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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
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Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG) J. Neural Eng. (IF 4.0) Pub Date : 2024-02-22 Xiaolong Wu, Dingguo Zhang, Guangye Li, Xin Gao, Benjamin Metcalfe, Liang Chen
Objective. Deep learning is increasingly used for brain–computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper
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Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task J. Neural Eng. (IF 4.0) Pub Date : 2024-02-16 Xuepu Wang, Bowen Li, Yanfei Lin, Xiaorong Gao
Objective. Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge
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Nonlinear effects at the electrode-tissue interface of deep brain stimulation electrodes J. Neural Eng. (IF 4.0) Pub Date : 2024-02-14 K Sridhar, J Evers, M Lowery
Objective. The electrode-tissue interface provides the critical path for charge transfer in neurostimulation therapies and exhibits well-established nonlinear properties at high applied currents or voltages. These nonlinear properties may influence the efficacy and safety of applied stimulation but are typically neglected in computational models. In this study, nonlinear behavior of the electrode-tissue
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Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach J. Neural Eng. (IF 4.0) Pub Date : 2024-02-09 Mohammad Shokri, Alex R Gogliettino, Paweł Hottowy, Alexander Sher, Alan M Litke, E J Chichilnisky, Sérgio Pequito, Dante Muratore
Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs
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Combining electrodermal activity analysis and dynamic causal modeling to investigate the visual-odor multimodal integration during face perception J. Neural Eng. (IF 4.0) Pub Date : 2024-02-09 Gianluca Rho, Alejandro Luis Callara, Francesco Bossi, Dimitri Ognibene, Cinzia Cecchetto, Tommaso Lomonaco, Enzo Pasquale Scilingo, Alberto Greco
Objective. This study presents a novel methodological approach for incorporating information related to the peripheral sympathetic response into the investigation of neural dynamics. Particularly, we explore how hedonic contextual olfactory stimuli influence the processing of neutral faces in terms of sympathetic response, event-related potentials and effective connectivity analysis. The objective
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Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram J. Neural Eng. (IF 4.0) Pub Date : 2024-02-09 Luca Mesin
Objective. The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications. Approach. An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals
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Neural activity of retinal ganglion cells under continuous, dynamically-modulated high frequency electrical stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-02-09 Madhuvanthi Muralidharan, Tianruo Guo, David Tsai, Jae-Ik Lee, Shelley Fried, Socrates Dokos, John W Morley, Nigel H Lovell, Mohit N Shivdasani
Objective. Current retinal prosthetics are limited in their ability to precisely control firing patterns of functionally distinct retinal ganglion cell (RGC) types. The aim of this study was to characterise RGC responses to continuous, kilohertz-frequency-varying stimulation to assess its utility in controlling RGC activity. Approach. We used in vitro patch-clamp experiments to assess electrically-evoked
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Identifying temporal correlations between natural single-shot videos and EEG signals J. Neural Eng. (IF 4.0) Pub Date : 2024-02-07 Yuanyuan Yao, Axel Stebner, Tinne Tuytelaars, Simon Geirnaert, Alexander Bertrand
Objective. Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature
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REPORT-SCS: minimum reporting standards for spinal cord stimulation studies in spinal cord injury J. Neural Eng. (IF 4.0) Pub Date : 2024-02-07 Raza N Malik, Soshi Samejima, Claire Shackleton, Tiev Miller, Alessandra Laura Giulia Pedrocchi, Alexander G Rabchevsky, Chet T Moritz, David Darrow, Edelle C Field-Fote, Eleonora Guanziroli, Emilia Ambrosini, Franco Molteni, Parag Gad, Vivian K Mushahwar, Rahul Sachdeva, Andrei V Krassioukov
Objective. Electrical spinal cord stimulation (SCS) has emerged as a promising therapy for recovery of motor and autonomic dysfunctions following spinal cord injury (SCI). Despite the rise in studies using SCS for SCI complications, there are no standard guidelines for reporting SCS parameters in research publications, making it challenging to compare, interpret or reproduce reported effects across
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What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention J. Neural Eng. (IF 4.0) Pub Date : 2024-02-06 Iustina Rotaru, Simon Geirnaert, Nicolas Heintz, Iris Van de Ryck, Alexander Bertrand, Tom Francart
