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<p>Schizophrenia (SZ) is a significant mental health illness that impacts people’s thoughts, feelings, and behaviour. It may cause a variety of hallucinations, delusions, and disorganised thought and behaviour, making daily life difficult. SZ patients require lifetime care. Early intervention improves the long-term prognosis by controlling symptoms before major complications occur. It has been determined that electroencephalography (EEG) is an effective biomarker for SZ detection. However, it is challenging to extract useful information from EEG signals because of their low intensity and instability. Thus, the vision transformer (ViT) that operates in the time-frequency domain is used in this work to automatically detect SZ. However, pure transformer designs frequently need a substantial quantity of training data or additional supervision. And, there is always a scarcity of datasets in the medical field. Therefore, we present a deep neural network (MGViT) incorporated ViT that combines the benefits of transformers in creating long-range dependencies and CNNs in extracting low-level features. Initially, the multichannel EEG signals are converted to recurrence plots (RP) to capture both spatial and temporal information. Later, RP images are processed through MGViT for early identification of SZ. The proposed approach is trained on two publicly accessible EEG datasets and obtained 98.92% and 88.17% accuracies, respectively. Further, the proposed work is contrasted with seven competitive techniques and achieved a better performance with an improvement of 1–26% in dataset-1 and 2–24% in dataset-2, proving its effectiveness in medical applications.</p>

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A Multi-level Deep Neural Network incorporating Vision Transformer for Electroencephalography signals based Schizophrenia Detection

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Geet Sahu,
  • Sunidhi Singh,
  • Mohan Karnati,
  • Malay Kishore Dutta

摘要

Schizophrenia (SZ) is a significant mental health illness that impacts people’s thoughts, feelings, and behaviour. It may cause a variety of hallucinations, delusions, and disorganised thought and behaviour, making daily life difficult. SZ patients require lifetime care. Early intervention improves the long-term prognosis by controlling symptoms before major complications occur. It has been determined that electroencephalography (EEG) is an effective biomarker for SZ detection. However, it is challenging to extract useful information from EEG signals because of their low intensity and instability. Thus, the vision transformer (ViT) that operates in the time-frequency domain is used in this work to automatically detect SZ. However, pure transformer designs frequently need a substantial quantity of training data or additional supervision. And, there is always a scarcity of datasets in the medical field. Therefore, we present a deep neural network (MGViT) incorporated ViT that combines the benefits of transformers in creating long-range dependencies and CNNs in extracting low-level features. Initially, the multichannel EEG signals are converted to recurrence plots (RP) to capture both spatial and temporal information. Later, RP images are processed through MGViT for early identification of SZ. The proposed approach is trained on two publicly accessible EEG datasets and obtained 98.92% and 88.17% accuracies, respectively. Further, the proposed work is contrasted with seven competitive techniques and achieved a better performance with an improvement of 1–26% in dataset-1 and 2–24% in dataset-2, proving its effectiveness in medical applications.