Personne :
Gagnon-Turcotte, Gabriel

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Université Laval. Département de génie électrique et de génie informatique
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Voici les éléments 1 - 10 sur 13
  • Publication
    A wireless electro-optic headstage with a 0.13-μm CMOS customintegrated DWT neural signal decoder for closed-loop optogenetics
    (IEEE, 2019-07-23) Gagnon-Turcotte, Gabriel; Keramidis, Iason; Ethier, Christian; De Koninck, Yves; Gosselin, Benoit
    We present awireless electro-optic headstage that uses a 0.13-μm CMOS custom integrated circuit (IC) implementing a digital neural decoder (ND-IC) for enabling real-time closed-loop (CL) optogenetics. The ND-IC processes the neural activity data using three digital cores: 1) the detector core detects and extracts the action potential (AP) of individual neurons by using an adaptive threshold; 2) the data compression core compresses the detected AP by using an efficient Symmlet-2 discrete wavelet transform (DWT) processor for decreasing the amount of data to be transmitted by the low-power wireless link; and 3) the classification core sorts the compressed AP into separated clusters on the fly according to their wave shapes. The ND-IC encompasses several innovations: 1) the compression core decreases the complexity from O(n2) to O(n· log(n)) compared to the previous solutions, while using two times less memory, thanks to the use of a new coefficient sorting tree; and 2) the AP classification core reuses both the compressed DWT coefficients to perform implicit dimensionality reduction, which allows for performing intensive signal processing on-chip, while increasing power and hardware efficiency. This core also reuses the signal standard deviation already computed by theAPdetector core as threshold for performing automatic AP sorting. The headstage also introduces innovations by enabling a new wireless CL scheme between the neural data acquisition module and the optical stimulator. Our CL scheme uses the AP sorting and timing information produced by the ND-IC for detecting complex firing patternswithin the brain. The headstage is also smaller (1.13 cm3), lighter (3.0 g with a 40mAhbattery) and less invasive than the previous solutions, while providing a measured autonomy of 2h40, with the ND-IC. The whole system and the ND-IC are first validated in vivo in the LD thalamus of a Long-Evans rat, and then in freely-moving CL experiments involving a mouse virally expressing ChR2-mCherry in inhibitory neurons of the prelimbic cortex, and the results show that our system works well within an in vivo experimental setting with a freely moving mouse.
  • Publication
    Accès libre
    A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC
    (Institute of Electrical and Electronics Engineers, 2018-12-13) Gosselin, Benoit; Gagnon-Turcotte, Gabriel; Fall, Cheikh Latyr; Bouyer, Laurent; Mascret, Quentin; Bielmann, Mathieu
    This paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13μm CMOS mixed-signal system-on-chip (SoC) analog frontend circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10x 3 rd order ΔΣ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 μVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 μW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 × 2.0 cm 2 printed circuit board and weights <; 1 g.
  • Publication
    Smart autonomous electro-optic platforms enabling innovative brain therapies
    (Institute of Electrical and Electronics Engineers, 2020-11-12) Gagnon-Turcotte, Gabriel; Bilodeau, Guillaume; Tsiakaka, Olivier; Gosselin, Benoit
    The future of brain research lies in the application of new technologies drawing from the latest developments in biology, physics and engineering to advance our understanding of how this complex organ processes, integrates and transfers information. Among these, optogenetics is a groundbreaking technology that allows using light to selectively activate neurons in the cortex of transgenic animals, usually mice, to observe its effect in large biological networks. A new research paradigm drawing from these advances consists of synchronizing optogenetic stimulation with electrophysiology recordings, to close the loop and to regulate the neural microcircuits, or to repair them. Such an approach holds promise to accelerate the development of new therapeutics against brain diseases by enabling entirely new experimental research scenarios with freely behaving animal models. As a result, the development of advanced wireless microelectronic implantable systems to elicit, ex tract and process brain data in real time has become a source of significant interest. This paper reviews the design challenges and the state-of-the- art technology in this field. We present the design of a complete electro-optic device for preforming optogenetics and multichannel electrophysiology in a closed-loop (CL) system with live neurons. We cover the design of the different CMOS integrated building blocks involved in this system to perform photostimulation and multichannel neural recording in parallel. We describe advanced hardware strategies to perform action potential (AP) detection, neural data compression and AP sorting in real-time, over several parallel recording channels for enabling real-time CL neural control. Finally, we present CL experimental results obtained in vivo with an electro-optic prototype.
