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TinyLFP: A Tiny Local-Field-Potential Sensor

Version 2 2024-06-03, 12:43
Version 1 2022-02-17, 08:32
journal contribution
posted on 2024-06-03, 12:43 authored by LS Kumari, Abbas KouzaniAbbas Kouzani
Efficient acquisition and analysis of neural signals play a significant role in enhancing our understanding of the brain function and its abnormalities. Neural interfaces involve electrodes inserted into the brain tissues, and also hardware circuits and software to record neural activities and analyze them. Local field potentials (LFPs) are the low frequency neural signals recorded invasively capturing the dynamic extracellular neuronal activities. The information on different bands of neural oscillations (alpha, beta, theta, and gamma) in LFP signals has been used to determine brain abnormalities. LFP signals contain components with amplitudes in the range of 10-100 μV, which are small and can be easily distorted by external noise sources. With the advancements in closed-loop brain stimulation paradigms, there is a need for miniaturized sensors to capture and monitor LFP signals. The sensed LFP signals can then be used to close the feedback loop in a control system to adjust stimulation parameters in a closed-loop brain stimulation device. A LFP sensor requires a low power circuit to ensure prolonged operation. In addition, an area-efficient and low-noise front end stage needs to be formed. Considering these requirements, a single-channel, power-efficient, low-noise, compact neural sensor for acquisition of LFP signals is presented. The miniaturized LFP sensor, named TinyLFP, has a low-noise first stage instrumentation amplifier for providing a pre-amplification of 100 V/V, a band pass filter with a pass band of 4-200 Hz, and a post amplifier stage with an amplification factor of 200 V/V. Overall, the device provides a gain of around 85 dB for the signals in the range of 4 to 200 Hz to be used as the first stage of a closed loop optogenetic brain stimulation device.



IEEE Transactions on Medical Robotics and Bionics






Piscataway, New Jersey





Publication classification

C1 Refereed article in a scholarly journal




Institute of Electrical and Electronics Engineers (IEEE)