Journal of Neural Engineering

Objective. Ipsilateral motor evoked potentials (iMEPs) are believed to represent cortically evoked excitability of uncrossed brainstem-mediated pathways. In the event of extensive injury to (crossed) corticospinal pathways, which can occur following a stroke, uncrossed ipsilateral pathways may serve as an alternate resource to support the recovery of the paretic limb. However, iMEPs, even in neurally intact people, can be small, infrequent, and noisy, so discerning them in stroke survivors is very challenging. This study aimed to investigate the inter-rater reliability of iMEP features (presence/absence, amplitude, area, onset, and offset) to evaluate the reliability of existing methods for objectively analyzing iMEPs in stroke survivors with chronic upper extremity (UE) motor impairment. Approach. Two investigators subjectively measured iMEP features from thirty-two stroke participants with chronic UE motor impairment. Six objective methods based on standard deviation (SD) and mean consecutive differences (MCD) were used to measure the iMEP features from the same 32 participants. IMEP analysis used both trial-by-trial (individual signal) and average-signal analysis approaches. Inter-rater reliability of iMEP features and agreement between the subjective and objective methods were analyzed (percent agreement-PA and intraclass correlation coefficient-ICC). Main results. Inter-rater reliability was excellent for iMEP detection (PA > 85%), amplitude, and area (ICC > 0.9). Of the six objective methods we tested, the 1SD method was most appropriate for identifying and analyzing iMEP amplitude and area (ICC > 0.9) in both trial-by-trial and average signal analysis approaches. None of the objective methods were reliable for analyzing iMEP onset and offset. Results also support using the average-signal analysis approach over the trial-by-trial analysis approach, as it offers excellent reliability for iMEP analysis in stroke survivors with chronic UE motor impairment. Significance. Findings from our study have relevance for understanding the role of ipsilateral pathways that typically survive unilateral severe white matter injury in people with stroke.

Objective. Non-invasive brain stimulation (NIBS) offers therapeutic benefits for various brain disorders. Personalization may enhance these benefits by optimizing stimulation parameters for individual subjects. Approach. We present a computational pipeline for simulating and assessing the effects of NIBS using personalized, large-scale brain network activity models. Using structural MRI and diffusion-weighted imaging data, the pipeline leverages a convolutional neural network-based segmentation algorithm to generate subject-specific head models with up to 40 tissue types and personalized dielectric properties. We integrate electromagnetic simulations of NIBS exposure with whole-brain network models to predict NIBS-dependent perturbations in brain dynamics, simulate the resulting EEG traces, and quantify metrics of brain dynamics. Main results. The pipeline is implemented on o²S²PARC, an open, cloud-based infrastructure designed for collaborative and reproducible computational life science. Furthermore, a dedicated planning tool provides guidance for optimizing electrode placements for transcranial temporal interference stimulation. In two proof-of-concept applications, we demonstrate that: (i) transcranial alternating current stimulation produces expected shifts in the EEG spectral response, and (ii) simulated baseline network activity exhibits physiologically plausible fluctuations in inter-hemispheric synchronization. Significance. This pipeline facilitates a shift from exposure-based to response-driven optimization of NIBS, supporting new stimulation paradigms that steer brain dynamics towards desired activity patterns in a controlled manner.

Objective. Vibrotactile stimulation (VS) has been widely used as an appropriate motor imagery (MI) guidance strategy to improve MI performance. However, most VS induced by a single vibrator cannot provide spatiotemporal information of tactile sensation associated with the visual guidance of the imagined motion process, not vividly providing MI guidance for subjects. Approach. This paper proposed a paradigm with visual and spatiotemporal tactile synchronized stimulation (VSTSS) to provide vivid MI guidance to help subjects perform lower-limb MI tasks and improve MI-based brain-computer interface (MI-BCI) performance, with a focus on poorly performing subjects. The proposed paradigm provided subjects with the natural spatiotemporal tactile sensation associated with the visual guidance of the foot movement process during MI. Fourteen healthy subjects were recruited to participate in the MI and Rest tasks and divided into good and poor performers. Furthermore, electrophysiological features and classification performance were analyzed to assess motor cortical activation and MI-BCI performance under no VS (NVS), VS, and VSTSS. Main results. The phenomenon of event-related desynchronization (ERD) in the sensorimotor cortex during MI under the VSTSS was more pronounced compared to the NVS and VS. Specifically, the VSTSS could improve the average ERD values in the motor cortex during the task segment by 34.70% and 14.28% than the NVS and VS in the alpha rhythm for poor performers, respectively. Additionally, the VSTSS could significantly enhance the classification accuracy between the MI and Rest tasks by 12.52% and 4.05% compared to NVS and VS for poor performers, respectively. Significance. The proposed paradigm could enhance motor cortical activation during MI and improve classification performance by providing vivid MI guidance for subjects, offering a promise for the application of lower-limb MI-BCI in stroke rehabilitation in the future.

