A 382nVrms 100GΩ@50Hz Active Electrode for Dry-Electrode EEG Recording

Area of Science:

• Biomedical Engineering
• Electrical Engineering
• Neuroscience Instrumentation

Background:

• Dry-electrode electroencephalography (EEG) offers a non-invasive method for brain activity monitoring but faces challenges with high electrode-skin impedance.
• Existing active electrode (AE) interface chips struggle to achieve the ultra-high input impedance required for reliable dry-electrode EEG recording.
• Flicker noise and parasitic capacitance significantly degrade signal quality in traditional AE designs.

Purpose of the Study:

• To develop a low-noise, ultra-high input impedance active electrode (AE) interface chip specifically for dry-electrode EEG applications.
• To address limitations in previous AE designs by incorporating chopper stabilization for flicker noise suppression and advanced bootstrapping techniques.
• To validate the performance of the proposed AE chip in real-world EEG recording scenarios.

Main Methods:

• Designed an AE interface chip using a 0.18μm CMOS process, integrating power/ground/ESD bootstrapping to manage parasitic capacitance and leakage.
• Implemented a chopper stabilization technique to mitigate amplifier flicker noise, a novel approach for bootstrapped AE designs.
• Employed active shielding for both on-chip and off-chip input routing to minimize parasitic effects.

Main Results:

• Achieved an ultra-high input impedance of 100GΩ at 50Hz and over 1GΩ at 1kHz.
• Demonstrated a low input-referred noise of 382nVrms (0.5Hz-70Hz).
• The AE core is compact (0.056mm²) and power-efficient (17.95μA at 1.8V).

Conclusions:

• The proposed AE chip represents a significant advancement, being the first to achieve 100GΩ@50Hz input impedance with chopper stabilization for dry-electrode EEG.
• Successfully verified the AE's capability for recording spontaneous α-wave, event-related potential, and steady-state visual evoked potential.
• This technology holds promise for improving the quality and accessibility of dry-electrode EEG systems.