International Journal For Multidisciplinary Research

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NEUROID:EEG-Based Brainwave Authentication System

Author(s) Mr. Shreyas Shripad Wakhare, Mr. Eshaan Deepak Warade, Mr. Parth Anil Yangandul, Dr. Shagufta Mohammad Sayeed Sheikh
Country India
Abstract This study introduces a functional EEG-based Multi-Factor Authentication (EEG-MFA) system engineered for accessibility and security utilizing affordable consumer hardware. Our version uses the BioAmp EXG Pill ( |3,000) with Arduino UNO, which is far cheaper than standard biometric systems that need expensive medical-grade equipment (|50,000–|500,000). It gets 86.7% authentication accuracy when the signal is good.The system uses three authentication factors: a password (knowledge), a pattern (behavior), and an EEG biometric (inherence). This makes it more secure. We utilize One-Class SVM with RBF kernel (nu=0.1) for user modeling, which means we don’t have to collect fake data, which is a big problem when using biometrics. The system learns brain patterns unique to each user using just 3–5 enrollment recordings (12 seconds each) and a simple electrode setup (3 electrodes: forehead + ears).Recent improvements in open-source EEG gear have made it much cheaper. With devices like the BioAmp EXG Pill (around 3,000 rupees), OpenBCI boards (100–500 dollars), and NeuroSky MindWave (100 dollars), students can do projects and small-scale research that weren’t possible before with medical-grade equipment. This lower price makes it possible to look into EEG authentication outside of established labs, utilizing real-world consumer technology that has its own problems. Some of the most important new features are: (1) an adaptive learning mechanism that lowers the False Rejection Rate from 20% to 0% over five sessions while keeping the False Acceptances at zero; (2) a tolerance margin system (10%) that makes up for differences in electrode placement; and
(3) a complete end-to-end implementation with FastAPI backend, PostgreSQL database, and Next.js frontend.When we tested with real consumer hardware, we found that the most important performance aspect was signal quality (electrode preparation). With the right setup, we got an 80% genuine acceptance rate and a 0% imposter acceptance rate. The 10% Equal Error Rate (EER) is higher than medical-grade systems (¡5%), but it shows that it is possible to use it for specialized security applications, educational research, and proof-of-concept deployments where cost is more important than accuracy.
Keywords EEG, biometric authentication, brain-computer interface, support vector machine, SVM(Support Vector Ma- chine), brainwave analysis
Field Computer Applications
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.60549
Short DOI https://doi.org/hbdrdn
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