Objective. Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listener’s neural recordings, e.g. electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases
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Neural signal data collection and analysis of Percept™ PC BrainSense recordings for thalamic stimulation in epilepsy J. Neural Eng. (IF 4.0) Pub Date : 2024-02-06 Zachary T Sanger, Thomas R Henry, Michael C Park, David Darrow, Robert A McGovern, Theoden I Netoff
Deep brain stimulation (DBS) using Medtronic’s Percept™ PC implantable pulse generator is FDA-approved for treating Parkinson’s disease (PD), essential tremor, dystonia, obsessive compulsive disorder, and epilepsy. Percept™ PC enables simultaneous recording of neural signals from the same lead used for stimulation. Many Percept™ PC sensing features were built with PD patients in mind, but these features
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A machine learning approach for real-time cortical state estimation J. Neural Eng. (IF 4.0) Pub Date : 2024-02-01 David A Weiss, Adriano MF Borsa, Aurélie Pala, Audrey J Sederberg, Garrett B Stanley
Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as ‘cortical state’. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical
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Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review J. Neural Eng. (IF 4.0) Pub Date : 2024-02-01 Khaled M Taghlabi, Jesus G Cruz-Garza, Taimur Hassan, Ojas Potnis, Lokeshwar S Bhenderu, Jaime R Guerrero, Rachael E Whitehead, Yu Wu, Lan Luan, Chong Xie, Jacob T Robinson, Amir H Faraji
Peripheral nerve interfaces (PNIs) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types
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Real-time estimation of EEG-based engagement in different tasks J. Neural Eng. (IF 4.0) Pub Date : 2024-01-31 Angela Natalizio, Sebastian Sieghartsleitner, Leonhard Schreiner, Martin Walchshofer, Antonio Esposito, Josef Scharinger, Harald Pretl, Pasquale Arpaia, Marco Parvis, Jordi Solé-Casals, Marc Sebastián-Romagosa, Rupert Ortner, Christoph Guger
Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using
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Transfer learning with data alignment and optimal transport for EEG based motor imagery classification J. Neural Eng. (IF 4.0) Pub Date : 2024-01-31 Chao Chu, Lei Zhu, Aiai Huang, Ping Xu, Nanjiao Ying, Jianhai Zhang
Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability among different subjects present significant challenges in current Brain–Computer Interfaces (BCI) research, which requires a time-consuming specific calibration procedure to address. Transfer Learning (TL) offers a potential solution by leveraging data or models from one or more source domains to facilitate learning
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Early prediction of dementia using fMRI data with a graph convolutional network approach J. Neural Eng. (IF 4.0) Pub Date : 2024-01-29 Shuning Han, Zhe Sun, Kanhao Zhao, Feng Duan, Cesar F Caiafa, Yu Zhang, Jordi Solé-Casals
Objective. Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging
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cTBS over primary motor cortex increased contralateral corticomuscular coupling and interhemispheric functional connection J. Neural Eng. (IF 4.0) Pub Date : 2024-01-25 Rui Xu, Haichao Zhang, Shizhong Liu, Lin Meng, Dong Ming
Objective. Transcranial magnetic stimulation is a non-invasive brain stimulation technique that changes the activity of the cerebral cortex. Contralesional continuous theta burst stimulation (cTBS) has been proposed and verified beneficial to stroke motor recovery. However, the underlying mechanism is still unclear. Approach. 20 healthy right-handed subjects were recruited in this study, receiving
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MEP and TEP features variability: is it just the brain-state? J. Neural Eng. (IF 4.0) Pub Date : 2024-01-23 Claudia Bigoni, Sara Pagnamenta, Andéol Cadic-Melchior, Michele Bevilacqua, Sylvain Harquel, Estelle Raffin, Friedhelm C Hummel
Objective. The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determination. Here, we provide further insights in the effects of the brain-state on cortical and corticospinal excitability and quantify the impact of
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Limitations in the electrochemical analysis of voltage transients J. Neural Eng. (IF 4.0) Pub Date : 2024-01-23 Alexander R Harris
Objective. Chronopotentiometric voltage transients (VTs) are used to assess the performance of bionic electrodes. The data obtained from VTs are used to define the safe operating conditions of clinical devices. Various approaches to analysing VTs have been reported, and a number of limitations in the accuracy of the measurements in relation to electrode size have been noted previously. Approach. The
