NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Shin, Uisub; Ding, Cong; Zhu, Bingzhao; Vyza, Yashwanth; Trouillet, Alix; Revol, Emilie C. M.; Lacour, Stéphanie P.; Shoaran, Mahsa

doi:10.1109/jssc.2022.3204508

Cited by 25 publications

(4 citation statements)

References 68 publications

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“…The selection of traditional and ANN decoders was based on existing literature, while the long short-term memory (LSTM) and SNN-based decoders were optimized for this study. The presented decoders are non-exhaustive (the interested reader is referred to recently proposed models [52][53][54][55][56]), yet the decoders aim to represent commonly used neural decoding methods. The final subsection outlines the experimental setup and details the conditions and parameters under which the benchmark was conducted.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Hueber,

Tang,

Sifalakis

et al. 2024

Neuromorph. Comput. Eng.

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Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency. Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources. This distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

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Section: Methodsmentioning

confidence: 99%

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Hueber,

Tang,

Sifalakis

et al. 2024

Neuromorph. Comput. Eng.

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show abstract

“…To this end, a first-of-its-kind ultra-low-power and high-density neural interface microchip was proposed featuring 256-channel ECoG/local field potentials (LFP) recording, biomarker extraction, ML, and closed-loop stimulation capabilities, all integrated on a single chip with an active area of ~3.5 mm 2 . 75,76 Existing closed-loop devices suffer from relatively low channel count (4-6 channels) and bulky form factor and rely on simple detection methods such as feature thresholding. These issues were addressed by introducing a versatile closed-loop neuromodulation system-on-chip, the NeuralTree.…”

Section: Intelligent Neural Prosthesesmentioning

confidence: 99%

“…Epileptic seizure control could greatly benefit from such intelligent closed‐loop platforms. To this end, a first‐of‐its‐kind ultra‐low‐power and high‐density neural interface microchip was proposed featuring 256‐channel ECoG/local field potentials (LFP) recording, biomarker extraction, ML, and closed‐loop stimulation capabilities, all integrated on a single chip with an active area of ~3.5 mm 2 75,76 …”

Section: Current and Future Devicesmentioning

confidence: 99%

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From basic sciences and engineering to epileptology: A translational approach

et al. 2023

Self Cite

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Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal‐processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)–enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data‐sharing initiatives.

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Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology

Ribeiro,

Andreis,

Jabban

et al. 2024

Journal of Neuroscience Methods

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NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Cited by 25 publications

References 68 publications

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

From basic sciences and engineering to epileptology: A translational approach

Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology

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