International Journal of Brain Sciences Original Research
AI-Driven IoT-Based Decision-Making System for Brainwave Study: The KSK Approach
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 neurorehabilitation.
Keywords
References (1)
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