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Modified motor unit properties in residual muscle following transtibial amputation J. Neural Eng. (IF 4.0) Pub Date : 2024-01-17 Noah Rubin, Robert Hinson, Katherine Saul, William Filer, Xiaogang Hu, He (Helen) Huang
Objective. Neural signals in residual muscles of amputated limbs are frequently decoded to control powered prostheses. Yet myoelectric controllers assume muscle activities of residual muscles are similar to that of intact muscles. This study sought to understand potential changes to motor unit (MU) properties after limb amputation. Approach. Six people with unilateral transtibial amputation were recruited
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Beta bursts question the ruling power for brain–computer interfaces J. Neural Eng. (IF 4.0) Pub Date : 2024-01-17 Sotirios Papadopoulos, Maciej J Szul, Marco Congedo, James J Bonaiuto, Jérémie Mattout
Objective: Current efforts to build reliable brain–computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience
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Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain–computer interface J. Neural Eng. (IF 4.0) Pub Date : 2024-01-17 Aarthy Nagarajan, Neethu Robinson, Kai Keng Ang, Karen Sui Geok Chua, Effie Chew, Cuntai Guan
Objective. Motor imagery (MI) brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. This study aims to assess the feasibility of applying MI-BCI models pre-trained using data from healthy individuals to detect
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EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing J. Neural Eng. (IF 4.0) Pub Date : 2024-01-17 Faghihe Massaeli, Sarah D Power
Objective. To date, most research on electroencephalography (EEG)-based mental workload detection for passive brain-computer interface (pBCI) applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory
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One-shot random forest model calibration for hand gesture decoding J. Neural Eng. (IF 4.0) Pub Date : 2024-01-16 Xinyu Jiang, Chenfei Ma, Kianoush Nazarpour
Objective. Most existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user. Approach. We trained a random forest (RF) model with EMG data
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Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation J. Neural Eng. (IF 4.0) Pub Date : 2024-01-12 Ximeng Mao, Yao-Chuan Chang, Stavros Zanos, Guillaume Lajoie
Objective. Neurostimulation is emerging as treatment for several diseases of the brain and peripheral organs. Due to variability arising from placement of stimulation devices, underlying neuroanatomy and physiological responses to stimulation, it is essential that neurostimulation protocols are personalized to maximize efficacy and safety. Building such personalized protocols would benefit from accumulated
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Evaluating and benchmarking the EEG signal quality of high-density, dry MXene-based electrode arrays against gelled Ag/AgCl electrodes J. Neural Eng. (IF 4.0) Pub Date : 2024-01-12 Brian Erickson, Ryan Rich, Sneha Shankar, Brian Kim, Nicolette Driscoll, Georgios Mentzelopoulos, Guadalupe Fernandez-Nuñez, Flavia Vitale, John D Medaglia
Objective. To evaluate the signal quality of dry MXene-based electrode arrays (also termed ‘MXtrodes’) for electroencephalographic (EEG) recordings where gelled Ag/AgCl electrodes are a standard. Approach. We placed 4 × 4 MXtrode arrays and gelled Ag/AgCl electrodes on different scalp locations. The scalp was cleaned with alcohol and rewetted with saline before application. We recorded from both electrode
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Pseudo-online framework for BCI evaluation: a MOABB perspective using various MI and SSVEP datasets J. Neural Eng. (IF 4.0) Pub Date : 2024-01-12 Igor Carrara, Theodore Papadopoulo
Objective. BCI (Brain–Computer Interfaces) operate in three modes: online, offline, and pseudo-online. In online mode, real-time EEG data is constantly analyzed. In offline mode, the signal is acquired and processed afterwards. The pseudo-online mode processes collected data as if they were received in real-time. The main difference is that the offline mode often analyzes the whole data, while the
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The role of vowel and consonant onsets in neural tracking of natural speech J. Neural Eng. (IF 4.0) Pub Date : 2024-01-11 Mohammad Jalilpour Monesi, Jonas Vanthornhout, Tom Francart, Hugo Van hamme
Objective. To investigate how the auditory system processes natural speech, models have been created to relate the electroencephalography (EEG) signal of a person listening to speech to various representations of the speech. Mainly the speech envelope has been used, but also phonetic representations. We investigated to which degree of granularity phonetic representations can be related to the EEG signal
