AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study

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Year : 2026 | Volume : 03 | 02 | Page :
    By

    Kazi Kutubuddin Sayyad Liyakat,

  • Kaustubh Subhash Mangrulkar,

  1. Professor, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Researcher, NBN Sinhgad College of Engineering, Solapur, Maharashtra, India

Abstract

The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors with an edge-computing layer that utilizes a lightweight Convolutional Neural Network (CNN) to perform preliminary feature extraction and noise filtering at the source. This filtered data is subsequently transmitted to a cloud-based deep learning engine, which leverages a Long Short- Term Memory (LSTM) network to decode complex temporal patterns in brain activity for clinical decision support. Experimental results demonstrate that this integrated system achieves a 94.2% classification accuracy in detecting cognitive states while reducing power consumption by 22% compared to traditional centralized processing models. By bridging the gap between raw neural oscillation data and actionable intelligence, this system offers a scalable solution for remote patient monitoring, early seizure detection, and personalized neuro-rehabilitation.

Keywords: AI driven IoT, Brain wave, EEG, IoT, Precision, Accuracy, F1 Score

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat, Kaustubh Subhash Mangrulkar. AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study. International Journal of Brain Sciences. 2026; 03(02):-.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat, Kaustubh Subhash Mangrulkar. AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study. International Journal of Brain Sciences. 2026; 03(02):-. Available from: https://journals.stmjournals.com/ijbs/article=2026/view=244688


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Ahead of Print Subscription Review Article
Volume 03
02
Received 05/05/2026
Accepted 12/05/2026
Published 30/05/2026
Publication Time 25 Days
6

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