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BRAND: a platform for closed-loop experiments with deep network models J. Neural Eng. (IF 4.0) Pub Date : 2024-04-17 Yahia H Ali, Kevin Bodkin, Mattia Rigotti-Thompson, Kushant Patel, Nicholas S Card, Bareesh Bhaduri, Samuel R Nason-Tomaszewski, Domenick M Mifsud, Xianda Hou, Claire Nicolas, Shane Allcroft, Leigh R Hochberg, Nicholas Au Yong, Sergey D Stavisky, Lee E Miller, David M Brandman, Chethan Pandarinath
Objective. Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support
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Towards ASSR-based hearing assessment using natural sounds J. Neural Eng. (IF 4.0) Pub Date : 2024-04-17 Anna Sergeeva, Christian Bech Christensen, Preben Kidmose
Objective. The auditory steady-state response (ASSR) allows estimation of hearing thresholds. The ASSR can be estimated from electroencephalography (EEG) recordings from electrodes positioned on both the scalp and within the ear (ear-EEG). Ear-EEG can potentially be integrated into hearing aids, which would enable automatic fitting of the hearing device in daily life. The conventional stimuli for ASSR-based
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Peripheral direct current reduces naturally evoked nociceptive activity at the spinal cord in rodent models of pain J. Neural Eng. (IF 4.0) Pub Date : 2024-04-17 Tom F Su, Jack D Hamilton, Yiru Guo, Jason R Potas, Mohit N Shivdasani, Gila Moalem-Taylor, Gene Y Fridman, Felix P Aplin
Objective. Electrical neuromodulation is an established non-pharmacological treatment for chronic pain. However, existing devices using pulsatile stimulation typically inhibit pain pathways indirectly and are not suitable for all types of chronic pain. Direct current (DC) stimulation is a recently developed technology which affects small-diameter fibres more strongly than pulsatile stimulation. Since
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MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition J. Neural Eng. (IF 4.0) Pub Date : 2024-04-17 Guangqiang Li, Ning Chen, Yixiang Niu, Zhangyong Xu, Yuxuan Dong, Jing Jin, Hongqin Zhu
Objective. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model. Approach. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition
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Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury J. Neural Eng. (IF 4.0) Pub Date : 2024-04-15 Marvin Wolf, Rüdiger Rupp, Andreas Schwarz
Objective. Chronic motor impairments of arms and hands as the consequence of a cervical spinal cord injury (SCI) have a tremendous impact on activities of daily life. A considerable number of people however retain minimal voluntary motor control in the paralyzed parts of the upper limbs that are measurable by electromyography (EMG) and inertial measurement units (IMUs). An integration into human-machine
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Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies J. Neural Eng. (IF 4.0) Pub Date : 2024-04-15 Simone Tanzarella, Dario Di Domenico, Inna Forsiuk, Nicolò Boccardo, Michela Chiappalone, Chiara Bartolozzi, Marianna Semprini
Objective. We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control. Approach. Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes
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Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method J. Neural Eng. (IF 4.0) Pub Date : 2024-04-11 Zhengyuan Lv, Jingming Li, Li Yao, Xiaojuan Guo
Objective. Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation
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Cross-modal credibility modelling for EEG-based multimodal emotion recognition J. Neural Eng. (IF 4.0) Pub Date : 2024-04-11 Yuzhe Zhang, Huan Liu, Di Wang, Dalin Zhang, Tianyu Lou, Qinghua Zheng, Chai Quek
Objective. The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities,
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Computational modeling of dorsal root ganglion stimulation using an Injectrode J. Neural Eng. (IF 4.0) Pub Date : 2024-04-11 Sauradeep Bhowmick, Robert D Graham, Nishant Verma, James K Trevathan, Manfred Franke, Stephan Nieuwoudt, Lee E Fisher, Andrew J Shoffstall, Douglas J Weber, Kip A Ludwig, Scott F Lempka
Objective. Minimally invasive neuromodulation therapies like the Injectrode, which is composed of a tightly wound polymer-coated Platinum/Iridium microcoil, offer a low-risk approach for administering electrical stimulation to the dorsal root ganglion (DRG). This flexible electrode is aimed to conform to the DRG. The stimulation occurs through a transcutaneous electrical stimulation (TES) patch, which
