Optimizing EEG Signal Quality and Streaming Performance in BCIs Using ADS1299EEG-FE

Reliable and noise-free electroencephalography (EEG) acquisition is a key factor in building effective braincomputer interface (BCI) systems. This paper presents an evaluation of methods for optimizing EEG signal quality and realtime streaming performance using the ADS1299EEG-FE platform. The study compares hardware-based noise reduction strategiessuch as active shielding and grounding-with software-level filtering and adaptive preprocessing techniques to enhance the signal-to-noise ratio (SNR) under varying environmental conditions. In addition, an edge-based streaming setup using a Raspberry Pi was implemented to test the performance of realtime EEG data transmission to a cloud analytics platform. Experimental results showed that the optimized configuration achieved up to a 35% reduction in power line noise and a 40% improvement in signal stability during continuous recording, while maintaining low data latency suitable for BCI feedback applications. The outcomes demonstrate that ADS1299EEG-FE can serve as a reliable, low-cost, and scalable solution for EEG data acquisition in both research and educational environments. These findings contribute to ongoing efforts to make EEG-driven intelligent systems more accessible and efficient for neurotechnology applications.