  • Publication
    Accès libre
    A low-cost, wireless, 3-D-printed custom armband for sEMG hand gesture recognition
    (MDPI, 2019-06-24) Côté Allard, Ulysse; Gagnon-Turcotte, Gabriel; Laviolette, François; Gosselin, Benoit
    Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost ( 150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (p < 0.05) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.
  • Publication
    A wireless headstage for combined optogenetics and multichannel electrophysiological recording
    (IEEE, 2016-06-20) Gagnon-Turcotte, Gabriel; LeChasseur, Yoan; Bories, Cyril; Messaddeq, Younès; De Koninck, Yves; Gosselin, Benoit
    This paper presents a wireless headstage with realtime spike detection and data compression for combined optogenetics and multichannel electrophysiological recording. The proposed headstage, which is intended to perform both optical stimulation and electrophysiological recordings simultaneously in freely moving transgenic rodents, is entirely built with commercial off-the-shelf components, and includes 32 recording channels and 32 optical stimulation channels. It can detect, compress and transmit full action potential waveforms over 32 channels in parallel and in real time using an embedded digital signal processor based on a low-power field programmable gate array and a Microblaze microprocessor softcore. Such a processor implements a complete digital spike detector featuring a novel adaptive threshold based on a Sigma-delta control loop, and a wavelet data compression module using a new dynamic coefficient re-quantization technique achieving large compression ratios with higher signal quality. Simultaneous optical stimulation and recording have been performed in-vivo using an optrode featuring 8 microelectrodes and 1 implantable fiber coupled to a 465-nm LED, in the somatosensory cortex and the Hippocampus of a transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line 4) under anesthetized conditions. Experimental results show that the proposed headstage can trigger neuron activity while collecting, detecting and compressing single cell microvolt amplitude activity from multiple channels in parallel while achieving overall compression ratios above 500. This is the first reported high-channel count wireless optogenetic device providing simultaneous optical stimulation and recording. Measured characteristics show that the proposed headstage can achieve up to 100% of true positive detection rate for signal-to-noise ratio (SNR) down to 15 dB, while achieving up to 97.28% at SNR as low as 5 dB. The implemented prototype features a lifespan of up to 105 minutes, and uses a lightweight (2.8 g) and compact (17 × 18 × 10 mm3) rigid-flex printed circuit board.
  • Publication
    Accès libre
    Interfaces neuronales CMOS haute résolution pour l'électrophysiologie et l'optogénétique en boucle fermée
    (2019) Gagnon-Turcotte, Gabriel; Gosselin, Benoit
    L’avenir de la recherche sur les maladies du cerveau repose sur le développement de nouvelles technologies qui permettront de comprendre comment cet organe si complexe traite, intègre et transfère l’information. Parmi celles-ci, l’optogénétique est une technologie révolutionnaire qui permet d’utiliser de la lumière afin d’activer sélectivement les neurones du cortex d’animaux transgéniques pour observer leur effet dans un vaste réseau biologique. Ce cadre expérimental repose typiquement sur l’observation de l’activité neuronale de souris transgéniques, car elles peuvent exprimer une grande variété de gènes et de maladies et qu’elles sont peu couteuses. Toutefois, la plupart des appareils de mesure ou de stimulation optogénétique disponible ne sont pas appropriés, car ils sont câblés, trop lourds et/ou trop simplistes. Malheureusement, peu de systèmes sans fil existent, et ces derniers sont grandement limités par la bande passante requise pour transmettre les données neuronales, et ils ne fournissent pas de stimulation optogénétique multicanal afin de stimuler et observer plusieurs régions du cerveau. Dans les dispositifs actuels, l’interprétation des données neuronales est effectuée ex situ, alors que la recherche bénéficierait grandement de systèmes sans fil assez intelligents pour interpréter et stimuler les neurones en boucle fermée, in situ. Le but de ce projet de recherche est de concevoir des circuits analogiques-numériques d’acquisition et de traitement des signaux neuronaux, des algorithmes d’analyse et de traitement de ces signaux et des systèmes electro-optiques miniatures et sans fil pour : i) Mener des expériences combinant l’enregistrement neuronal et l’optogénétique multicanal haute résolution avec des animaux libres de leurs mouvements. ii) Mener des expériences optogénétiques synchronisées avec l’observation, c.-à-d. en boucle fermée, chez des animaux libres de leurs mouvements. iii) Réduire la taille, le poids et la consommation énergétique des systèmes optogénétiques sans fil afin de minimiser l’impact de la recherche chez de petits animaux. Ce projet est en 3 phases, et ses principales contributions ont été rapportées dans dix conférences internationales (ISSCC, ISCAS, EMBC, etc.) et quatre articles de journaux publiés ou soumis, ainsi que dans un brevet et deux divulgations. La conception d’un système optogénétique haute résolution pose plusieurs défis importants. Notamment, puisque les signaux neuronaux ont un contenu fréquentiel élevé (_10 kHz), le nombre de canaux sous observation est limité par la bande passante des transmetteurs sans fil (2-4 canaux en général). Ainsi, la première phase du projet a visé le développement d’algorithmes de compression des signaux neuronaux et leur intégration dans un système optogénétique sans fil miniature et léger (2.8 g) haute résolution possédant 32 canaux d’acquisition et 32 canaux de stimulation optique. Le système détecte, compresse et transmet les formes d’onde des potentiels d’action (PA) produits par les neurones avec un field programmable gate array (FPGA) embarqué à faible consommation énergétique. Ce processeur implémente un algorithme de détection des PAs basé sur un seuillage adaptatif, ce qui permet de compresser les signaux en transmettant seulement les formes détectées. Chaque PA est davantage compressé par une transformée en ondelette discrète (DWT) de type Symmlet-2 suivie d’une technique de discrimination et de requantification dynamique des coefficients. Les résultats obtenus démontrent que cet algorithme est plus robuste que les méthodes existantes tout en permettant de reconstruire les signaux compressés avec une meilleure qualité (SNDR moyen de 25 dB _ 5% pour un taux de compression (CR) de 4.2). Avec la détection, des CR supérieurs à 500 sont rapportés lors de la validation in vivo. L’utilisation de composantes commerciales dans des systèmes optogénétiques sans fil augmente
  • Publication
    Accès libre
    A wireless sEMG-based body-machine interface for assistive technology devices
    (Institute of Electrical and Electronics Engineer, 2016-12-21) Gosselin, Benoit; Gagnon-Turcotte, Gabriel; Gosselin, Clément; Campeau-Lecours, Alexandre; Fall, Cheikh Latyr; Gagné, Jean Simon; Delisle, Yanick; Rioux-Dubé, Jean-François
    Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.
  • Publication
    A 0.13μm CMOS SoC for simultaneous multichannel optogenetics and electrophysiological brain recording
    (IEEE, 2018-03-12) Gagnon-Turcotte, Gabriel; Ethier, Christian; De Koninck, Yves; Gosselin, Benoit
    Optogenetics and multi-unit electrophysiological recording are state-of-the-art approaches in neuroscience to observe neural microcircuits in vivo [1]. Thereby, brain-implantable devices incorporating optical stimulation and low-noise data acquisition means have been designed based on custom integrated circuits (IC) to study the brain of small freely behaving laboratory animals. However, no existing IC provides multichannel optogenetic photo-stimulation along with multiunit electrophysiological recording capability within the same die [2-5]. They also lack critical features: they are not multichannel and/or do not include an ADC [6], or they address only one signal modality [5-6], i.e., either local field potentials (LFP) or action potentials (AP). In this paper, we report an IC for simultaneous multichannel optogenetics and electrophysiological recording addressing both LFP and AP signals all at once. This 0.13μm CMOS chip, which includes 4/10 stimulation/recording channels, is enclosed inside a small wireless optogenetic platform, and is demonstrated with simultaneous in vivo optical stimulation and electrophysiological recordings with a virally mediated Channelrhodopsin-2 (ChR2) rat.
  • Publication
    Accès libre
    A transferable adaptive domain adversarial neural network for virtual reality augmented EMG-Based gesture recognition
    (IEEE Xplore, 2021-02-16) Côté Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre; Scheme, Erik; Laviolette, François; Gosselin, Benoit
    Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique.
  • Publication
    Accès libre
    A wireless electro-optic platform for multimodal electrophysiology and optogenetics in freely moving rodents
    (Frontiers Media S.A., 2021-08-16) Bilodeau, Guillaume; Gagnon-Turcotte, Gabriel; L. Gagnon, Léonard; Keramidis, Iason; De Koninck, Yves; Ethier, Christian; Gosselin, Benoit; Timofeev, Igor
    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.