From sensor to inference: end-to-end chip design for wearable and implantable biomedical applications

Van Assche, Jonah; Carlino, Marco Francesco; Alea, Mark Daniel; Massaioli, Sergio; Gielen, Georges

doi:10.1109/biocas58349.2023.10388515

2023 IEEE Biomedical Circuits and Systems Conference (BioCAS) 2023

DOI: 10.1109/biocas58349.2023.10388515

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From sensor to inference: end-to-end chip design for wearable and implantable biomedical applications

Jonah Van Assche,

Marco Francesco Carlino,

Mark Daniel Alea

et al.

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2024

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Cited by 2 publications

References 37 publications

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Brain Feature Extraction With an Artifact-Tolerant Multiplexed Time-Encoding Neural Frontend for True Real-Time Closed-Loop Neuromodulation

Carlino,

Gielen

2024

IEEE Trans. Biomed. Circuits Syst.

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Brain Feature Extraction With an Artifact-Tolerant Multiplexed Time-Encoding Neural Frontend for True Real-Time Closed-Loop Neuromodulation

Carlino,

Gielen

2024

IEEE Trans. Biomed. Circuits Syst.

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Single-trial detection of auditory cues from the rat brain using memristors

Sbandati,

Stathopoulos,

Foster

et al. 2024

Sci. Adv.

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Implantable devices hold the potential to address conditions currently lacking effective treatments, such as drug-resistant neural impairments and prosthetic control. Medical devices need to be biologically compatible while providing enhanced performance metrics of low-power consumption, high accuracy, small size, and minimal latency to enable ongoing intervention in brain function. Here, we demonstrate a memristor-based processing system for single-trial detection of behaviorally meaningful brain signals within a timeframe that supports real-time closed-loop intervention. We record neural activity from the reward center of the brain, the ventral tegmental area, in rats trained to associate a musical tone with a reward, and we use the memristors built-in thresholding properties to detect nontrivial biomarkers in local field potentials. This approach yields consistent and accurate detection of biomarkers >98% while maintaining power consumption as low as 4.14 nanowatt per channel. The efficacy of our system’s capabilities to process real-time in vivo neural data paves the way for low-power chronic neural activity monitoring and biomedical implants.

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From sensor to inference: end-to-end chip design for wearable and implantable biomedical applications

Cited by 2 publications

References 37 publications

Brain Feature Extraction With an Artifact-Tolerant Multiplexed Time-Encoding Neural Frontend for True Real-Time Closed-Loop Neuromodulation

Brain Feature Extraction With an Artifact-Tolerant Multiplexed Time-Encoding Neural Frontend for True Real-Time Closed-Loop Neuromodulation

Single-trial detection of auditory cues from the rat brain using memristors

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