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High-density ear-EEG for understanding ear-centered EEG J. Neural Eng. (IF 4.0) Pub Date : 2024-01-09 Arnd Meiser, Anna Lena Knoll, Martin G Bleichner
Background. Mobile ear-EEG provides the opportunity to record EEG unobtrusively in everyday life. However, in real-life, the EEG data quickly becomes difficult to interpret, as the neural signal is contaminated by other, non-neural signal contributions. Due to the small number of electrodes in ear-EEG devices, the interpretation of the EEG becomes even more difficult. For meaningful and reliable ear-EEG
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A spiking neural network with continuous local learning for robust online brain machine interface J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 Elijah A Taeckens, Sahil Shah
Objective. Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically
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A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 Yan Liu, Xinhao Peng, Yingxiao Tan, Tolulope Tofunmi Oyemakinde, Mengtao Wang, Guanglin Li, Xiangxin Li
Objective. Surface electromyography pattern recognition (sEMG-PR) is considered as a promising control method for human-machine interaction systems. However, the performance of a trained classifier would greatly degrade for novel users since sEMG signals are user-dependent and largely affected by a number of individual factors such as the quantity of subcutaneous fat and the skin impedance. Approach
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Neural regulation of slow waves and phasic contractions in the distal stomach: a mathematical model J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 Omkar N Athavale, Recep Avci, Alys R Clark, Madeleine R Di Natale, Xiaokai Wang, John B Furness, Zhongming Liu, Leo K Cheng, Peng Du
Objective. Neural regulation of gastric motility occurs partly through the regulation of gastric bioelectrical slow waves (SWs) and phasic contractions. The interaction of the tissues and organs involved in this regulatory process is complex. We sought to infer the relative importance of cellular mechanisms in inhibitory neural regulation of the stomach by enteric neurons and the interaction of inhibitory
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Selective chronic recording in small nerve fascicles of sciatic nerve with carbon nanotube yarns in rats J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 B P Kotamraju, Thomas E Eggers, Grant A McCallum, Dominique M Durand
Objective. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory
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Degree of differentiation impacts neurobiological signature and resistance to hypoxia of SH-SY5Y cells J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 E J H F Voogd, N Doorn, M R Levers, J Hofmeijer, M Frega
Objective. SH-SY5Y cells are valuable neuronal in vitro models for studying patho-mechanisms and treatment targets in brain disorders due to their easy maintenance, rapid expansion, and low costs. However, the use of various degrees of differentiation hampers appreciation of results and may limit the translation of findings to neurons or the brain. Here, we studied the neurobiological signatures of
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Simulation study on high spatio-temporal resolution acousto-electrophysiological neuroimaging J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 Ruben Schoeters, Thomas Tarnaud, Luc Martens, Emmeric Tanghe
Objective. Acousto-electrophysiological neuroimaging (AENI) is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will
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Electrospinography for non-invasively recording spinal sensorimotor networks in humans J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 Alexander G Steele, Amir H Faraji, Jose L Contreras-Vidal
Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity
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Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model J. Neural Eng. (IF 4.0) Pub Date : 2024-01-04 Konstantinos Spiliotis, Konstantin Butenko, Jens Starke, Ursula van Rienen, Rüdiger Köhling
Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target. Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically
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Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs J. Neural Eng. (IF 4.0) Pub Date : 2023-12-29 Nicolas Ivanov, Aaron Lio, Tom Chau
Objective. While electroencephalography (EEG)-based brain–computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user’s ability to perform mental control tasks and produce discernible EEG patterns.
