Journal of neural engineering最新文献

Objective.High-density nerve cuffs have been successfully utilized to restore somatosensation in individuals with lower-limb loss by interfacing directly with the peripheral nervous system. Elicited sensations via these devices have improved various functional outcomes, including standing balance, walking symmetry, and navigating complex terrains. Deploying neural interfaces in the lower limbs of individuals with limb loss presents unique challenges, particularly due to repetitive muscle contractions and the natural range of motion in the knee and hip joints for transtibial and transfemoral amputees, respectively. This study characterizes the long-term performance of these peripheral nerve interfaces, which is crucial for informing design modifications to optimize functionality.Approach.We evaluated the longitudinal performance of 16-contact nerve cuffs and their associated components implanted in four participants with unilateral transtibial limb loss over five years. Key outcome measures included charge density at sensory thresholds and electrical impedance.Main results.Out of 158 channels (i.e. individual contacts within the nerve cuffs and their corresponding leads), 63% were consistently responsive, 33% were partially responsive, and 4% were non-responsive. Smaller connector assemblies and increased lead length near the cuffs significantly enhanced performance, with the final two participants demonstrating notably improved responses where 77% and 96% of channels were consistently responsive, respectively, compared to 50% and 6% in the first two participants.Significance.Overall, the implanted nerve cuffs showed robust stability in the residual limbs of highly active individuals with limb loss. Furthermore, employing strategies to reduce stress on transition points in the components significantly improved overall system performance.

Objective.Development of cortical visual prostheses requires optimization of evoked responses to electrical stimulation to reduce charge requirements and improve safety, efficiency, and efficacy. One promising approach is timing stimulation to the local field potential (LFP), where action potentials have been found to occur preferentially at specific phases. To assess the relationship between electrical stimulation and the phase of the LFP, we recorded action potentials from primary (V1) and secondary (V2) visual cortex in marmosets while delivering single-pulse electrical microstimulation at different phases of the LFP.Approach.A 64-channel 4 shank probe was inserted into V1 and V2. Microstimulation (single biphasic pulse, 10µA and 200µs per phase) was applied to selected channels in V1, and action potentials recorded simultaneously in V1 and V2. Microstimulation pulses were jittered in time to randomize the phase of the LFP at the time of stimulation.Results.We found frequency-specific phase modulation in a subset of units, where microstimulation in V1 evokes a higher firing rate in both V1 and V2 when delivered at specific phases of the LFP. We characterize phase modulation in terms of the preferred phase and frequency of V1 stimulation for responses in both V1 and V2, and effect size as a function of phase estimation accuracy.Significance.Phase modulation could reduce charge requirements for neural activation, reducing the volume of activated tissue and improving the safety, efficacy, and specificity of cortical visual prostheses. Phase modulation could allow cortical visual prostheses to stimulate using more simultaneous electrodes, with improved neural specificity, and, potentially, targeting downstream cortical activation.

Objective. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy.Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable electroencephalogram-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition.Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures.Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces.

Objective.Measuring causal brain network from neurophysiological signals has recently attracted much attention in the field of neuroinformatics. Traditional data-driven algorithms are computationally time-consuming and unstable due to parameter settings.Approach.To resolve these limits, we proposed a novel parameter-free technique, called 'non-parametric full cross mapping (NFCM)'. The NFCM adapts current convergent cross-mapping concept, and makes two improvements: (1) an improved phase-space reconstruction with constant embedding parameters and (2) cross-mapping estimate of all embedding vectors on manifolds following simplex projection.Main results.Numerical experiments verify that our NFCM has the highest quantization stability even when perturbed by system noise, and its coefficient of variation is almost lower than that of the six baseline methods. The developed NFCM is finally used in stereoelectroencephalogram analysis of drug-resistant epilepsy in children (DREC). A total of 36 seizures, comprising 18 surgical successes and 18 failures, were included to explore the brain network dynamics. The average causal coupling in epileptogenic zones of successful surgery (0.81 ± 0.04) is significantly higher than that in non-epileptogenic zones (0.40 ± 0.03) withP<0.001via Mann-Whitney-U-test. While there is no significant difference among the 18 failed surgeries.Significance.The causal brain network measured by our NFCM is confirmed as a credible biomarker for localizing epileptogenic zones in DREC. These findings promise to advance precision medicine for DREC.