Objective. The identification of individual neuronal activity from multielectrode arrays poses significant challenges, including handling data from numerous electrodes, resolving overlapping action potentials and tracking activity across long recordings. This study introduces NeuroNella, an automated algorithm developed to address these challenges. Approach. NeuroNella employs blind source separation to leverage the sparsity of action potentials in multichannel recordings. It was validated using three datasets, including two publicly available ones: (1) in vitro recordings (252 channels) of retinal ganglion cells from mice with simultaneous ground-truth loose patch data to assess accuracy; (2) a Neuropixel recording from an awake mouse, comprising 374 channels spanning different brain areas, to demonstrate scalability with dense multielectrode configurations in in vivo recordings; and (3) data (32 channels) recorded from the medullary reticular formation in a terminally anaesthetised macaque, to showcase decomposition over long periods of time. Main results. The algorithm exhibited an error rate of less than 1% compared to ground-truth data. It reliably identified individual neurons, detected neuronal activity across a wide amplitude range, and tolerated minor probe shifts, maintaining robustness in prolonged experimental sessions. Significance. NeuroNella provides an automated and efficient method for neuronal activity identification. Its adaptability to diverse dataset, species, and recording configurations underscores its potential to advance studies of neuronal dynamics and facilitate real-time neuronal decoding systems.

Objective. Muscle fiber conduction velocity (MFCV) describes the speed at which electrical activity propagates along muscle fibers and is typically assessed using invasive or surface electromyography. Because electrical currents generate magnetic fields, propagation velocity can potentially also be measured magnetically using magnetomyography (MMG), offering the advantage of a contactless approach. Approach. To test this hypothesis, we recorded MMG signals from the right biceps brachii muscle of 24 healthy subjects (12 male, 12 female) using a linear array of seven optically pumped magnetometers (OPMs). Subjects maintained muscle force for 30 s at 20%, 40%, and 60% of their maximum voluntary contraction. Main results. In 20 subjects, propagation of MMG signals was observable. Change in polarity and signal cancellation enabled localization of the innervation zone. We estimated the MFCV for each condition by cross-correlating double-differentiated MMG signals. To validate our results, we examined whether MFCV estimations increased with higher force levels, a well-documented characteristic of the neuromuscular system. The median MFCV significantly increased with force (p = 0.007), with median values of 3.2 m s ⁻¹ at 20%, 3.8 m s ⁻¹ at 40%, and 4.4 m s ⁻¹ at 60% across all 20 subjects. Significance. Our results establish the first measurements of magnetic MFCV in MMG using OPMs. These findings pave the way for further developments and application of quantum sensors for contactless clinical neurophysiology.

Transcranial magnetic resonance-guided focused ultrasound (MRgFUS) represents a transformative modality in treating neurological disorders and diseases, offering precise, minimally invasive interventions for conditions such as essential tremor and Parkinson's disease. Objective. This paper presents an industry-focused perspective on the current state of MRgFUS, highlighting recent advancements, challenges, and emerging opportunities within the field. Approach. We review key clinical applications and therapeutic mechanisms, focusing on targeted ablation, while discussing technological innovations that support new indications. Current regulatory frameworks, challenges in device development, and market trends are examined to provide an understanding of the industry landscape. Main results. We indicate some limitations in MRgFUS and suggest potential strategies for overcoming these limitations to optimize treatment outcomes. Significance. We conclude with an outlook on promising developments, including artificial intelligence-enhanced targeting, low and high-field magnetic resonance imaging integration, and multimodal imaging techniques, that could potentially drive further innovation and adoption of MRgFUS in brain therapy.

Objective. Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering. Approach. We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering. Main results. Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions. Significance. By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research.

Objective. Sharing computational models offers many benefits, including increased scientific rigor during project execution, readership of the associated paper, resource usage efficiency, replicability, and reusability. In recognition of the growing practice and requirement of sharing models, code, and data, herein, we provide guidance to facilitate sharing of computational models by providing an accessible resource for regular reference throughout a project's stages. Approach. We synthesized literature on good practices in scientific computing and on code and data sharing with our experience in developing, sharing, and using models of neural stimulation, although the guidance will also apply well to most other types of computational models. Main results. We first describe the '6 R' characteristics of shared models, leaning on prior scientific computing literature, which enforce accountability and enable advancement: re-runnability, repeatability, replicability, reproducibility, reusability, and readability. We then summarize action items associated with good practices in scientific computing, including selection of computational tools during project planning, code and documentation design during development, and user instructions for deployment. We provide a detailed checklist of the contents of shared models and associated materials, including the model itself, code for reproducing published figures, documentation, and supporting datasets. We describe code, model, and data repositories, including a list of characteristics to consider when selecting a platform for sharing. We describe intellectual property (IP) considerations to balance permissive, open-source licenses versus software patents and bespoke licenses that govern and incentivize commercialization. Finally, we exemplify these practices with our ASCENT pipeline for modeling peripheral nerve stimulation. Significance. We hope that this paper will serve as an important and actionable reference for scientists who develop models—from project planning through publication—as well as for model users, institutions, IP experts, journals, funding sources, and repository platform developers.

The Tenth International brain–computer interface (BCI) meeting was held June 6–9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.

The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.

Objective. With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable. Approach. To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state visual evoked potentials (SSVEP), Newton's ring for steady-state motion visual evoked potentials (SSMVEP), and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared. Main results. The online information transfer rates (ITR) for the three stimulus paradigms were 53.77 bits/min, 51.41 ± 3.55 bits/min, and 52.07 ± 3.09 bits/min, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst. Significance. These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.

Objective. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in feature extraction. This study aims to develop a hybrid model that integrates the advantages of convolutional neural networks (CNNs) and Transformer to enhance seizure prediction performance. Approach. We propose FusionXNet, a hybrid model inspired by CNNs and Transformer architectures, for seizure prediction. Specifically, we design a token synthesis unit (TSU) to extract local features using convolution operations and capture global EEG representations via attention mechanisms. By merging local and global features extracted from the EEG segments, FusionXNet enhances feature representations, which are subsequently fed into a classifier for final seizure prediction. Main results. We evaluate the model on the publicly available CHB-MIT dataset, conducting segment-based and event-based experiments in both patient-specific and cross-patient settings. In event-based patient-specific experiments, FusionXNet achieves a sensitivity of 97.602% and a false positive rate (FPR) of 0.059/h. The results demonstrate that the proposed model effectively predicts seizures with high sensitivity and a low FPR, outperforming existing methods. Significance. The proposed FusionXNet model provides a robust and efficient approach for seizure prediction by leveraging both local and global feature extraction. The high sensitivity and low FPR indicate its potential for real-world clinical applications, improving patient management and reducing healthcare costs.

Objective: The study of neurovascular coupling (NVC), the relationship between neuronal activity and cerebral blood flow, is essential for understanding brain physiology in both healthy and pathological states. Current methods to study NVC include neuroimaging techniques with limited temporal resolution and indirect neuronal activity measures. Methods including electroencephalographic (EEG) data are predominantly linear and display limitations that nonlinear methods address. Transfer Entropy (TE) explores linear and nonlinear relationships simultaneously. This study hypothesizes that complex NVC interactions in stroke patients, both linear and&#xD;nonlinear, can be detected using TE. Approach: TE between simultaneously recorded EEG and cerebral blood flow velocity (CBFV) signals was computed and analyzed in three settings: ipsilateral (EEG and CBFV from same hemisphere) stroke and nonstroke, and contralateral (EEG from stroke hemisphere, CBFV from nonstroke hemisphere). A surrogate analysis was performed to evaluate the significance of TE values and to identify the nature of the interactions. Main results: The results showed that EEG generally influenced CBFV. There were more linear+nonlinear interactions in the ipsilateral nonstroke setting and in the delta band in ipsilateral&#xD;stroke and contralateral settings. Interactions between EEG and CBFV were stronger on the nonstroke side for linear+nonlinear dynamics. The strength and nature of the interactions were weakly correlated with clinical outcomes (e.g., delta band (p<0.05): infarct growth linear = -0.448, linear+nonlinear = -0.339; NHISS linear = -0.473, linear+nonlinear = -0.457). Significance: This study exemplifies the benefits of using TE in linear and nonlinear NVC analysis to better understand the implications of these dynamics in stroke severity.

Objective&#xD;A novel regression method is introduced to decode Sign Language finger movements from human electrocorticography (ECoG) recordings.&#xD;Approach The proposed Graph-Optimized Block-Term Tensor Regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a Causal Graph Process (CGP) to account for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively.&#xD;Main results&#xD;Two ECoG datasets were used, one recorded in 5 patients expressing 4 hand gestures, and another in 2 patients expressing all gestures of the Flemish Sign Language Alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single finger scenarios. For the American Sign Language Alphabet (Utrecht dataset), the average correlation across all fingers for all subjects was 0.73 for Go-BTTR, 0.719 for eBTTR and 0.70 for BTTR. For the Flemish Sign Language Alphabet (Leuven dataset), the average correlation across all fingers for all subjects was 0.37 for Go-BTTR, 0.34 for eBTTR and 0.33 for BTTR. &#xD;Significance Our findings show that Go-BTTR is capable of decoding complex hand gestures from the Sign Language Alphabet. Go-BTTR is also computationally efficient which is an advantage when intracranial electrodes are implanted acutely, as part of a patient's presurgical workup, limiting the time for decoder development and testing.&#xD;

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Objective. Non-invasive brain stimulation (NIBS) offers therapeutic benefits for various brain disorders. Personalization may enhance these benefits by optimizing stimulation parameters for individual subjects. Approach. We present a computational pipeline for simulating and assessing the effects of NIBS using personalized, large-scale brain network activity models. Using structural MRI and diffusion-weighted imaging data, the pipeline leverages a convolutional neural network-based segmentation algorithm to generate subject-specific head models with up to 40 tissue types and personalized dielectric properties. We integrate electromagnetic simulations of NIBS exposure with whole-brain network models to predict NIBS-dependent perturbations in brain dynamics, simulate the resulting EEG traces, and quantify metrics of brain dynamics. Main results. The pipeline is implemented on o²S²PARC, an open, cloud-based infrastructure designed for collaborative and reproducible computational life science. Furthermore, a dedicated planning tool provides guidance for optimizing electrode placements for transcranial temporal interference stimulation. In two proof-of-concept applications, we demonstrate that: (i) transcranial alternating current stimulation produces expected shifts in the EEG spectral response, and (ii) simulated baseline network activity exhibits physiologically plausible fluctuations in inter-hemispheric synchronization. Significance. This pipeline facilitates a shift from exposure-based to response-driven optimization of NIBS, supporting new stimulation paradigms that steer brain dynamics towards desired activity patterns in a controlled manner.

Objective: The study of neurovascular coupling (NVC), the relationship between neuronal activity and cerebral blood flow, is essential for understanding brain physiology in both healthy and pathological states. Current methods to study NVC include neuroimaging techniques with limited temporal resolution and indirect neuronal activity measures. Methods including electroencephalographic (EEG) data are predominantly linear and display limitations that nonlinear methods address. Transfer Entropy (TE) explores linear and nonlinear relationships simultaneously. This study hypothesizes that complex NVC interactions in stroke patients, both linear and&#xD;nonlinear, can be detected using TE. Approach: TE between simultaneously recorded EEG and cerebral blood flow velocity (CBFV) signals was computed and analyzed in three settings: ipsilateral (EEG and CBFV from same hemisphere) stroke and nonstroke, and contralateral (EEG from stroke hemisphere, CBFV from nonstroke hemisphere). A surrogate analysis was performed to evaluate the significance of TE values and to identify the nature of the interactions. Main results: The results showed that EEG generally influenced CBFV. There were more linear+nonlinear interactions in the ipsilateral nonstroke setting and in the delta band in ipsilateral&#xD;stroke and contralateral settings. Interactions between EEG and CBFV were stronger on the nonstroke side for linear+nonlinear dynamics. The strength and nature of the interactions were weakly correlated with clinical outcomes (e.g., delta band (p<0.05): infarct growth linear = -0.448, linear+nonlinear = -0.339; NHISS linear = -0.473, linear+nonlinear = -0.457). Significance: This study exemplifies the benefits of using TE in linear and nonlinear NVC analysis to better understand the implications of these dynamics in stroke severity.

