Journal of neural engineering最新文献

Objective.Brain-computer interfaces (BCIs) can support non-muscular communication and device control for severely paralyzed people. However, efforts that directly involve potential or actual end-users and address their individual needs are scarce, demonstrating a translational gap. An online BCI forum supported by the BCI Society could initiate and sustainably strengthen interactions between BCI researchers and end-users to bridge this gap.Approach.We interviewed six severely paralyzed individuals and surveyed 121 BCI researchers to capture their opinions and wishes concerning an online BCI forum. Data were analyzed with a mixed-method quantitative and qualitative content analysis.Main results.All end-users and most researchers (83%) reported an interest in participating in an online BCI forum. Rating questions and open comments to identify design aspects included what should be featured most prominently, how people would get engaged in the online BCI forum, and which pitfalls should be considered.Significance.Responses support establishing an online BCI forum to serve as a meaningful resource for the entire BCI community.

Objective. Evoked compound action potentials (ECAPs) measured during epidural spinal cord stimulation (SCS) can help elucidate fundamental mechanisms for the treatment of pain and inform closed-loop control of SCS. Previous studies have used ECAPs to characterize neural responses to various neuromodulation therapies and have demonstrated that ECAPs are highly prone to multiple sources of artifact, including post-stimulus pulse capacitive artifact, electromyography (EMG) bleed-through, and motion artifact. However, a thorough characterization has yet to be performed for how these sources of artifact may contaminate recordings within the temporal window commonly used to determine activation of A-beta fibers in a large animal model.Approach. We characterized sources of artifacts that can contaminate the recording of ECAPs in an epidural SCS swine model using the Abbott Octrode™ lead.Main results. Spinal ECAP recordings can be contaminated by capacitive artifact, short latency EMG from nearby muscles of the back, and motion artifact. The capacitive artifact can appear nearly identical in duration and waveshape to evoked A-beta responses. EMG bleed-through can have phase shifts across the electrode array, similar to the phase shift anticipated by propagation of an evoked A-beta fiber response. The short latency EMG is often evident at currents similar to those needed to activate A-beta fibers associated with the treatment of pain. Changes in CSF between the cord and dura, and motion induced during breathing created a cyclic oscillation in all evoked components of recorded ECAPs.Significance. Controls must be implemented to separate neural signal from sources of artifact in SCS ECAPs. We suggest experimental procedures and reporting requirements necessary to disambiguate underlying neural response from these confounds. These data are important to better understand the framework for epidural spinal recordings (ESRs), with components such as ECAPs, EMG, and artifacts, and have important implications for closed-loop control algorithms to account for transient motion such as postural changes and cough.

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 integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for Brain-computer interface (BCI), where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes.Approach.Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the Phase-SPDNet architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark framework.Main results.The results of our Phase-SPDNet demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding.Significance.This new architecture is explainable and with a low number of trainable parameters.

Objective: Elucidating neurological processes in the mammalian brain requires improved methods for imaging and detecting neuronal subtypes. Transgenic mouse models utilizing Cre/lox recombination have been developed to selectively label neuronal subtypes with fluorophores, however, light-scattering attenuation of both excitation light and emission light limits their effective range of detection.

Approach: To overcome these limitations, this study investigates the use of a near-infrared fluorophore, iRFP713, for subtype labeling of neurons found within brain regions that are typically inaccessible by optical methods. Towards this goal, a custom photoacoustic system is developed for detection of iRFP in neurons in brain slices, expressed via Cre/lox, and within in vitro cell culture.

Results: In this study, a custom system is developed to detect iRFP in neuronal cells both in brain slices and in vitro. Furthermore, this work validates iRFP expression in the brains of transgenic mice and neuronal cell culture.

Objective. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning distributions in the deep learning model's feature space is more effective for classification.Approach. We introduce the Latent Alignment method, which won the Benchmarks for EEG Transfer Learning competition. This method can be formulated as a deep set architecture applied to trials from a given subject, introducing deep sets to EEG decoding for the first time. We compare Latent Alignment to recent statistical domain adaptation techniques, carefully considering class-discriminative artifacts and the impact of class distributions on classification performance.Main results. Our experiments across motor imagery, sleep stage classification, and P300 event-related potential tasks validate Latent Alignment's effectiveness. We identify a trade-off between improved classification accuracy when alignment is performed at later modeling stages and increased susceptibility to class imbalance in the trial set used for statistical computation.Significance. Latent Alignment offers consistent improvements to subject-independent deep learning models for EEG decoding when relevant practical considerations are addressed. This work advances our understanding of statistical alignment techniques in EEG decoding and provides insights for their effective implementation in real-world BCI applications, potentially facilitating broader use of BCIs in healthcare, assistive technologies, and beyond. The model code is available athttps://github.com/StylianosBakas/LatentAlignment.

Objective.Transcranial electrical stimulation (TES) is an effective technique to modulate brain activity and treat diseases. However, TES is primarily used to stimulate superficial brain regions and is unable to reach deeper targets. The spread of injected currents in the head is affected by volume conduction and the additional spreading of currents as they move through head layers with different conductivities, as is discussed in Forssellet al(2021J. Neural Eng.18046042). In this paper, we introduce DeepFocus, a technique aimed at stimulating deep brain structures in the brain's 'reward circuit' (e.g. the orbitofrontal cortex, Brodmann area 25, amygdala, etc).Approach.To accomplish this, DeepFocus utilizes transnasal electrode placement (under the cribriform plate and within the sphenoid sinus) in addition to electrodes placed on the scalp, and optimizes current injection patterns across these electrodes. To quantify the benefit of DeepFocus, we develop the DeepROAST simulation and optimization platform. DeepROAST simulates the effect of complex skull-base bones' geometries on the electric fields generated by DeepFocus configurations using realistic head models. It also uses optimization methods to search for focal and efficient current injection patterns, which we use in our simulation and cadaver studies.Main results.In simulations, optimized DeepFocus patterns created larger and more focal fields in several regions of interest than scalp-only electrodes. In cadaver studies, DeepFocus patterns created large fields at the medial orbitofrontal cortex (OFC) with magnitudes comparable to stimulation studies, and, in conjunction with established cortical stimulation thresholds, suggest that the field intensity is sufficient to create neural response, e.g. at the OFC.Significance.This minimally invasive stimulation technique can enable more efficient and less risky targeting of deep brain structures to treat multiple neural conditions.

Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.Approach.This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning (DL). Specifically, this study enhanced SSVEP features using global template alignment and discriminant spatial pattern, and then designed a compacted temporal-spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.Main Results.The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, DL methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset. The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.Significance.This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.

Objective.Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices. However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. This article describes assist-as-needed sensor-based shared control (SC) methods relying on the blending of BCI and depth-sensor-based control.Approach.The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram recording implant named WIMAGINE.Main results.Results showed that the proposed methods can assist BCI users in both tasks. Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided.Significance.The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed SC methods improved the performance of the two assistive devices.Clinical trial, registration number: NCT02550522.

Objective. Axonal demyelination leads to failure of axonal conduction. Current research on demyelination focuses on the promotion of remyelination. Electromagnetic stimulation is widely used to promote neural activity. We hypothesized that electromagnetic stimulation of the demyelinated area, by providing excitation to the nodes of Ranvier, could rescue locally demyelinated axons from conductance failure.Approach. We built a multi-compartment NEURON model of a myelinated axon under electromagnetic stimulation. We simulated the action potential (AP) propagation and observed conductance failure when local demyelination occurred. Conductance failure was due to current leakage and a lack of activation of the nodes in the demyelinated region. To investigate the effects of electromagnetic stimulation on locally demyelinated axons, we positioned a miniature coil next to the affected area to activate nodes in the demyelinated region.Main results. Subthreshold microcoil stimulation caused depolarization of node membranes. This depolarization, in combination with membrane depolarization induced by the invading AP, resulted in sufficient activation of nodes in the demyelinated region and restoration of axonal conductance. Efficacy of restoration was dependent on the amplitude and frequency of the stimuli, and the location of the microcoil relative to the targeted nodes. The restored axonal conductance was due to the enhanced Na⁺current and reduced K⁺current in the nodes, rather than a reduction in leakage current in the demyelinated region. Finally, we found that microcoil stimulation had no effect on axonal conductance in healthy, myelinated axons.Significance. Activation of nodes in the demyelinated region using electromagnetic stimulation provides an alternative treatment strategy to restore axonal function under local demyelination conditions. Results provide insights to the development of microcoil technology for the treatment of focal segmental demyelination cases, such as neuropraxia, spinal cord injury, and auditory nerve demyelination.