Journal of neural engineering最新文献

Objective.To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.Approach.In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searchingin silico. Main results.We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjectsin vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject's safety threshold.Significance.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.

Objective. Previous preclinical and clinical studies have demonstrated that pudendal nerve is a promising target for restoring bladder control. The spatial proximity between the pudendal nerve and its accompanying blood vessels in the pudendal canal provides an opportunity for endovascular neurostimulation, which is a less invasive approach compared to conventional chronically implanted electrodes. In this study, we investigated the feasibility of excitatory stimulation and kilohertz-frequency block of the compound pudendal nerve in sheep using a stent-mounted electrode array.Approach. In a set of acute animal experiments, a commercially available hexapolar electrode catheter was introduced in the unilateral internal pudendal artery to deliver bipolar electrical stimulation of the adjacent compound pudendal nerve. The catheter electrode was replaced with a custom-made stent-mounted electrode array and the stimulation sessions were repeated. Global electromyogram activity of the pelvic floor and related sphincter muscles was recorded with a monopolar electrode placed within the urethra concurrently.Main results. We demonstrated the feasibility of endovascular stimulation of the pudendal nerve with both electrode types. The threshold current of endovascular stimulation was influenced by electrode-nerve distance and electrode orientation. Increasing the axial inter-electrode distance significantly decreased threshold current. Endovascular kilohertz-frequency nerve block was possible with the electrode catheter.Significance. The present study demonstrated that endovascular stimulation of the pudendal nerve with the stent-mounted electrode array may be a promising less invasive alternative to conventional implantable electrodes, which has important clinical implications in the treatment of urinary incontinence. Endovascular blocking of pudendal nerve may provide an alternative solution to the bladder-sphincter dyssynergia problem in bladder management for people with spinal cord injury.

Objective.While brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is widely used in target detection, patterns of event-related potential (ERP), as well as the performance on detecting inconspicuous targets remain unknown. Moreover, participant-screening methods to excluded 'BCI-blind' users are still lacking.Approach.A RSVP paradigm was designed with targets of varied concealment, size, and location. ERPs (e.g. P300 and N2pc) and target detection accuracy were compared among these conditions. The relationship between participants' attention scores and target detection accuracy was also analyzed to test attention level as a criterion for participant screening.Main results.Statistical analysis showed that the conditions of target concealment and size significantly influenced ERP. In particular, ERP for inconspicuous targets, such as concealed and small targets, exhibited lower amplitudes and longer latencies. In consistent, the accuracy of detection in inconspicuous condition was significantly lower than that of conspicuous condition. In addition, a significant association was found between attention scores and target detection accuracy for camouflaged targets.Significance.The study was the first to address ERP features among multiple dimensions of concealment, size, and location. The conclusion provided insights into the relationship between ERP decoding and properties of targets. In addition, the association between attention scores and detection accuracy implied a promising method in screening well-behaved participants for camouflaged target detection.

Objective. Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Freyet alprovided an exciting deep learning method for learning eye movements from functional magnetic resonance imaging (fMRI) data. It employed the multi-step co-registration of fMRI into the group template to obtain eyeball signal, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space.Approach. The MRGazer consists of an eyeball extraction module and a residual network-based eye gaze prediction module. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol, and achieves end-to-end eye gaze regression.Main results. The proposed method achieved superior performance in eye fixation regression (Euclidean error, EE = 2.04°) than the co-registration-based method (EE = 2.89°), and delivered objective results within a shorter time (∼0.02 s volume^-1) than prior method (∼0.3 s volume^-1).Significance. The MRGazer is an efficient, simple, and accurate deep learning framework for predicting eye movement from fMRI data, and can be employed during fMRI scans in psychological and cognitive research. The code is available athttps://github.com/ustc-bmec/MRGazer.

Objective.Accurate neuron identification is fundamental to the analysis of neuronal population dynamics and signal extraction in fluorescence videos. However, several factors such as severe imaging noise, out-of-focus neuropil contamination, and adjacent neuron overlap would impair the performance of neuron identification algorithms and lead to errors in neuron shape and calcium activity extraction, or ultimately compromise the reliability of analysis conclusions.Approach.To address these challenges, we developed a novel cascade framework named SomaSeg. This framework integrates Duffing denoising and neuropil contamination defogging for video enhancement, and an overlapping instance segmentation network for stacked neurons differentiating.Main results.Compared with the state-of-the-art neuron identification methods, both simulation and actual experimental results demonstrate that SomaSeg framework is robust to noise, insensitive to out-of-focus contamination and effective in dealing with overlapping neurons in actual complex imaging scenarios.Significance.The SomaSeg framework provides a widely applicable solution for two-photon video processing, which enhances the reliability of neuron identification and exhibits value in distinguishing visually confusing neurons.

Objective.In recent years, the robot assisted (RA) rehabilitation training has been widely used to counteract defects of the manual one provided by physiotherapists. However, since the proprioception feedback provided by the robotic assistance or the manual methods is relatively weak for the paralyzed patients, their rehabilitation efficiency is still limited. In this study, a dynamic electrical stimulation (DES) based proprioception enhancement and the associated quantitative analysis methods have been proposed to overcome the limitation mentioned above.Approach.Firstly, the DES based proprioception enhancement method was proposed for the RA neural rehabilitation. In the method, the relationship between the surface electromyogram (sEMG) envelope of the specified muscle and the associated joint angles was constructed, and the electrical stimulation (ES) pulses for the certain joint angles were designed by consideration of the corresponding sEMG envelope, based on which the ES can be dynamically regulated during the rehabilitation training. Secondly, power spectral density, source estimation, and event-related desynchronization of electroencephalogram, were combinedly used to quantitatively analyze the proprioception from multiple perspectives, based on which more comprehensive and reliable analysis results can be obtained. Thirdly, four modes of rehabilitation training tasks, namely active, RA, DES-RA, and ES-only training, were designed for the comparison experiment and validation of the proposed DES based proprioception enhancement method.Main results.The results indicated that the activation of the sensorimotor cortex was significantly enhanced when the DES was added, and the cortex activation for the DES-RA training was similar to that for the active training. Meanwhile, relatively consistent results from the multiple perspectives were obtained, which validates the effectiveness and robustness of the proposed proprioception analysis method.Significance.The proposed methods have the potential to be applied in the practical rehabilitation training to improve the rehabilitation efficiency.

Objective.This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. Trust in these models can be established by incorporating reasoning or causal relationships from domain experts. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding, often used interchangeably in this context, and formulate a comprehensive framework.Approach.To understand the need of XAI for BCI, we pose six key research questions for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology-preferred reporting items for systematic reviews and meta-analyses to review (n = 1246) and analyse (n = 84) studies published in 2015 and onwards for key insights.Main results.The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualise and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.Significance.This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important discussions on establishing standards for BCI explanations, highlighting current limitations, and guiding the future of XAI in BCI.

Objective.The transcranial magnetic stimulation (TMS) coil induces an electric field that diminishes rapidly upon entering the brain. This presents a challenge in achieving focal stimulation of a deep brain structure. Neuronal elements, including axons, dendrites, and cell bodies, exhibit specific time constants. When exposed to repetitive TMS pulses at a high frequency, there is a cumulative effect on neuronal membrane potentials, resulting in temporal summation. This study aims to determine whether TMS pulse train at high-frequency and subthreshold intensity could induce a suprathreshold response.Approach.As a proof of concept, we developed a TMS machine in-house that could consistently output pulses up to 250 Hz, and performed experiments on 22 awake rats to test whether temporal summation was detectable under pulse trains at 100, 166, or 250 Hz.Main results.Results revealed that TMS pulses at 55% maximum stimulator output (MSO, peak dI/dt= 68.5 A/μs at 100% MSO, pulse width = 48μs) did not induce motor responses with either single pulses or pulse trains. Similarly, a single TMS pulse at 65% MSO failed to evoke a motor response in rats; however, a train of TMS pulses at frequencies of 166 and 250 Hz, but not at 100 Hz, successfully triggered motor responses and MEP signals, suggesting a temporal summation effect dependent on both pulse intensities and pulse train frequencies.Significance.We propose that the temporal summation effect can be leveraged to design the next-generation focal TMS system: by sequentially driving multiple coils at high-frequency and subthreshold intensity, areas with the most significant overlapping E-fields undergo maximal temporal summation effects, resulting in a suprathreshold response.

Objective. Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ('RP', the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established 'shuffling' procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach. In a novel 'residuals' method, we first estimate the RP duration (n_r) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the precedingn_rmilliseconds. Finally, we compute the PSD of the model's residuals.Main results. We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey-in which alpha-beta oscillations (8-30 Hz) were anticipated-the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance. These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.