Objective. The identification of individual neuronal activity from multielectrode arrays poses significant challenges, including handling data from numerous electrodes, resolving overlapping action potentials and tracking activity across long recordings. This study introduces NeuroNella, an automated algorithm developed to address these challenges. Approach. NeuroNella employs blind source separation to leverage the sparsity of action potentials in multichannel recordings. It was validated using three datasets, including two publicly available ones: (1) in vitro recordings (252 channels) of retinal ganglion cells from mice with simultaneous ground-truth loose patch data to assess accuracy; (2) a Neuropixel recording from an awake mouse, comprising 374 channels spanning different brain areas, to demonstrate scalability with dense multielectrode configurations in in vivo recordings; and (3) data (32 channels) recorded from the medullary reticular formation in a terminally anaesthetised macaque, to showcase decomposition over long periods of time. Main results. The algorithm exhibited an error rate of less than 1% compared to ground-truth data. It reliably identified individual neurons, detected neuronal activity across a wide amplitude range, and tolerated minor probe shifts, maintaining robustness in prolonged experimental sessions. Significance. NeuroNella provides an automated and efficient method for neuronal activity identification. Its adaptability to diverse dataset, species, and recording configurations underscores its potential to advance studies of neuronal dynamics and facilitate real-time neuronal decoding systems.

Objective. Muscle fiber conduction velocity (MFCV) describes the speed at which electrical activity propagates along muscle fibers and is typically assessed using invasive or surface electromyography. Because electrical currents generate magnetic fields, propagation velocity can potentially also be measured magnetically using magnetomyography (MMG), offering the advantage of a contactless approach. Approach. To test this hypothesis, we recorded MMG signals from the right biceps brachii muscle of 24 healthy subjects (12 male, 12 female) using a linear array of seven optically pumped magnetometers (OPMs). Subjects maintained muscle force for 30 s at 20%, 40%, and 60% of their maximum voluntary contraction. Main results. In 20 subjects, propagation of MMG signals was observable. Change in polarity and signal cancellation enabled localization of the innervation zone. We estimated the MFCV for each condition by cross-correlating double-differentiated MMG signals. To validate our results, we examined whether MFCV estimations increased with higher force levels, a well-documented characteristic of the neuromuscular system. The median MFCV significantly increased with force (p = 0.007), with median values of 3.2 m s ⁻¹ at 20%, 3.8 m s ⁻¹ at 40%, and 4.4 m s ⁻¹ at 60% across all 20 subjects. Significance. Our results establish the first measurements of magnetic MFCV in MMG using OPMs. These findings pave the way for further developments and application of quantum sensors for contactless clinical neurophysiology.

Objective. Modern neural devices allow to interact with degenerated tissue in order to restore sensoric loss function and to suppress symptoms of neurodegenerative diseases using microelectronic arrays (MEA). They have a bidirectional interface for performing electrical stimulation to write-in new information and for recording the neural activity to read-out a neural task, e.g. movement ambitions. For both applications, the electrical impedance of the electrode-tissue interface (ETI) is crucial. However, the ETI can change during run-time due to encapsulation effects and changes of the neuronal structures. We investigated if an impedance spectrum can be reliably extracted from recordings during stimulation with microelectrode arrays. Approach. We present a measurement method for characterizing the electrical impedance spectrum during stimulation. We performed charge-controlled stimulation with a penetrating microelectrode array in an electrolyte solution. From the stimulation recordings, we extracted the impedance. Furthermore, a numerical model (digital twin) of the stimulation electrodes is established. Main results. We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transform-based impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties. Significance. Our results pave the way to enable a live assessment of the impedance in future MEA-based neural devices. This will enable adaptive electrical stimulation or (re-)selection of recording electrodes by taking the actual state of the electrode into account.

