Design and Quantitative Evaluation of an Embedded EEG Instrumentation Platform for Real-Time SSVEP Decoding

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Authors: Manh-Dat Nguyen, Thomas Do, Nguyen Thanh Trung Le, Xuan-The Tran, Fred Chang, Chin-Teng Lin
Cited by: 0

Abstract

This paper presents an embedded EEG instrumentation platform for real-time steady-state visually evoked potential (SSVEP) decoding based on an ESP32-S3 microcontroller and an ADS1299 analog front end. The system performs $8$-channel EEG acquisition, zero-phase bandpass filtering, and canonical correlation analysis entirely on-device, while supporting wireless communication and closed-loop operation without external computation. A central contribution is the quantitative characterization of the platform's measurement integrity. Reported results demonstrate a stable shorted-input noise floor ($\approx 0.08~μ\text{V}_{\text{RMS}}$), tightly bounded sampling jitter ($0.56~μ\text{s}$ standard deviation), and negligible long-term drift ($< 1~\text{ppm}$). Numerical fidelity analysis shows $100\%$ decision agreement between the mixed-precision embedded pipeline and a $64$-bit double-precision reference. Effective common-mode attenuation exceeded $112~\text{dB}$ under balanced conditions, with a localized $26.9~\text{dB}$ degradation observed under source-impedance mismatch. Closed-loop validation achieved $99.17\%$ online accuracy and an information transfer rate of $27.66~\text{bits/min}$. These results position the proposed system as a quantitatively characterized embedded EEG measurement and processing platform for real-time SSVEP decoding.

Fields of Study & Subjects

EEG and Brain-Computer Interfaces Advanced Memory and Neural Computing Gaze Tracking and Assistive Technology

Publication details

Journal / source
arXiv (Cornell University)
Publication date
5 January 2026
Document type
Preprint