Ell, M., Büyükakyüz, A., & Zeck, G. (2024). Digital Filter on FPGA for Neuronal Spike Detection recorded by a CMOS-Based Microelectrode Array. In 2024 Austrochip Workshop on Microelectronics (Austrochip) (pp. 1–4). IEEE. https://doi.org/10.34726/7219
2024 Austrochip Workshop on Microelectronics (Austrochip)
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ISBN:
979-8-3315-1617-8
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Date (published):
2024
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Event name:
32nd Austrochip Workshop on Microelectronics (Austrochip 2024)
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Event date:
25-Sep-2024 - 26-Sep-2024
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Event place:
Vienna, Austria
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Number of Pages:
4
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Publisher:
IEEE, Vienna, Austria
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Peer reviewed:
Yes
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Keywords:
Microelectrodes; Noise; Streaming media; Logic gates; Retina; Recording; Spatiotemporal phenomena; Sensors; Field programmable gate arrays; Sensor arrays; Spiking Neural Networks; Digital Filter; Microelectrode Arrays; Action Potential; Real-time Performance; Noise Sources; Data Streams; Electrical Tomography; Voltage Noise; Real-time Data Processing; Electrode; Spectral Density; Power Spectral Density; Finite Impulse Response; Recording Sites; Finite Impulse Response Filter; Retinal Samples; Electrical Detection; CMOS-based microelectrode array; adhesion noise spectroscopy; real-time systems; Field Programmable Gate Array; Digital Signal Processing; ex vivo retina
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Abstract:
CMOS-based microelectrode arrays (MEAs) enable the recording of electrical activities from biological cells, i.e., neurons or cardiac cells, at multiple sites with single-cell resolution. However, biological and electronic noise sources impair the detection of extracellular voltages. Reliable detection of so-called action potentials (small voltage deflections) would be significantly improved with real-time data processing. Therefore, we present here a Field Programmable Gate Array (FPGA) that filters the data stream and extracts the relevant electrophysiological information. In addition to single-cell activity, we analyze the CMOS MEA area covered by biological tissue using electrical imaging via adhesion voltage noise spectroscopy. Electrical imaging enables the recording of selected areas at unprecedented spatiotemporal resolution.
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Research Areas:
Biological and Bioactive Materials: 20% Computer Engineering and Software-Intensive Systems: 30% Sensor Systems: 50%