Abstract:
Neural interfaces allow better understanding of the brain by precisely measuring its activity, down to the level of single neurons. However, recording a high number of ne...Show MoreMetadata
Abstract:
Neural interfaces allow better understanding of the brain by precisely measuring its activity, down to the level of single neurons. However, recording a high number of neurons requires a high number of analog-to-digital converters that generate a massive amount of data, making wireless transmission difficult. To solve this, we designed a neural recording application specific integrated circuit (ASIC) comprising a single ramp analog-to-digital converter (ADC) and a spike-by-spike digital compression circuit based on principal component analysis (PCA). The ASIC comprises 49 channels (each occupying an on-chip area of 50 × 60 µm2), a simulated noise of 12.3 µV RMS, and a power consumption of 4.6 µW. The circuit measures 1370 × 1370 µm2 and consumes 828 µW. This paper presents the architecture and preliminary performance results of the neural recording ASIC and its compression circuit.
Published in: 2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)
Date of Conference: 19-22 June 2022
Date Added to IEEE Xplore: 05 August 2022
ISBN Information: