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Gagnon-Turcotte, Gabriel

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Gagnon-Turcotte

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Gabriel

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Université Laval. Département de génie électrique et de génie informatique

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ncf11885578

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Voici les éléments 1 - 10 sur 11
  • PublicationRestreint
    A 0.13- μm CMOS SoC for simultaneous multichannel optogenetics and neural recording
    (Institute of Electrical and Electronics Engineers, 2018-09-02) Gagnon-Turcotte, Gabriel; Noormohammadi Khiarak, Mehdi; Ethier, Christian; De Koninck, Yves; Gosselin, Benoit
    This paper presents a 0.13-μm CMOS system-onchip (SoC) for simultaneous multichannel optogenetics and multichannel neural recording in freely moving laboratory animals. This fully integrated system provides 10 multimodal recording channels with analog-to-digital conversion and a four- channel LED driver circuit for optogenetic stimulation. The bio-amplifier design includes a programmable bandwidth (BW) (0.5 Hz–7 kHz) to collect either the action potentials (APs) and/or the local field potentials (LFPs) and has a noise efficiency factor (NEF) of 2.30 for an input-referred noise of 3.2 μVrms within a BW of 10–7 kHz. The low-power delta–sigma () MASH 1-1-1 analog-to-digital converter (ADC) is designed to work at low oversampling ratios (OSRs) (≤50) and has an effective number of bits (ENOB) of 9.75 bits at an OSR of 25 (BW of 10 kHz). The utilization of a ADC is the key to address the flexibility needed to address different noise versus power consumption tradeoff of various experimental settings. It leverages a new technique that reduces its size by subtracting the output of each branch in the digital domain, instead of in the analog domain as done conventionally. The ADC is followed by an on-chip fourthorder cascaded integrator-comb (CIC4) decimation filter (DF). A whole recording channel, including the bio-amplifier, the MASH 1-1-1, and the DF consumes 11.2 μW. Optical stimulation is performed with an LED driver using a regulated cascode current source with feedback that can accommodate a wide range of LED parameters and battery voltages. The SoC is validated in vivo within a wireless experimental platform in both the ventral posteromedial nucleus (VPM) and cerebral motor cortex brain regions of a virally mediated Channelrhodopsin-2. (ChR2) rat.
  • PublicationAccès libre
    Unsupervised domain adversarial self-calibration for electromyography-based gesture recognition
    (IEEE Access, 2020-10-08) Côté Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre; Scheme, Erik J.; Laviolette, François; Gosselin, Benoit
    Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.
  • PublicationAccès 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.
  • PublicationAccès 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.
  • PublicationRestreint
    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.
  • PublicationRestreint
    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.
  • PublicationAccès 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.
  • PublicationRestreint
    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.
  • PublicationAccès 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.
  • PublicationRestreint
    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.