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Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM) J. Neural Eng. (IF 4.0) Pub Date : 2023-12-27 Tao Xie, Thomas J Foutz, Markus Adamek, James R Swift, Cory S Inman, Joseph R Manns, Eric C Leuthardt, Jon T Willie, Peter Brunner
Objective. Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals. Approach. To validate
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Towards a more accurate quasi-static approximation of the electric potential for neurostimulation with kilohertz-frequency sources * * This work has been partially supported by ANID Millennium Science Initiative Program through Millennium Nucleus for Applied Control and Inverse Problems NCN19-161. J. Neural Eng. (IF 4.0) Pub Date : 2023-12-22 Thomas Caussade, Esteban Paduro, Matías Courdurier, Eduardo Cerpa, Warren M Grill, Leonel E Medina
Objective. Our goal was to determine the conditions for which a more precise calculation of the electric potential than the quasi-static approximation may be needed in models of electrical neurostimulation, particularly for signals with kilohertz-frequency components. Approach. We conducted a comprehensive quantitative study of the differences in nerve fiber activation and conduction block when using
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Distally-referred surface electrical nerve stimulation (DR-SENS) for haptic feedback J. Neural Eng. (IF 4.0) Pub Date : 2023-12-21 Luis Mesias, M Akif Gormez, Dustin J Tyler, Nathaniel S Makowski, Emily L Graczyk, Michael J Fu
Objective. This study’s objective is to understand distally-referred surface electrical nerve stimulation (DR-SENS) and evaluates the effects of electrode placement, polarity, and stimulation intensity on the location of elicited sensations in non-disabled individuals. Approach. A two-phased human experiment was used to characterize DR-SENS. In Experiment One, we explored 182 electrode combinations
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EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES) J. Neural Eng. (IF 4.0) Pub Date : 2023-12-20 Alicia Everitt, Haley Richards, Yinchen Song, Joel Smith, Erik Kobylarz, Timothy Lukovits, Ryan Halter, Ethan Murphy
Objective. Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to
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Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions J. Neural Eng. (IF 4.0) Pub Date : 2023-12-15 Sharath Koorathota, Jia Li Ma, Josef Faller, Linbi Hong, Pawan Lapborisuth, Paul Sajda
Objective. Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving. Approach. Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the
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The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia J. Neural Eng. (IF 4.0) Pub Date : 2023-12-14 Zhenhu Liang, Zhilei Lan, Yong Wang, Yang Bai, Jianghong He, Juan Wang, Xiaoli Li
Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the
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Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers J. Neural Eng. (IF 4.0) Pub Date : 2023-12-14 Rui Wang, Tianyi Zhou, Zheng Li, Jing Zhao, Xiaoli Li
Objective. In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain–computer
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Unsupervised learning of stationary and switching dynamical system models from Poisson observations J. Neural Eng. (IF 4.0) Pub Date : 2023-12-12 Christian Y Song, Maryam M Shanechi
Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases
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EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks J. Neural Eng. (IF 4.0) Pub Date : 2023-12-12 Jinpei Han, Xiaoxi Wei, A Aldo Faisal
Objective. Brain-machine interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to the variation in recording equipment and electrode layouts resulting
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Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum J. Neural Eng. (IF 4.0) Pub Date : 2023-12-12 Yufeng Ke, Tao Wang, Feng He, Shuang Liu, Dong Ming
Objective. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs
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Exploration of sensations evoked during electrical stimulation of the median nerve at the wrist level J. Neural Eng. (IF 4.0) Pub Date : 2023-12-11 Nebojsa Malesevic, Frida Lindén, Lycke Fureby, Carolina Rudervall, Anders Björkman, Christian Antfolk
Objective. Nerve rehabilitation following nerve injury or surgery at the wrist level is a lengthy process during which not only peripheral nerves regrow towards receptors and muscles, but also the brain undergoes plastic changes. As a result, at the time when nerves reach their targets, the brain might have already allocated some of the areas within the somatosensory cortex that originally processed
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Eliciting calcium transients with UV nanosecond laser stimulation in adult patient-derived glioblastoma brain cancer cells in vitro J. Neural Eng. (IF 4.0) Pub Date : 2023-12-11 Nicholas G Mellor, Sylvia A Chung, E Scott Graham, Bryan W Day, Charles P Unsworth
Objective. Glioblastoma (GBM) is the most common and lethal type of high-grade adult brain cancer. The World Health Organization have classed GBM as an incurable disease because standard treatments have yielded little improvement with life-expectancy being 6–15 months after diagnosis. Different approaches are now crucial to discover new knowledge about GBM communication/function in order to establish
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Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study J. Neural Eng. (IF 4.0) Pub Date : 2023-12-07 Zhicheng Teng, Guanghua Xu, Xun Zhang, Xiaobi Chen, Sicong Zhang, Hsien-Yung Huang
Objective. The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control. Approach. Eleven healthy
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Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI J. Neural Eng. (IF 4.0) Pub Date : 2023-12-04 Cooper J Mellema, Albert A Montillo
Objective. New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects