Title : AI based brain computer interface for detecting neurological disorders using EEG signals
Abstract:
Neurological disorders like epilepsy, Parkinson's, Alzheimer's, and stroke affect millions of people around the world. These disorders are especially hard to treat in rural places where people don't have easy access to specialized medical care. Traditional diagnostic approaches like MRI scans and clinical assessments, although valuable, tend to be costly, lengthy, and necessitate expert analysis, making them unfeasible for early detection and continuous monitoring in environments with limited resources. This research introduces an AI-Based Brain-Computer Interface (BCI) system to tackle these challenges and performs the real-time EEG-based detection and monitoring of neurological disorders using state-of-the-art artificial intelligence techniques. The suggested method consists of a systematic pipeline that includes EEG data acquisition, signal artifact removal, feature extraction, classification, and real-time implementation phases. Multi- channel electrodes collect EEG signals using the 10−20 or 10−10 international electrode system to cover a large area of the brain. Acquired signals are subsequently subjected to band-pass filtering, Independent Component Analysis (ICA), and wavelet transform in order to eliminate noise and artifacts like muscle movements and eye blinks, providing high-quality data for analysis. The process of feature extraction should be included and predominantly represented by the calculation of the Power Spectral Density (PSD), the evaluation of the entropy-based measures, Common Spatial Patterns (CSP), and functional connectivity analysis. For disorder detection, machine learning and deep learning models are employed, with ensemble learning techniques integrate multiple models to improve classification accuracy and robustness, ensuring precise differentiation between normal and pathological brain activity.
The system uses IoT-enabled remote monitoring and edge computing, which in turn allow low-latency processing and efficient deployment in rural settings, to facilitate real-time functionality. At the end of a process, a portable AI-based BCI device, providing prompt diagnostics, alerts and EEG visualizations, will be a fully functional piece of equipment that will aid medical professionals and caregivers in decision-making. An innovative and affordable BCI system that includes an artificial intelligence solution that is well ratings in cost and accessibility will be developed for the accurate and early detection of neurological disorders in remote areas. It is through timely medical intervention, personalized treatment, and long-term patient monitoring, the proposed AI-based BCI system that has the potential to revolutionize neurological healthcare, improve patient outcomes and bridge the accessibility gap between urban and rural populations.
