A Wireless Electro-Optic Headstage With a 0.13-${\mu}$m CMOS Custom Integrated DWT Neural Signal Decoder for Closed-Loop Optogenetics

Gagnon-Turcotte, Gabriel; Keramidis, Iason; Éthier, Christian; Gosselin, Benoît

doi:10.1109/tbcas.2019.2930498

Cited by 17 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The APs are detected using a digital adaptive threshold and compressed using a four level discrete wavelet transform (DWT) applied on each AP sample. The utilized detection and the compression strategies were previously presented in detail by our group in Gagnon-Turcotte et al ( 2019 ).…”

Section: Methodsmentioning

confidence: 99%

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper presents the design and the utilization of a wireless electro-optic platform to perform simultaneous multimodal electrophysiological recordings and optogenetic stimulation in freely moving rodents. The developed system can capture neural action potentials (AP), local field potentials (LFP) and electromyography (EMG) signals with up to 32 channels in parallel while providing four optical stimulation channels. The platform is using commercial off-the-shelf components (COTS) and a low-power digital field-programmable gate array (FPGA), to perform digital signal processing to digitally separate in real time the AP, LFP and EMG while performing signal detection and compression for mitigating wireless bandwidth and power consumption limitations. The different signal modalities collected on the 32 channels are time-multiplexed into a single data stream to decrease power consumption and optimize resource utilization. The data reduction strategy is based on signal processing and real-time data compression. Digital filtering, signal detection, and wavelet data compression are used inside the platform to separate the different electrophysiological signal modalities, namely the local field potentials (1–500 Hz), EMG (30–500 Hz), and the action potentials (300–5,000 Hz) and perform data reduction before transmitting the data. The platform achieves a measured data reduction ratio of 7.77 (for a firing rate of 50 AP/second) and weights 4.7 g with a 100-mAh battery, an on/off switch and a protective plastic enclosure. To validate the performance of the platform, we measured distinct electrophysiology signals and performed optogenetics stimulation in vivo in freely moving rondents. We recorded AP and LFP signals with the platform using a 16-microelectrode array implanted in the primary motor cortex of a Long Evans rat, both in anesthetized and freely moving conditions. EMG responses to optogenetic Channelrhodopsin-2 induced activation of motor cortex via optical fiber were also recorded in freely moving rodents.

show abstract

Section: Methodsmentioning

confidence: 99%

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A classic approach to solve a classification/clustering problem is to allocate each data point to the neighboring class with minimal distance. In such methods, the distance/proximity metric could be the Manhattan (l 1 -norm) distance [7], [5] or cosine similarity [12]. K-means [12], [8] and template matching [6] are the distancebased methods widely used for spike sorting (Fig.…”

Section: B State-of-the-art Bmi Socsmentioning

confidence: 99%

“…2(a)). For instance, wavelet features were extracted and used to cluster spikes based on the l 1 -norm distance in [5]. The chip consumed 56.9µW/ch and occupied a silicon area of 0.08mm 2 /ch.…”

Section: B State-of-the-art Bmi Socsmentioning

confidence: 99%

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Shaeri¹,

Afzal²,

Shoaran³

2022

Preprint

View full text Add to dashboard Cite

Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applications), and treat underlying neurological symptoms (therapeutic applications). In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain machine interfaces (BMIs), with a focus on state-of-theart prosthetic BMIs. Major technological challenges for the effectiveness of AI models will be discussed. Finally, we will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.

show abstract

“…Recently, several artefact-free closed-loop battery-powered optogenetics/optoelectrophysiology systems have been introduced [119]- [124]. Some of the strategies that can reduce light artefacts are using graphene electrodes [119], covering the electrode with opaque polyimide [120], or reducing photopotential [121]. Additionally, the battery-powered head-mounted optogenetics closed-loop devices permits majority of the system to stay outside the body.…”

Section: Stimulation and Closed-loop Implantable Neural Devicesmentioning

confidence: 99%

Biointegrated and Wirelessly Powered Implantable Brain Devices: A Review

Das

Moradi

Heidari

2020

IEEE Trans. Biomed. Circuits Syst.

115

View full text Add to dashboard Cite

Implantable neural interfacing devices have added significantly to neural engineering by introducing the lowfrequency oscillations of small populations of neurons known as local field potential as well as high-frequency action potentials of individual neurons. Regardless of the astounding progression as of late, conventional neural modulating system is still incapable to achieve the desired chronic in vivo implantation. The real constraint emerges from mechanical and physical differences between implants and brain tissue that initiates an inflammatory reaction and glial scar formation that reduces the recording and stimulation quality. Furthermore, traditional strategies consisting of rigid and tethered neural devices cause substantial tissue damage and impede the natural behavior of an animal, thus hindering chronic in vivo measurements. Therefore, enabling fully implantable neural devices requires biocompatibility, wireless power/data capability, biointegration using thin and flexible electronics, and chronic recording properties. This article reviews biocompatibility and design approaches for developing biointegrated and wirelessly powered implantable neural devices in animals aimed at long-term neural interfacing and outlines current challenges toward developing the next generation of implantable neural devices.Index Terms-Biocompatibility, biointegration, implantable neural device, mechanical flexibility, wireless power transfer.

show abstract

A Wireless Electro-Optic Headstage With a 0.13-${\mu}$m CMOS Custom Integrated DWT Neural Signal Decoder for Closed-Loop Optogenetics

Cited by 17 publications

References 32 publications

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Biointegrated and Wirelessly Powered Implantable Brain Devices: A Review

Contact Info

Product

Resources

About