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Video s3
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    Author(s)
    Display Name
    Lemaire William
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Display Name
    Takwa Omrani
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation
    Display Name
    Maher Benhouria
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Display Name
    Konin Koua
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Display Name
    Charles Quesnel
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Display Name
    Jérémy Ménard
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation
    Display Name
    Keven Gagnon
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Display Name
    Sébastien Roy
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Display Name
    Fontaine Rejean
    Affiliation
    Affiliation
    Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke
    Abstract

    Neural interfaces allow to better understand the brain by precisely measuring its activity down to single neurons. However, recording a high number of neurons generates a massive amount of data, making wireless transmission difficult. To solve this, we designed a neural recording ASIC comprising a ramp ADC and a spike-by-spike digital compression circuit based on the principal component analysis (PCA). The ASIC comprises 49 channels with an area of 50 x 60 um2 and a power consumption of 4.6 uW. The ASIC measures 1370 x 1370 um2 and consumes 828 uW. This paper presents preliminary performance of the neural recording.