AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study - Kazi Kutubuddin Sayyad Liyakat; Kaustubh Subhash Mangrulkar (International Journal of Brain Sciences, 2026)
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; Accuracy; Brain wave; EEG; F1 Score; IOT; Precision