Title : Evaluating ADS1299EEG-FE methods for noise reduction and real-time EEG streaming in BCI
Abstract:
The quality of EEG data is crucial for effective brain-computer interface (BCI) applications, and the ADS1299EEG-FE board has emerged as a powerful contender in this arena due to its high-resolution ADC and integrated amplifiers. This review critically examines two distinct approaches for EEG acquisition using the ADS1299EEG-FE. The first approach, explored by Jiang et al. (2018), leverages advanced hardware-based shielding techniques—including active shielding—to mitigate power line noise and movement-induced artifacts, thereby enhancing the signal-to-noise ratio (SNR) across various biopotential recordings. In contrast, the second approach, demonstrated by Yong and Ho (2016), integrates the ADS1299EEG-FE with a Raspberry Pi and cloud computing infrastructure to enable real-time, portable EEG monitoring. While this method offers a promising pathway for continuous, remote data acquisition, it also introduces challenges related to data loss and computational overhead due to network latency. By synthesizing the methodologies and outcomes of these studies, this review provides a comparative analysis of the advantages and limitations inherent to each method. In doing so, it offers critical insights into optimizing EEG data acquisition for diverse BCI applications.
