Journal of neural engineering最新文献

Objective: Transcranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for clinical and research applications. Yet, it remains unclear how the stimulation frequency differentially impacts various neuron types. Here, we aimed to quantify the frequency-dependent behavior of key neocortical cell types.

Approach: We used both detailed (anatomical multicompartments) and simplified (three compartments) single-cell modeling approaches based on the Hodgkin--Huxley formalism to study neocortical excitatory and inhibitory cells under various-amplitude tACS frequencies within the 5-50 Hz range at rest and during basal 10 Hz activity.

Main results: L5 pyramidal cells exhibited the highest polarizability at DC, ranging from 0.21 to 0.25 mm and decaying exponentially with frequency. Inhibitory neurons displayed membrane resonance in the 5-15 Hz range with lower polarizability, although bipolar cells had higher polarizability. Layer 5 PC demonstrated the highest entrainment close to 10 Hz, which decayed with frequency. In contrast, inhibitory neurons entrainment increased with frequency, reaching levels akin to PC. Results from simplified models could replicate phase preferences, while amplitudes tended to follow opposite trends in PC.

Significance: tACS-induced membrane polarization is frequency-dependent, revealing observable resonance behavior. Whilst optimal phase entrainment of sustained activity is achieved in PC when tACS frequency matches endogenous activity, inhibitory neurons tend to be entrained at higher frequencies. Consequently, our results highlight the potential for precise, cell-specific targeting for tACS.

Objective.Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artifacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential.Approach.To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG-fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues.Main results.Rigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks.Significance.This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.

Objective. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics.Approach. This paper introduces a novel multi-model fusion approach, FusionNet-Long Short-Term Memory (LSTM), designed to address these issues. Specifically, it integrates Convolutional Neural Networks for spatial feature extraction, Gated Recurrent Units and LSTM networks for capturing temporal dependencies, and Autoregressive (AR) models for representing signal dynamics.Main results. Compared to single models and state-of-the-art methods, this fusion approach demonstrates substantial improvements in classification accuracy. Experimental results show that the proposed model achieves an accuracy of 87.1% in cross-subject data classification and 99.1% in within-subject data classification. Additionally, Gradient Boosting Trees were employed to evaluate the significance of various EEG features to the model.Significance. This study highlights the advantages of integrating multiple models and introduces a superior classification model, which is pivotal for the advancement of BCI systems.

Objective.We aim to assess the severity of spatial neglect (SN) through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test-conventional lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale provides valuable clinical information, it does not detail the specific FOV affected in neglect patients.Approach.Building on our previously developed EEG-based brain-computer interface system, AR-guided EEG-based neglect detection, assessment, and rehabilitation system (AREEN), we aim to map neglect severity across a patient's FOV. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined spatio-temporal network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with SN. We also propose a FOV correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.Main results.Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.Significance.These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical settings.

Background Brain-computer interfaces (BCIs) have the potential to restore motor capabilities and functional independence in individuals with motor impairments. Despite accelerating advances in the performance of various implanted devices, few studies have identified patient preferences underlying device design, and moreover, each study has typically captured a single aetiology of motor impairment. We aimed to characterise BCI patient preferences in a large patient cohort across multiple aetiologies. Methods We performed a systematic review of all published studies reporting patient preferences for BCI devices. We searched MEDLINE, Embase, and CINAHL from inception to April 18th, 2023. We included any study reporting either qualitative or quantitative preferences concerning BCI devices. Article screening and data extraction were performed by two reviewers in duplicate. Extracted information included demographic information, current digital device use, device invasiveness preference, device design preferences, and device functional preferences. Findings Our search identified 1316 articles, of which 28 studies were eligible for inclusion. Preference information was captured from 1701 patients (mean age = 42.1-64.3 years). Amyotrophic lateral sclerosis was the most represented clinical condition (n = 15 studies, 53.6%), followed by spinal cord injury (n = 13 studies, 46.4%). We found that individuals with motor impairment prioritise device accuracy over other device design characteristics. We also found that the speed and accuracy of BCI systems in recent publications exceeds reported patient preferences, however this performance has been achieved with a level of training and setup burden that would not be tolerated by most patients. When comparing populations across studies, we found that patient preferences vary according to both disease aetiology and the severity of motor impairment. Interpretation Our findings support a greater research emphasis on minimising BCI setup and training burden, and they suggest future BCI devices may require bespoke configuration and training for specific patient groups. .

Objective: Visual prostheses are effective tools for restoring vision, yet real-world complexities pose ongoing challenges. The progress in AI has led to the emergence of the concept of intelligent visual prosthetics with auditory support, leveraging deep learning to create practical artificial vision perception beyond merely restoring natural sight for the blind.