Objective.While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human-machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard. This can lead the myoelectric control system, which is trained on a closed set of gestures and thus unaware of the muscle activity associated with these ADLs, to be falsely activated, leading to erroneous inputs and user frustration.Approach.To overcome this problem, the concept of wake gestures, whereby users could switch between a dedicated control mode and a sleep mode by snapping their fingers, was explored. Using a simple dynamic time warping model, the real-world user-in-the-loop efficacy of wake gestures as a toggle for myoelectric interfaces was demonstrated through two online ubiquitous control tasks with varying levels of difficulty: (1) dismissing an alarm and (2) controlling a robot.Main results.During these online evaluations, the designed system ignored almost all (>99.9%) non-target EMG activity generated during a set of ADLs (i.e. walking, typing, writing, phone use, and driving), ignored all control gestures (i.e. wrist flexion, wrist extension, hand open, and hand close), and enabled reliable mode switching during intentional wake gesture elicitation. Additionally, questionnaires revealed that participants responded well to the use of wake gestures and generally preferred false negatives over false positives, providing valuable insights into the future design of these systems.Significance.These results highlight the real-world viability of wake gestures for enabling the intermittent use of myoelectric control, opening up new interaction possibilities for EMG-based inputs.

Objective.electroencephalography (EEG) and magnetoencephalography (MEG) are widely used non-invasive techniques in clinical and cognitive neuroscience. However, low spatial resolution measurements, partial brain coverage by some sensor arrays, as well as noisy sensors could result in distorted sensor topographies resulting in inaccurate reconstructions of underlying brain dynamics. Solving these problems has been a challenging task. This paper proposes a robust framework based on electromagnetic source imaging for interpolation of unknown or poor quality EEG/MEG measurements.Approach.This framework consists of two steps: (1) estimating brain source activity using a robust inverse algorithm along with the leadfield matrix of available good sensors, and (2) interpolating unknown or poor quality EEG/MEG measurements using the reconstructed brain sources using the leadfield matrices of unknown or poor quality sensors. We evaluate the proposed framework through simulations and several real datasets, comparing its performance to two popular benchmarks-neighborhood interpolation and spherical spline interpolation algorithms.Results.In both simulations and real EEG/MEG measurements, we demonstrate several advantages compared to benchmarks, which are robust to highly correlated brain activity, low signal-to-noise ratio data and accurately estimates cortical dynamics.Significance.These results demonstrate a rigorous platform to enhance the spatial resolution of EEG and MEG, to overcome limitations of partial coverage of EEG/MEG sensor arrays that is particularly relevant to low-channel count optically pumped magnetometer arrays, and to estimate activity in poor/noisy sensors to a certain extent based on the available measurements from other good sensors. Implementation of this framework will enhance the quality of EEG and MEG, thereby expanding the potential applications of these modalities.

Spike sorting is a commonly used analysis method for identifying single-units and multi-units from extracellular recordings. The extracellular recordings contain a mixture of signal components, such as neural and non-neural events, possibly due to motion and breathing artifacts or electrical interference. Identifying single and multi-unit spikes using a simple threshold-crossing method may lead to uncertainty in differentiating the actual neural spikes from non-neural spikes. The traditional method for classifying neural and non-neural units from spike sorting results is manual curation by a trained person. This subjective method suffers from human error and variability and is further complicated by the absence of ground truth in experimental extracellular recordings. Moreover, the manual curation process is time consuming and is becoming intractable due to the growing size and complexity of extracellular datasets. To address these challenges, we, for the first time, present a novel automatic curation method based on an autoencoder model, which is trained on features of simulated extracellular spike waveforms. The model is then applied to experimental electrophysiology datasets, where the reconstruction error is used as the metric for classifying neural and non-neural spikes. As an alternative to the traditional frequency domain and statistical techniques, our proposed method offers a time-domain evaluation model to automate the analysis of extracellular recordings based on learned time-domain features. The model exhibits excellent performance and throughput when applied to real-world extracellular datasets without any retraining, highlighting its generalizability. This method can be integrated into spike sorting pipelines as a pre-processing filtering step or a post-processing curation method.

Objective: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).

Approach: Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak µ frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state µ SMRs.

Main results: After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of µ sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state µ SMRs and µ ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.

Significance: Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.

Objective. Speech comprehension involves detecting words and interpreting their meaning according to the preceding semantic context. This process is thought to be underpinned by a predictive neural system that uses that context to anticipate upcoming words. However, previous studies relied on evaluation metrics designed for continuous univariate sound features, overlooking the discrete and sparse nature of word-level features. This mismatch has limited effect sizes and hampered progress in understanding lexical prediction mechanisms in ecologically-valid experiments.Approach. We investigate these limitations by analyzing both simulated and actual electroencephalography (EEG) signals recorded during a speech comprehension task. We then introduce two novel assessment metrics tailored to capture the neural encoding of lexical surprise, improving upon traditional evaluation approaches.Main results. The proposed metrics demonstrated effect-sizes over 140% larger than those achieved with the conventional temporal response function (TRF) evaluation. These improvements were consistent across both simulated and real EEG datasets.Significance. Our findings substantially advance methods for evaluating lexical prediction in neural data, enabling more precise measurements and deeper insights into how the brain builds predictive representations during speech comprehension. These contributions open new avenues for research into predictive coding mechanisms in naturalistic language processing.

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 motor impairment. Approach:Two investigators subjectively measured iMEP features from thirty-two stroke participants with chronic upper extremity 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 upper extremity 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. .