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

TL;DR

This paper introduces an embedded EEG platform using ESP32-S3 and ADS1299 for real-time SSVEP decoding, demonstrating high measurement fidelity, stability, and accuracy suitable for closed-loop brain-computer interface applications.

Contribution

The paper presents a fully on-device EEG system with comprehensive quantitative performance evaluation, enabling real-time SSVEP decoding without external computation.

Findings

Stable noise floor of approximately 0.08 μV RMS

Tightly bounded sampling jitter of 0.56 μs

Closed-loop accuracy of 99.17% and information transfer rate of 27.66 bits/min

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 ($\approx 0.08~\mu\text{V}_{\text{RMS}}$), tightly bounded sampling jitter ($0.56~\mu\text{s}$ 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.