Approach: This study introduces an object-based attention mechanism that simulates human gaze points when observing the external world to descriptions of physical regions. By transforming this mechanism into a ranking problem of salient entity regions, we introduce prior visual attention cues to build a new salient object ranking dataset, and propose a salient object ranking (SaOR) network aimed at providing depth perception for prosthetic vision. Furthermore, we propose a SaOR-guided image description method to align with human observation patterns, toward providing additional visual information by auditory feedback. Finally, the integration of the two aforementioned algorithms constitutes an audiovisual cognitive optimization strategy for prosthetic vision.

Main results: Through conducting psychophysical experiments based on scene description tasks under simulated prosthetic vision, we verify that the SaOR method improves the subjects' performance in terms of object identification and understanding the correlation among objects. Additionally, the cognitive optimization strategy incorporating image description further enhances their prosthetic visual cognition.

Significance: This offers valuable technical insights for designing next-generation intelligent visual prostheses and establishes a theoretical groundwork for developing their visual information processing strategies. Code will be made publicly available.

Objective: Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control. Approach: Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings. Main Results: Dataset aggregation reduced the root-mean-squared error of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets. Significance: Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.

Objective Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces (BCI) based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex. Approach Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex. Main Results Response models were obtained by analyzing, EEG data (n=8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the Steady-State Visual Evoked Potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controler's Linear, Time-Invariant (LTI) models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability. Significance In silico and in vivo data matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits. .

Objective.Phrenic nerve stimulation reduces ventilator-induced-diaphragmatic-dysfunction, which is a potential complication of mechanical ventilation. Electromagnetic simulations provide valuable information about the effects of the stimulation and are used to determine appropriate stimulation parameters and evaluate possible co-activation.Approach.Using a multiscale approach, we built a novel detailed anatomical model of the neck and the phrenic nerve. The model consisted of a macroscale volume conduction model of the neck with 13 tissues, a mesoscale volume conduction model of the phrenic nerve with three tissues, and a microscale biophysiological model of axons with diameters ranging from 5 to 14 µm based on the McIntyre-Richardson-Grill-model for myelinated axons. This multiscale model was used to quantify activation thresholds of phrenic nerve fibers using different stimulation pulse parameters (pulse width, interphase delay, asymmetry of biphasic pulses, pulse polarity, and rise time) during non-invasive electrical stimulation. Electric field strength was used to evaluate co-activation of the other nerves in the neck.Main results.For monophasic pulses with a pulse width of 150 µs, the activation threshold depended on the fiber diameter and ranged from 20 to 156 mA, with highest activation threshold for the smallest fiber diameter. The relationship was approximated using a power fit functionx^-3. Biphasic (symmetric) pulses increased the activation threshold by 25 to 30 %. The use of asymmetric biphasic pulses or an interphase delay lowered the threshold close to the monophasic threshold. Possible co-activated nerves were the more superficial nerves and included the transverse cervical nerve, the supraclavicular nerve, the great auricular nerve, the cervical plexus, the brachial plexus, and the long thoracic nerve.Significance.Our multiscale model and electromagnetic simulations provided insight into phrenic nerve activation and possible co-activation by non-invasive electrical stimulation and provided guidance on the use of stimulation pulse types with minimal activation threshold.

Objective: Kilohertz (kHz) frequency stimulation has gained attention as a neuromodulation therapy in spinal cord and in peripheral nerve block applications, mainly for treating chronic pain. Yet, few studies have investigated the effects of high-frequency stimulation on the performance of the electrode materials. In this work, we assess the electrochemical characteristics and stability of sputtered iridium oxide film (SIROF) microelectrodes under kHz frequency pulsed electrical stimulation.

Approach: SIROF microelectrodes were subjected to 1.5-10 kHz pulsing at charge densities of 250-1000 µC cm^-2(25-100 nC phase^-1), under monopolar and bipolar configurations, in buffered saline solution. The electrochemical behavior as well as the long-term stability of the pulsed electrodes was evaluated by voltage transient, cyclic voltammetry, and electrochemical impedance spectroscopy measurements.

Main results: Electrode polarization was more pronounced at higher stimulation frequencies in both monopolar and bipolar configurations. Bipolar stimulation resulted in an overall higher level of polarization than monopolar stimulation with the same parameters. In all tested pulsing conditions, except one, the maximum cathodal and anodal potential excursions stayed within the water window of iridium oxide (-0.6 to 0.8 V vs Ag|AgCl). Additionally, these SIROF microelectrodes showed little or no changes in the electrochemical performance under continuous current pulsing at frequencies up to 10 kHz for more than 10⁹pulses.

Significance: Our results suggest that 10,000 μm²SIROF microelectrodes can deliver high-frequency neural stimulation up to 10 kHz in buffered saline at charge densities between 250 and 1000 µC cm^-2(25-100 nC phase^-1).