negative Symptoms Scale (PANSS) and the World Health Organization Disability Assessment Schedule (WHODAS). All patients are admitted schizophrenia patients of the Mental Health Ward, Medical Outpatient Department of the Obafemi Awolowo University Teaching Hospital Complex (OAUTHC, Ile-Ife) and its subsidiary Wesley Guild Hospital Unit (OAUTHC, Ilesa). Controls are drawn from students and clinicians who volunteered to participate in the study at the Mental Health Ward of OAUTHC and the Wesley Guild Hospital Unit. This dataset is the first version of the Nigerian schizophrenia dataset (NSzED) and can be used by the neuroscience and computational psychiatry research community studying the diagnosis and prognosis of schizophrenia using the electroencephalogram signal modality.
Paper Structure (17 sections, 5 figures, 5 tables)

This paper contains 17 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. NSzED Overview
  3. EEG Recording
  4. EEG Recording Protocol
  5. EEG Records Selection
  6. Participants
  7. Dataset Organization and Format Description
  8. Methods
  9. Candidates Selection
  10. Experiments, Task & Protocol Design, Implementation
  11. Participants
  12. Pre, During & Post Data-Acquisition Precautions
  13. Data Preprocessing
  14. Dataset Utility
  15. Mismatch Negativity (MMN)
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Demographic Information Plots
  • Figure 2: Demographic Information Plots
  • Figure 3: Average of Mismatch Negativity Amplitude Values Across Patients & Controls
  • Figure 4: Average of Mismatch Negativity Waveforms Across Patients & Controls
  • Figure 5: Average ofFuzzy Entropy Values Across Patients & Controls