Related Experiment Video
Updated: Oct 28, 2026
High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
Published on: November 26, 2016
Open-Source, High-Speed and High-Resolution Data Acquisition Platform for Biopotential Recordings and Neural EIT
Enrico Ravagli1, Alistair McEwan2, Kirill Y Aristovich3
1Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, London, WC1E 6BT, United Kingdom of Great Britain and Northern Ireland.
Objective:
Biopotential measurement devices, such as EEG, ECG, and EMG recorders, are available in low-cost, open-source implementations with standard specifications. However, high-end systems remain expensive and predominantly proprietary, limiting accessibility and customization by research laboratories. In addition, neurophysiology techniques such as bioimpedance-based Fast Neural Electrical Impedance Tomography (FN-EIT) also rely on these systems for data acquisition. This work aimed to develop an open-source biopotential recording system using off-the-shelf components that achieves performance comparable to high-end devices.
Approach:
We designed our system to provide simultaneous sampling over 32 channels, 24-bit resolution, 10 kHz bandwidth, 50 kHz sampling rate, and battery-powered operation while reducing cost by two orders of magnitude. System performance was evaluated comparatively against a reference device. Initial validation involved benchtop recordings in saline solution and standard non-invasive biopotential measurements (ECG and EMG). Further in-vivo validation was performed by recording evoked electrophysiological responses and FN-EIT traces from the sciatic nerve of a rat during tibial branch stimulation.
Main Results:
EMG recordings showed comparable RMS peak amplitudes (814±153 µV vs. 897±113µV, p=0.07), while ECG-derived heart rates closely matched between systems (64.8±4.0 bpm vs. 65.1±3.1bpm, p=0.54). During in-vivo recordings, compound action potentials exhibited comparable amplitudes and morphology (129±26 mV vs 128±25 mV, P=0.15). FN-EIT recordings showed strongly correlated baseline voltages (R>0.93, P=0.11), sub-microvolt noise levels (0.83±0.36µV vs. 0.42±0.25µV, p<0.05), and comparable impedance variations (0.006±0.002% vs 0.005±0.003, P>0.05). FN-EIT images of functional activity recorded with the novel device closely matched reference ones, exhibiting a 98.5% overlap in activated area.
Significance:
The proposed open-source device has the potential to broaden research access to customizable, high-specification data acquisition hardware and facilitate wider adoption of specialized neural recording techniques such as FN-EIT.