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Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition J. Neural Eng. (IF 4.0) Pub Date : 2024-04-11 Wenjie Li, Haoyu Li, Xinlin Sun, Huicong Kang, Shan An, Guoxin Wang, Zhongke Gao
Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore
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Avoidance of axonal stimulation with sinusoidal epiretinal stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-04-10 Andrea Corna, Andreea-Elena Cojocaru, Mai Thu Bui, Paul Werginz, Günther Zeck
Objective. Neuromodulation, particularly electrical stimulation, necessitates high spatial resolution to achieve artificial vision with high acuity. In epiretinal implants, this is hindered by the undesired activation of distal axons. Here, we investigate focal and axonal activation of retinal ganglion cells (RGCs) in epiretinal configuration for different sinusoidal stimulation frequencies. Approach
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Gaze-contingent processing improves mobility, scene recognition and visual search in simulated head-steered prosthetic vision J. Neural Eng. (IF 4.0) Pub Date : 2024-04-10 Jaap de Ruyter van Steveninck, Mo Nipshagen, Marcel van Gerven, Umut Güçlü, Yağmur Güçlüturk, Richard van Wezel
Objective. The enabling technology of visual prosthetics for the blind is making rapid progress. However, there are still uncertainties regarding the functional outcomes, which can depend on many design choices in the development. In visual prostheses with a head-mounted camera, a particularly challenging question is how to deal with the gaze-locked visual percept associated with spatial updating conflicts
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Variability of pulse width in transcranial magnetic stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-04-10 Mirja Osnabruegge, Carolina Kanig, Stefan Schoisswohl, Karsten Litschel, Wolfgang Mack, Martin Schecklmann, Berthold Langguth, Florian Schwitzgebel
Objective. There is a high variability in the physiological effects of transcranial magnetic brain stimulation, resulting in limited generalizability of measurements. The cause of the variability is assumed to be primarily based on differences in brain function and structure of the stimulated individuals, while the variability of the physical properties of the magnetic stimulus has so far been largely
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Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex J. Neural Eng. (IF 4.0) Pub Date : 2024-04-09 Naofumi Suematsu, Alberto L Vazquez, Takashi D Y Kozai
Objective. Intracortical microstimulation (ICMS) can be an effective method for restoring sensory perception in contemporary brain–machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site. Approach. Different microstimulation
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Transferable non-invasive modal fusion-transformer (NIMFT) for end-to-end hand gesture recognition J. Neural Eng. (IF 4.0) Pub Date : 2024-04-09 Tianxiang Xu, Kunkun Zhao, Yuxiang Hu, Liang Li, Wei Wang, Fulin Wang, Yuxuan Zhou, Jianqing Li
Objective. Recent studies have shown that integrating inertial measurement unit (IMU) signals with surface electromyographic (sEMG) can greatly improve hand gesture recognition (HGR) performance in applications such as prosthetic control and rehabilitation training. However, current deep learning models for multimodal HGR encounter difficulties in invasive modal fusion, complex feature extraction from
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Orthogonal extended infomax algorithm J. Neural Eng. (IF 4.0) Pub Date : 2024-04-09 Nicole Ille
Objective. The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster. Approach. Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax
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Axonal stimulation affects the linear summation of single-point perception in three Argus II users J. Neural Eng. (IF 4.0) Pub Date : 2024-04-08 Yuchen Hou, Devyani Nanduri, Jacob Granley, James D Weiland, Michael Beyeler
Objective. Retinal implants use electrical stimulation to elicit perceived flashes of light (‘phosphenes’). Single-electrode phosphene shape has been shown to vary systematically with stimulus parameters and the retinal location of the stimulating electrode, due to incidental activation of passing nerve fiber bundles. However, this knowledge has yet to be extended to paired-electrode stimulation. Approach
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Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays J. Neural Eng. (IF 4.0) Pub Date : 2024-04-05 Ingrid N McNamara, Steven M Wellman, Lehong Li, James R Eles, Sajishnu Savya, Harbaljit S Sohal, Matthew R Angle, Takashi D Y Kozai
