EEG-Based Classification of Schizophrenia and Bipolar Disorder with the Fuzzy Method


  • Aryo Sidik Nusa Putra University
  • Harurikson Lumbantobing Nusa Putra University
  • Anang Suryana Nusa Putra University
  • Muchtar Ali Setyo Yudono Nusa Putra University
  • Edwinanto Nusa Putra University
  • Yudha Putra Nusa Putra University
  • Yufriana Imamulhak Nusa Putra University
  • Bayu Indrawan Nusa Putra University



Load modeling, Realistic load, composite load, MATLAB


This study demonstrates various fuzzy-based strategies for classifying and diagnosing people with mental illnesses such as schizophrenia and bipolar disorder. The signals collected from 32 unipolar electrodes during non-invasive electroencephalogram analysis were examined to determine their key characteristics. This research uses a sophisticated fuzzy-based radial basis function neural network. Entropy analysis and analysis of variance of other statistical parameters are also used. Three hundred and twelve schizophrenic patients and 105 individuals with bipolar disorder were examined. In contrast to healthy controls, the data indicated that the patients were correctly classified. With close to 96% accuracy, the suggested method outperforms existing machine learning methods, such as support vector machines and k-nearest neighbors. Conclusion: This categorization method will enable the development of highly accurate algorithms to identify and classify various mental illnesses.




How to Cite

Sidik, A., Lumbantobing, H. ., Suryana, A. ., Yudono, M. A. S. ., Edwinanto, Putra, Y. ., Imamulhak, Y. ., & Indrawan, B. . (2022). EEG-Based Classification of Schizophrenia and Bipolar Disorder with the Fuzzy Method. INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT), 5(2), 1–6.