Objective. Resting electroencephalographic activity is typically indistinguishable from a filtered linear random process across a diverse range of behavioural and pharmacological states, suggesting that the power spectral density of the resting electroencephalogram (EEG) can be modelled as the superposition of multiple, stochastically driven and independent, alpha band (8–13 Hz) relaxation oscillators. This simple model can account for variations in alpha band power and '1/f scaling' in eyes-open/eyes-closed conditions in terms of alterations in the distribution of the alpha band oscillatory relaxation rates. As changes in alpha band power and '1/f scaling' have been reported in anaesthesia we hypothesise that such changes may also be accounted for by alterations in alpha band relaxation oscillatory rate distributions. Approach. On this basis we choose to study the EEG activity of xenon and nitrous oxide, gaseous anaesthetic agents that have been reported to produce different EEG effects, notable given they are both regarded as principally acting via N-methyl-D-aspartate (NMDA) receptor antagonism. By recording high density EEG from participants receiving equilibrated step-level increases in inhaled concentrations of xenon (n = 24) and nitrous oxide (n = 20), alpha band relaxation rate (damping) distributions were estimated by solving an inhomogeneous integral equation describing the linear superposition of multiple alpha-band relaxation oscillators having a continuous distribution of dampings. Main results. For both agents, level-dependent reductions in alpha band power and spectral slope exponent (15–40 Hz) were observed, that were accountable by increases in mean alpha band damping. Significance. These shared increases suggest that, consistent with their identified molecular targets of action, xenon and nitrous oxide are mechanistically similar, a conclusion further supported by neuronal population modelling in which NMDA antagonism is associated with increases in damping and reductions in peak alpha frequency. Alpha band damping may provide an important link between experiment and theories of consciousness, such as the global neuronal network theory, where the likelihood of a globally excited state ('conscious percept'), is inversely related to mean damping.

Objective. Intracortical brain–computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as 'click-and-hold' or 'drag-and-drop'. Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel 'latch decoder' that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task. Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control. Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.

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Objective. Evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) may be useful in the treatment of chronic pain as a control signal for closed-loop neuromodulation. However, considerable inter-individual variability in evoked responses requires robust methods in order to realize effective, personalized pain management. These methods include artifact removal, feature extraction, classification, and prediction. Approach. We recorded ECAPs from eight participants with chronic pain undergoing an externalized trial with two percutaneous leads. The two most caudal electrodes were used for stimulation and the remaining electrodes were used for recording. Artifact-cleaned waveforms were clustered using principal component analysis and classified using a K-Nearest Neighbors classifier as non-ECAPs, ECAPs, or outlier (i.e. artifacts) to determine how well different features, including area under the curve (AUC) and peak-to-peak amplitude (P2P), discriminate between waveform classes. Finally, we used generalized linear mixed effects models to predict evoked response features and the probability of observing artifacts or ECAPs following individual stimulation pulses for different stimulation amplitudes, pulse widths, and polarities. Main results. AUC was better at discriminating between ECAPs and non-ECAPs than P2P (d' = 2.44 vs d' = 2.27) while most features were good at discriminating between ECAPs and artifacts (d' > 1.5). The application of an optimal AUC threshold was then used to analyze individual ECAPs at different stimulation amplitudes, pulse widths, and polarities. Interestingly, ECAPs could be evoked using ∼1.25 mA less current when using participant-specific, preferred stimulation polarities. Conversely, N1 latency consistently correlated with the location of the cathode. Significance. We developed an automated analysis pipeline for individual ECAPs during SCS. AUC was better than the widely used P2P for characterizing evoked responses. Furthermore, our modeling results provide a method for predicting how various stimulation parameters affect SCS responses in individual participants. The study registered on ClinicalTrials.gov (#NCT04938245).

Objective. Neurons exhibit deterministic behavior influenced by stochastic cellular or extracellular components. Estimating this random component is challenging due to unknown underlying deterministic dynamics. In this study, we aim to estimate the neural random component, termed intrinsic dynamic neural noise, from experimental time series without prior assumptions on the underlying neural model. Approach. The method relies on the nonlinear approximate entropy profile and was evaluated using synthetic data from Izhikevich's models and simulated calcium dynamics driven by dynamical noise. We then applied the method to experimental time series from calcium imaging in mice and zebrafish brain regions, as well as electrophysiological data from a 128-channel cortical probe in anesthetized rats. Main results. The results show region-specific behavior, with higher dynamic neural noise in the somatosensory cortex of mice and anterior telencephalic area of zebrafish. Furthermore, neuronal stochasticity is greater in genetically encoded $\text{Ca}^{2+}$ indicators than in $\text{Ca}^{2+}$ dyes, and neural noise increases with recording depth. Significance. These findings offer insights into neural dynamics and suggest dynamic noise as a key biomarker.