Objective. Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics’ high-density fine microwire arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated
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Comparison of the activation level in the sensorimotor cortex between motor point and proximal nerve bundle electrical stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-04-05 Rui Yuan, Yu Peng, Run Ji, Yang Zheng
Objective. Neuromuscular electrical stimulation (NMES) is widely used for motor function rehabilitation in stroke survivors. Compared with the conventional motor point (MP) stimulation, the stimulation at the proximal segment of the peripheral nerve (PN) bundles has been demonstrated to have multiple advantages. However, it is not known yet whether the PN stimulation can increase the cortical activation
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A personalized earbud for non-invasive long-term EEG monitoring J. Neural Eng. (IF 4.0) Pub Date : 2024-04-04 Mahmoud Zeydabadinezhad, Jon Jowers, Derek Buhl, Brian Cabaniss, Babak Mahmoudi
Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities. Approach
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The geometry of photopolymerized topography influences neurite pathfinding by directing growth cone morphology and migration J. Neural Eng. (IF 4.0) Pub Date : 2024-04-04 Joseph T Vecchi, Madeline Rhomberg, C Allan Guymon, Marlan R Hansen
Objective. Cochlear implants provide auditory perception to those with severe to profound sensorineural hearing loss: however, the quality of sound perceived by users does not approximate natural hearing. This limitation is due in part to the large physical gap between the stimulating electrodes and their target neurons. Therefore, directing the controlled outgrowth of processes from spiral ganglion
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Preparation of PLCL/ECM nerve conduits by electrostatic spinning technique and evaluation in vitro and in vivo J. Neural Eng. (IF 4.0) Pub Date : 2024-04-04 Yizhan Ma, Runze Zhang, Xiaoyan Mao, Xiaoming Li, Ting Li, Fang Liang, Jing He, Lili Wen, Weizuo Wang, Xiao Li, Yanhui Zhang, Honghao Yu, Binhan Lu, Tianhao Yu, Qiang Ao
Objective. Artificial nerve scaffolds composed of polymers have attracted great attention as an alternative for autologous nerve grafts recently. Due to their poor bioactivity, satisfactory nerve repair could not be achieved. To solve this problem, we introduced extracellular matrix (ECM) to optimize the materials. Approach. In this study, the ECM extracted from porcine nerves was mixed with Poly(
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Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus J. Neural Eng. (IF 4.0) Pub Date : 2024-04-04 Zhaoxiang Wang, Zhouyan Feng, Yue Yuan, Zheshan Guo, Jian Cui, Tianzi Jiang
Objective. The development of electrical pulse stimulations in brain, including deep brain stimulation, is promising for treating various brain diseases. However, the mechanisms of brain stimulations are not yet fully understood. Previous studies have shown that the commonly used high-frequency stimulation (HFS) can increase the firing of neurons and modulate the pattern of neuronal firing. Because
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Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces J. Neural Eng. (IF 4.0) Pub Date : 2024-04-04 Joana Soldado-Magraner, Alberto Antonietti, Jennifer French, Nathan Higgins, Michael J Young, Denis Larrivee, Rebecca Monteleone
Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical
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Robot-assisted support combined with electrical stimulation for the lower extremity in stroke patients: a systematic review J. Neural Eng. (IF 4.0) Pub Date : 2024-04-03 C J H Rikhof, Y Feenstra, J F M Fleuren, J H Buurke, E C Prinsen, J S Rietman, G B Prange-Lasonder
Objective. The incidence of stroke rising, leading to an increased demand for rehabilitation services. Literature has consistently shown that early and intensive rehabilitation is beneficial for stroke patients. Robot-assisted devices have been extensively studied in this context, as they have the potential to increase the frequency of therapy sessions and thereby the intensity. Robot-assisted systems
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Acute to long-term characteristics of impedance recordings during neurostimulation in humans J. Neural Eng. (IF 4.0) Pub Date : 2024-04-03 Jie Cui, Filip Mivalt, Vladimir Sladky, Jiwon Kim, Thomas J Richner, Brian N Lundstrom, Jamie J Van Gompel, Hai-long Wang, Kai J Miller, Nicholas Gregg, Long Jun Wu, Timothy Denison, Bailey Winter, Benjamin H Brinkmann, Vaclav Kremen, Gregory A Worrell
Objective. This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus, and posterior hippocampus over an extended period. Approach. Impedance was periodically sampled every 5–15 min over several months in five subjects with drug-resistant epilepsy using an investigational neuromodulation device
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intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics J. Neural Eng. (IF 4.0) Pub Date : 2024-04-03 Zhihe Zhao, Sina Shirinpour, Harry Tran, Miles Wischnewski, Alexander Opitz
Objective. Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local neural oscillatory power. Despite its increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Approach. We developed a computational neuronal network model of two-compartment pyramidal
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Galvanic vs. pulsatile effects on decision-making networks: reshaping the neural activation landscape J. Neural Eng. (IF 4.0) Pub Date : 2024-04-03 Paul W Adkisson, Cynthia R Steinhardt, Gene Y Fridman
Objective. Primarily due to safety concerns, biphasic pulsatile stimulation (PS) is the present standard for electrical excitation of neural tissue with a diverse set of applications. While pulses have been shown to be effective to achieve functional outcomes, they have well-known deficits. Due to recent technical advances, galvanic stimulation (GS), delivery of current for extended periods of time
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Online Bayesian optimization of vagus nerve stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-04-02 Lorenz Wernisch, Tristan Edwards, Antonin Berthon, Olivier Tessier-Lariviere, Elvijs Sarkans, Myrta Stoukidi, Pascal Fortier-Poisson, Max Pinkney, Michael Thornton, Catherine Hanley, Susannah Lee, Joel Jennings, Ben Appleton, Phillip Garsed, Bret Patterson, Will Buttinger, Samuel Gonshaw, Matjaž Jakopec, Sudhakaran Shunmugam, Jorin Mamen, Aleksi Tukiainen, Guillaume Lajoie, Oliver Armitage, Emil Hewage
Objective. In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating
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A calibration-free c-VEP based BCI employing narrow-band random sequences J. Neural Eng. (IF 4.0) Pub Date : 2024-04-02 Li Zheng, Yida Dong, Sen Tian, Weihua Pei, Xiaorong Gao, Yijun Wang
Objective. Code-modulated visual evoked potential (c-VEP) based brain–computer interfaces (BCIs) exhibit high encoding efficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularly when the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI
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Bionic blink improves real-time eye closure in unilateral facial paralysis J. Neural Eng. (IF 4.0) Pub Date : 2024-04-02 Mar Cervera-Negueruela, Lauren Chee, Andrea Cimolato, Giacomo Valle, Markus Tschopp, Marcel Menke, Anthia Papazoglou, Stanisa Raspopovic
Facial paralysis is the inability to move facial muscles thereby impairing the ability to blink and make facial expressions. Depending on the localization of the nerve malfunction it is subcategorised into central or peripheral and is usually unilateral. This leads to health deficits stemming from corneal dryness and social ostracization. Objective: Electrical stimulation shows promise as a method
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Functional brain network controllability dysfunction in Alzheimer’s disease and its relationship with cognition and gene expression profiling J. Neural Eng. (IF 4.0) Pub Date : 2024-03-28 Chuchu Zheng, Xiaoxia Xiao, Wei Zhao, Zeyu Yang, Shuixia Guo, for theAlzheimer’s Disease Neuroimaging Initiative4
Objective. In recent studies, network control theory has been applied to clarify transitions between brain states, emphasizing the significance of assessing the controllability of brain networks in facilitating transitions from one state to another. Despite these advancements, the potential alterations in functional network controllability associated with Alzheimer’s disease (AD), along with the underlying
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AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling J. Neural Eng. (IF 4.0) Pub Date : 2024-03-28 David A Lloyd, Maria Alejandra Gonzalez-Gonzalez, Mario I Romero-Ortega
Objective. Bioelectronic treatments targeting near-organ innervation have unprecedented clinical applications. Particularly in the spleen, the inhibition of the cholinergic inflammatory response by near-organ nerve stimulation has potential to replace pharmacological treatments in chronic and autoimmune diseases. A caveat is that the optimization of therapeutic stimulation parameters relies on in vivo
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Customizing the human-avatar mapping based on EEG error related potentials J. Neural Eng. (IF 4.0) Pub Date : 2024-03-27 Fumiaki Iwane, Thibault Porssut, Olaf Blanke, Ricardo Chavarriaga, José del R Millán, Bruno Herbelin, Ronan Boulic
Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside the virtual body when they perceive erroneous interaction, i.e. a break-in-embodiment (BiE). However, the way to detect such an inadequate event is
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Survival and maturation of human induced pluripotent stem cell-derived dopaminergic progenitors in the parkinsonian rat brain is enhanced by transplantation in a neurotrophin-enriched hydrogel J. Neural Eng. (IF 4.0) Pub Date : 2024-03-25 Giulia Comini, Rachel Kelly, Sarah Jarrin, Tommy Patton, Kaushik Narasimhan, Abhay Pandit, Nicola Drummond, Tilo Kunath, Eilís Dowd
Objective. Although human induced pluripotent stem cell (iPSC)-derived cell replacement for Parkinson’s disease has considerable reparative potential, its full therapeutic benefit is limited by poor graft survival and dopaminergic maturation. Injectable biomaterial scaffolds, such as collagen hydrogels, have the potential to address these issues via a plethora of supportive benefits including acting
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Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals * * This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project MYOREHAB under Grant 01DN23002, by the Bavarian Ministry of Economic Affairs, Regional Development and Energy (StMWi) through the project GraspAgain under Grant MV-2303-0006, and by the European J. Neural Eng. (IF 4.0) Pub Date : 2024-03-25 Raul C Sîmpetru, Vlad Cnejevici, Dario Farina, Alessandro Del Vecchio
Objective. Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics
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Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification J. Neural Eng. (IF 4.0) Pub Date : 2024-03-25 Hongfang Han, Jiuchuan Jiang, Lingyun Gu, John Q Gan, Haixian Wang
Objective. Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using
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Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation J. Neural Eng. (IF 4.0) Pub Date : 2024-03-22 Haneen Alsuradi, Arshiya Khattak, Ali Fakhry, Mohamad Eid
Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject
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Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive J. Neural Eng. (IF 4.0) Pub Date : 2024-03-21 Dennis Yeung, Francesco Negro, Ivan Vujaklija
Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity. Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist
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A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task J. Neural Eng. (IF 4.0) Pub Date : 2024-03-20 Naser Sadeghnejad, Mehdi Ezoji, Reza Ebrahimpour, Mohamad Qodosi, Sajjad Zabbah
Objective. Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking
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Explainable machine learning predictions of perceptual sensitivity for retinal prostheses J. Neural Eng. (IF 4.0) Pub Date : 2024-03-19 Galen Pogoncheff, Zuying Hu, Ariel Rokem, Michael Beyeler
Objective. Retinal prostheses evoke visual precepts by electrically stimulating functioning cells in the retina. Despite high variance in perceptual thresholds across subjects, among electrodes within a subject, and over time, retinal prosthesis users must undergo ‘system fitting’, a process performed to calibrate stimulation parameters according to the subject’s perceptual thresholds. Although previous
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Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration J. Neural Eng. (IF 4.0) Pub Date : 2024-03-19 Alka Rachel John, Avinash K Singh, Klaus Gramann, Dikai Liu, Chin-Teng Lin
Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns
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Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs J. Neural Eng. (IF 4.0) Pub Date : 2024-03-13 Pranav Mamidanna, Shima Gholinezhad, Dario Farina, Jakob Lund Dideriksen, Strahinja Dosen
Objective. Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively
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Irregularity of instantaneous gamma frequency in the motor control network characterize visuomotor and proprioceptive information processing J. Neural Eng. (IF 4.0) Pub Date : 2024-03-12 Jihye Ryu, Jeong Woo Choi, Soroush Niketeghad, Elizabeth B Torres, Nader Pouratian
Objective. The study aims to characterize movements with different sensory goals, by contrasting the neural activity involved in processing proprioceptive and visuo-motor information. To accomplish this, we have developed a new methodology that utilizes the irregularity of the instantaneous gamma frequency parameter for characterization. Approach. In this study, eight essential tremor patients undergoing
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Safety, tolerability and blinding efficiency of non-invasive deep transcranial temporal interference stimulation: first experience from more than 250 sessions J. Neural Eng. (IF 4.0) Pub Date : 2024-03-11 Pierre Vassiliadis, Emma Stiennon, Fabienne Windel, Maximilian J Wessel, Elena Beanato, Friedhelm C Hummel
Objective. Selective neuromodulation of deep brain regions has for a long time only been possible through invasive approaches, because of the steep depth-focality trade-off of conventional non-invasive brain stimulation (NIBS) techniques. Approach. An approach that has recently emerged for deep NIBS in humans is transcranial Temporal Interference Stimulation (tTIS). However, a crucial aspect for its
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Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomised controlled trial J. Neural Eng. (IF 4.0) Pub Date : 2024-03-08 S-C Noble, E Woods, T Ward, J V Ringwood
Objective. Neurofeedback (NFB) training through brain–computer interfacing has demonstrated efficacy in treating neurological deficits and diseases, and enhancing cognitive abilities in healthy individuals. It was previously shown that event-related potential (ERP)-based NFB training using a P300 speller can improve attention in healthy adults by incrementally increasing the difficulty of the spelling
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Mapping dynamic spatial patterns of brain function with spatial-wise attention J. Neural Eng. (IF 4.0) Pub Date : 2024-03-07 Yiheng Liu, Enjie Ge, Mengshen He, Zhengliang Liu, Shijie Zhao, Xintao Hu, Ning Qiang, Dajiang Zhu, Tianming Liu, Bao Ge
Objective: Using functional magnetic resonance imaging (fMRI) and deep learning to discover the spatial pattern of brain function, or functional brain networks (FBNs) has been attracted many reseachers. Most existing works focus on static FBNs or dynamic functional connectivity among fixed spatial network nodes, but ignore the potential dynamic/time-varying characteristics of the spatial networks themselves
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Fork-shaped neural interface with multichannel high spatial selectivity in the peripheral nerve of a rat J. Neural Eng. (IF 4.0) Pub Date : 2024-03-06 Wonsuk Choi, HyungDal Park, Seonghwan Oh, Jeong-Hyun Hong, Junesun Kim, Dae Sung Yoon, Jinseok Kim
Objective. This study aims to develop and validate a sophisticated fork-shaped neural interface (FNI) designed for peripheral nerves, focusing on achieving high spatial resolution, functional selectivity, and improved charge storage capacities. The objective is to create a neurointerface capable of precise neuroanatomical analysis, neural signal recording, and stimulation. Approach. Our approach involves
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The role of stimulus periodicity on spinal cord stimulation-induced artificial sensations in rodents J. Neural Eng. (IF 4.0) Pub Date : 2024-03-05 Jacob C Slack, Sidnee L Zeiser, Amol P Yadav
Objective. Sensory feedback is critical for effectively controlling brain-machine interfaces and neuroprosthetic devices. Spinal cord stimulation (SCS) is proposed as a technique to induce artificial sensory perceptions in rodents, monkeys, and humans. However, to realize the full potential of SCS as a sensory neuroprosthetic technology, a better understanding of the effect of SCS pulse train parameter
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Finite-element modeling of neuromodulation via controlled delivery of potassium ions using conductive polymer-coated microelectrodes J. Neural Eng. (IF 4.0) Pub Date : 2024-03-01 Claudio Verardo, Leandro Julian Mele, Luca Selmi, Pierpaolo Palestri
Objective. The controlled delivery of potassium is an interesting neuromodulation modality, being potassium ions involved in shaping neuron excitability, synaptic transmission, network synchronization, and playing a key role in pathological conditions like epilepsy and spreading depression. Despite many successful examples of pre-clinical devices able to influence the extracellular potassium concentration
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Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity J. Neural Eng. (IF 4.0) Pub Date : 2024-03-01 Parima Ahmadipour, Omid G Sani, Bijan Pesaran, Maryam M Shanechi
Objective. Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain–machine interfaces
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Frequency-dependent membrane polarization across neocortical cell types and subcellular elements by transcranial alternating current stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-02-29 Xuelin Huang, Xile Wei, Jiang Wang, Guosheng Yi
Objective. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that directly interacts with ongoing brain oscillations in a frequency-dependent manner. However, it remains largely unclear how the cellular effects of tACS vary between cell types and subcellular elements. Approach. In this study, we use a set of morphologically realistic models of neocortical
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Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering J. Neural Eng. (IF 4.0) Pub Date : 2024-02-27 Ali Mobaien, Reza Boostani, Saeid Sanei
Objective. the P300-based brain–computer interface (BCI) establishes a communication channel between the mind and a computer by translating brain signals into commands. These systems typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience an elicitation of a P300 event-related
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Noninvasive modulation of essential tremor with focused ultrasonic waves J. Neural Eng. (IF 4.0) Pub Date : 2024-02-27 Thomas S Riis, Adam J Losser, Panagiotis Kassavetis, Paolo Moretti, Jan Kubanek
Objective: Transcranial focused low-intensity ultrasound has the potential to noninvasively modulate confined regions deep inside the human brain, which could provide a new tool for causal interrogation of circuit function in humans. However, it has been unclear whether the approach is potent enough to modulate behavior. Approach: To test this, we applied low-intensity ultrasound to a deep brain thalamic
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Detection of evoked resonant neural activity in Parkinson’s disease J. Neural Eng. (IF 4.0) Pub Date : 2024-02-26 Wee-Lih Lee, Nicole Ward, Matthew Petoe, Ashton Moorhead, Kiaran Lawson, San San Xu, Kristian Bulluss, Wesley Thevathasan, Hugh McDermott, Thushara Perera
Objective. This study investigated a machine-learning approach to detect the presence of evoked resonant neural activity (ERNA) recorded during deep brain stimulation (DBS) of the subthalamic nucleus (STN) in people with Parkinson’s disease. Approach. Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features. Main
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Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework J. Neural Eng. (IF 4.0) Pub Date : 2024-02-26 Li-Dan Kuang, He-Qiang Li, Jianming Zhang, Yan Gui, Jin Zhang
Objective. Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize
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Three-dimensional electro-neural interfaces electroplated on subretinal prostheses J. Neural Eng. (IF 4.0) Pub Date : 2024-02-23 Emma Butt, Bing-Yi Wang, Andrew Shin, Zhijie Charles Chen, Mohajeet Bhuckory, Sarthak Shah, Ludwig Galambos, Theodore Kamins, Daniel Palanker, Keith Mathieson
Objective. Retinal prosthetics offer partial restoration of sight to patients blinded by retinal degenerative diseases through electrical stimulation of the remaining neurons. Decreasing the pixel size enables increasing prosthetic visual acuity, as demonstrated in animal models of retinal degeneration. However, scaling down the size of planar pixels is limited by the reduced penetration depth of the
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Multilayer network-based channel selection for motor imagery brain–computer interface J. Neural Eng. (IF 4.0) Pub Date : 2024-02-22 Shaoting Yan, Yuxia Hu, Rui Zhang, Daowei Qi, Yubo Hu, Dezhong Yao, Li Shi, Lipeng Zhang
Objective. The number of electrode channels in a motor imagery-based brain–computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between
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Local delivery of AdipoRon from self-assembled microparticles to inhibit myelin lipid uptake and to promote lipid efflux from rat macrophages J. Neural Eng. (IF 4.0) Pub Date : 2024-02-22 Robert B Shultz, Nan Hai, Yinghui Zhong
Objective. Abundant lipid-laden macrophages are found at the injury site after spinal cord injury (SCI). These cells have been suggested to be pro-inflammatory and neurotoxic. AdipoRon, an adiponectin receptor agonist, has been shown to promote myelin lipid efflux from mouse macrophage foam cells. While it is an attractive therapeutic strategy, systemic administration of AdipoRon is likely to exert
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Personalized motor imagery prediction model based on individual difference of ERP J. Neural Eng. (IF 4.0) Pub Date : 2024-02-22 Haodong Deng, Mengfan Li, Haoxin Zuo, Huihui Zhou, Enming Qi, Xue Wu, Guizhi Xu
Objective. Motor imagery-based brain–computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed