@article{pmid40404959,

title = {The Tailtag: A multi-mouse tracking system to measure social dynamics in complex environments},

author = {Vincent Coulombe and Arturo Marroquin Rivera and Sadegh Monfared and David-Alexandre Roussel and Quentin Leboulleux and Modesto R Peralta and Benoit Gosselin and Benoit Labonté},

doi = {10.1038/s41386-025-02126-y},

issn = {1740-634X},

year  = {2025},

date = {2025-08-01},

journal = {Neuropsychopharmacology},

volume = {50},

number = {9},

pages = {1336--1345},

abstract = {Despite recent advances, tracking individual movements safely and reliably over extended periods, particularly within complex social groups, remains a challenge. Traditional methods like color coding, tagging, and RFID tracking, while effective, have notable practical limitations. State-of-the-art neural network-based trackers often struggle to maintain individual identities in large groups for more than a few seconds. Fiducial tags such as ArUco codes present a potential solution to enable accurate tracking and identity management. However, their application to large groups of socially interacting mice in complex, enriched environments remain an open challenge. Here, we present the Tailtag system, a novel approach designed to address this challenge. The Tailtag is a non-invasive, safe, and ergonomic tail ring embedded with an ArUco marker allowing to track individual mice in colonies of up to 20 individuals in complex environments for at least seven days without performance degradation or behavioral alteration. We provide a comprehensive parameter optimization guide and practical recommendations for marker selection, for reproducibility across diverse experimental setups. Using data collected from Tailtag-equipped mice, we revealed the formation and evolution of social groups within the colony. Our analysis identified social hub regions within the vivarium where social contacts occur at different frequencies throughout one week of recordings. We quantified interactions and avoidance patterns between specific pairs of mice within the most active social hubs. Overall, our findings indicate that while the zone preferences and peer associations among the mice change over time, certain groups and pairwise interactions consistently form within the social colony.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40218833,

title = {Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm and an Ultra-High Input Impedance},

author = {Esmaeil Ranjbar Koleibi and William Lemaire and Konin Koua and Maher Benhouria and Reza Bostani and Mahziar Serri Mazandarani and Luis-Philip Gauthier and Marwan Besrour and Jérémy Ménard and Mahdi Majdoub and Benoit Gosselin and Sébastien Roy and Réjean Fontaine},

doi = {10.3390/s25072320},

issn = {1424-8220},

year  = {2025},

date = {2025-04-01},

journal = {Sensors (Basel)},

volume = {25},

number = {7},

abstract = {Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a -3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μVrms, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μm2, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39065908,

title = {Potential of a New, Flexible Electrode sEMG System in Detecting Electromyographic Activation in Low Back Muscles during Clinical Tests: A Pilot Study on Wearables for Pain Management},

author = {Antoine Frasie and Hugo Massé-Alarie and Mathieu Bielmann and Nicolas Gauthier and Mourad Roudjane and Isabelle Pagé and Benoit Gosselin and Jean-Sébastien Roy and Younes Messaddeq and Laurent J Bouyer},

doi = {10.3390/s24144510},

issn = {1424-8220},

year  = {2024},

date = {2024-07-01},

journal = {Sensors (Basel)},

volume = {24},

number = {14},

abstract = {BACKGROUND: While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering-Sorensen test) and abnormal muscle activation patterns (i.e., the flexion-extension test). Surface electromyography (sEMG) provides objective measures of muscle fatigue development (median frequency drop, MDF) and activation patterns (RMS amplitude change). This study therefore assessed the sensitivity and validity of a novel and flexible sEMG system (NSS) based on PEVA electrodes and potentially embeddable in textiles, as a tool for objective clinical LBP assessment.nnMETHODS: Twelve participants wearing NSS and a commercial laboratory sEMG system (CSS) performed two clinical tests used in LBP assessment (Biering-Sorensen and flexion-extension). Erector spinae muscle activity was recorded at T12-L1 and L4-L5.nnRESULTS: NSS showed sensitivity to sEMG changes associated with fatigue development and muscle activations during flexion-extension movements ( < 0.05) that were similar to CSS ( > 0.05). Raw signals showed moderate cross-correlations (MDF: 0.60-0.68; RMS: 0.53-0.62). Adding conductive gel to the PEVA electrodes did not influence sEMG signal interpretation ( > 0.05).nnCONCLUSIONS: This novel sEMG system is promising for assessing electrophysiological indicators of LBP during clinical tests.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37475396,

title = {Improving flat fluorescence microscopy in scattering tissue through deep learning strategies},

author = {Mohamad Feshki and Sylvain Martel and Yves De Koninck and Benoit Gosselin},

doi = {10.1364/OE.489677},

issn = {1094-4087},

year  = {2023},

date = {2023-07-01},

journal = {Opt Express},

volume = {31},

number = {14},

pages = {23008--23026},

abstract = {Intravital microscopy in small animals growingly contributes to the visualization of short- and long-term mammalian biological processes. Miniaturized fluorescence microscopy has revolutionized the observation of live animals' neural circuits. The technology's ability to further miniaturize to improve freely moving experimental settings is limited by its standard lens-based layout. Typical miniature microscope designs contain a stack of heavy and bulky optical components adjusted at relatively long distances. Computational lensless microscopy can overcome this limitation by replacing the lenses with a simple thin mask. Among other critical applications, Flat Fluorescence Microscope (FFM) holds promise to allow for real-time brain circuits imaging in freely moving animals, but recent research reports show that the quality needs to be improved, compared with imaging in clear tissue, for instance. Although promising results were reported with mask-based fluorescence microscopes in clear tissues, the impact of light scattering in biological tissue remains a major challenge. The outstanding performance of deep learning (DL) networks in computational flat cameras and imaging through scattering media studies motivates the development of deep learning models for FFMs. Our holistic ray-tracing and Monte Carlo FFM computational model assisted us in evaluating deep scattering medium imaging with DL techniques. We demonstrate that physics-based DL models combined with the classical reconstruction technique of the alternating direction method of multipliers (ADMM) perform a fast and robust image reconstruction, particularly in the scattering medium. The structural similarity indexes of the reconstructed images in scattering media recordings were increased by up to 20% compared with the prevalent iterative models. We also introduce and discuss the challenges of DL approaches for FFMs under physics-informed supervised and unsupervised learning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37386229,

title = {Portable impedance-sensing device for microorganism characterization in the field},

author = {Karim Bouzid and Jesse Greener and Sandro Carrara and Benoit Gosselin},

doi = {10.1038/s41598-023-37506-1},

issn = {2045-2322},

year  = {2023},

date = {2023-06-01},

journal = {Sci Rep},

volume = {13},

number = {1},

pages = {10526},

abstract = {A variety of biosensors have been proposed to quickly detect and measure the properties of individual microorganisms among heterogeneous populations, but challenges related to cost, portability, stability, sensitivity, and power consumption limit their applicability. This study proposes a portable microfluidic device based on impedance flow-cytometry and electrical impedance spectroscopy that can detect and quantify the size of microparticles larger than 45 µm, such as algae and microplastics. The system is low cost ($300), portable (5 cm [Formula: see text] 5 cm), low-power (1.2 W), and easily fabricated utilizing a 3D-printer and industrial printed circuit board technology. The main novelty we demonstrate is the use of square wave excitation signal for impedance measurements with quadrature phase-sensitive detectors. A linked algorithm removes the errors associated to higher order harmonics. After validating the performance of the device for complex impedance models, we used it to detect and differentiate between polyethylene microbeads of sizes between 63 and 83 µm, and buccal cells between 45 and 70 µm. A precision of 3% is reported for the measured impedance and a minimum size of 45 µm is reported for the particle characterization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36935753,

title = {Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied},

author = {Guillaume Weber-Boisvert and Benoit Gosselin and Frida Sandberg},

doi = {10.3389/fphys.2023.1126957},

issn = {1664-042X},

year  = {2023},

date = {2023-01-01},

journal = {Front Physiol},

volume = {14},

pages = {1126957},

abstract = {The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant  differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation  to systolic BP in PPG-BP all displayed muted correlation levels  in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34050220,

title = {Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning},

author = {Simon Tam and Mounir Boukadoum and Alexandre Campeau-Lecours and Benoit Gosselin},

doi = {10.1038/s41598-021-90688-4},

issn = {2045-2322},

year  = {2021},

date = {2021-05-01},

journal = {Sci Rep},

volume = {11},

number = {1},

pages = {11275},

abstract = {Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34504415,

title = {A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents},

author = {Guillaume Bilodeau and Gabriel Gagnon-Turcotte and Léonard L Gagnon and Iason Keramidis and Igor Timofeev and Yves De Koninck and Christian Ethier and Benoit Gosselin},

doi = {10.3389/fnins.2021.718478},

issn = {1662-4548},

year  = {2021},

date = {2021-01-01},

journal = {Front Neurosci},

volume = {15},

pages = {718478},

abstract = {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 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  optical fiber were also recorded in freely moving rodents.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34149347,

title = {Low-Cutoff Frequency Reduction in Neural Amplifiers: Analysis and Implementation in CMOS 65 nm},

author = {Fereidoon Hashemi Noshahr and Morteza Nabavi and Benoit Gosselin and Mohamad Sawan},

doi = {10.3389/fnins.2021.667846},

issn = {1662-4548},

year  = {2021},

date = {2021-01-01},

journal = {Front Neurosci},

volume = {15},

pages = {667846},

abstract = {Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cutoff frequency due to the short-channel effects. To improve the low-cutoff frequency, one solution is to increase the feedback capacitors' value. This solution is not desirable, as the input capacitors have to be increased to maintain the same gain, which increases the area and decreases the input impedance of the neural amplifier. We analytically analyze the small-signal behavior of the neural amplifier and prove that the main reason for the increase of the low-cutoff frequency in advanced CMOS technologies is the reduction of the input resistance of the operational transconductance amplifier (OTA). We also show that the reduction of the input resistance of the OTA is due to the increase in the gate oxide leakage in the input transistors. In this paper, we explore this fact and propose two solutions to reduce the low-cutoff frequency without increasing the value of the feedback capacitor. The first solution is performed by only simulation and is called cross-coupled positive feedback that uses pseudoresistors to provide a negative resistance to increase the input resistance of the OTA. As an advantage, only standard CMOS transistors are used in this method. Simulation results show that a low-cutoff frequency of 1.5 Hz is achieved while the midband gain is 30.4 dB at 1 V. In addition, the power consumption is 0.6 μW. In the second method, we utilize thick-oxide MOS transistors in the input differential pair of the OTA. We designed and fabricated the second method in the 65 nm TSMC CMOS process. Measured results are obtained by  recordings on slices of mouse brainstem. The measurement results show that the bandwidth is between 2 Hz and 5.6 kHz. The neural amplifier has 34.3 dB voltage gain in midband and consumes 3.63 μ at 1 V power supply. The measurement results show an input-referred noise of 6.1 μ  and occupy 0.04   silicon area.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32532116,

title = {Source-Detector Spectral Pairing-Related Inaccuracies in Pulse Oximetry: Evaluation of the Wavelength Shift},

author = {Olivier Tsiakaka and Benoit Gosselin and Sylvain Feruglio},

doi = {10.3390/s20113302},

issn = {1424-8220},

year  = {2020},

date = {2020-06-01},

journal = {Sensors (Basel)},

volume = {20},

number = {11},

abstract = {Pulse oximetry enables oxygen saturation estimation ( S p O 2) non-invasively in real time with few components and modest processing power. With the advent of affordable development kits dedicated to the monitoring of biosignals, capabilities once reserved to hospitals and high-end research laboratories are becoming accessible for rapid prototyping. While one may think that medical-grade equipment differs greatly in quality, surprisingly, we found that the performance requirements are not widely different from available consumer-grade components, especially regarding the photodetection module in pulse oximetry. This study investigates how the use of candidate light sources and photodetectors for the development of a custom S p O 2 monitoring system can lead to inaccuracies when using the standard computational model for oxygen saturation without calibration. Following the optical characterization of selected light sources, we compare the extracted parameters to the key features in their respective datasheet. We then quantify the wavelength shift caused by spectral pairing of light sources in association with photodetectors. Finally, using the widely used approximation, we report the resulting absolute error in S p O 2 estimation and show that it can lead up to 8% of the critical 90-100% saturation window.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}