Hardware Implementation of an Adaptive Data Acquisition System for Real-World EEG.

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) signal acquisition is challenged by a wide dynamic range and prominent noise in real-world settings.
• Conventional data acquisition systems (DAQs) are power-intensive, limiting continuous monitoring and requiring frequent recharging.

Purpose of the Study:

• To develop and evaluate an alternative ultra-low power DAQ approach for high-resolution EEG signal acquisition.
• To assess the feasibility of using real-time adaptive analog front-end (AFE) parameter adjustment with low-resolution analog-to-digital converters (ADCs).

Main Methods:

• A hardware implementation of an adaptive AFE DAQ was developed.
• Signal quality was compared against an industry-standard DAQ in both clean and noisy EEG environments.
• The adaptive DAQ adjusted AFE parameters in real time to accommodate low-resolution ADCs.

Main Results:

• The adaptive DAQ successfully reconstructed EEG signals with high correlation and low error standard deviation.
• Effective signal acquisition was demonstrated even at low bit-depths in noisy conditions.
• The adaptive DAQ maintained signal integrity comparable to conventional systems.

Conclusions:

• The adaptive AFE DAQ approach offers a promising solution for low-power, high-resolution EEG monitoring.
• This technology could enable fully integrated, long-duration EEG monitoring systems with reduced power consumption.
• Further development could lead to more accessible and less intrusive neuroimaging technologies.