An Interactive EEG Classification System for Seizure, Mental Illness, and Normal Brain Activity with Prescription Guidance

DOI

10.2991/978-94-6239-628-9_28How to use a DOI?

Keywords

Random Forest; XGBoost; SVM; Logistic Regression; KNN; GaussianNB and Gradient Boosting

Abstract

In this work, we introduce a deep learning and machine learning framework for identifying and categorizing neurological disorders. We concentrate on drug recommendations, normal brain activity, mental disease, and seizures. We gathered several datasets from Kaggle and included further data, such as food recommendations and medications. We produced a new dataset after combining these parameters. We created an intuitive user interface that allows users to enter a set of 178 EEG signal values. This research makes it possible to forecast neurological problems in real time. Based on the detected ailment, the system creates a customized prescription plan or strategy for nutritional and drug recommendations. After identifying the condition, the equipment automatically develops a completely individualized treatment plan The proposed system employs a comparative ensemble of classical and advanced machine learning algorithms, including Random Forest, XGBoost, SVM, Logistic Regression, KNN, GaussianNB, and Gradient Boosting, to identify the most accurate classifier based on model performance. The best model achieved 97% accuracy, outperforming the others in prediction and generalization.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rohit Sinha
AU  - Ravi Kant Prasad
AU  - Deepak Kumar
AU  - Chhotelal Mahto
AU  - Vikash Kumar Ravidas
AU  - Abhishek Kumar
AU  - Abhishek Kumar
PY  - 2026
DA  - 2026/03/31
TI  - An Interactive EEG Classification System for Seizure, Mental Illness, and Normal Brain Activity with Prescription Guidance
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 308
EP  - 318
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_28
DO  - 10.2991/978-94-6239-628-9_28
ID  - Sinha2026
ER  -