Journal of neural engineering最新文献

Objective. The phase of the electroencephalographic (EEG) signal predicts performance in motor, somatosensory, and cognitive functions. Studies suggest that brain phase resets align neural oscillations with external stimuli, or couple oscillations across frequency bands and brain regions. Transcranial Magnetic Stimulation (TMS) can cause phase resets noninvasively in the cortex, thus providing the potential to control phase-sensitive cognitive functions. However, the relationship between TMS parameters and phase resetting is not fully understood. This is especially true of TMS intensity, which may be crucial to enabling precise control over the amount of phase resetting that is induced. Additionally, TMS phase resetting may interact with the instantaneous phase of the brain. Understanding these relationships is crucial to the development of more powerful and controllable stimulation protocols.Approach.To test these relationships, we conducted a TMS-EEG study. We applied single-pulse TMS at varying degrees of stimulation intensity to the motor area in an open loop. Offline, we used an autoregressive algorithm to estimate the phase of the intrinsicµ-Alpha rhythm of the motor cortex at the moment each TMS pulse was delivered.Main results. We identified post-stimulation epochs whereµ-Alpha phase resetting and N100 amplitude depend parametrically on TMS intensity and are significantversusperipheral auditory sham stimulation. We observedµ-Alpha phase inversion after stimulations near peaks but not troughs in the endogenousµ-Alpha rhythm.Significance. These data suggest that low-intensity TMS primarily resets existing oscillations, while at higher intensities TMS may activate previously silent neurons, but only when endogenous oscillations are near the peak phase. These data can guide future studies that seek to induce phase resetting, and point to a way to manipulate the phase resetting effect of TMS by varying only the timing of the pulse with respect to ongoing brain activity.

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

textbf{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 BCI, where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography (EEG) 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. textbf{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 method{} 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 (MOABB) framework. textbf{Main results:} The results of our method{} demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. textbf{Significance:} This new architecture is explainable and with a low number of trainable parameters.

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected.

Approach: We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.

Main results: Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations.

Significance: Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.

Objective: Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols. Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level. Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand. Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their affected hand. Interestingly, brain features performed better in this latter condition with respect to healthy subjects. Significance. Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.

Background. Tremor is a cardinal symptom of Parkinson's disease (PD) that manifests itself through complex oscillatory activity across multiple neuronal populations. According to the finger-dimmer-switch (FDS) theory, tremor is triggered by transient pathological activity in the basal ganglia-thalamo-cortical (BTC) network (the finger) and transitions into an oscillatory form within the inner circuitry of the thalamus (the switch). The cerebello-thalamo-cortical (CTC) network (the dimmer) is then involved in sustaining and amplifying tremor amplitude. In this study, we aimed to investigate the generation and progression dynamics of PD tremor oscillations by developing a comprehensive and interacting FDS model that transitions sequentially from healthy to PD to tremor and then to tremor-off state.Methods.We constructed a computational model consisting of 700 neurons in 11 regions of BTC, CTC, and thalamic networks. Transition from healthy to PD state was simulated through modulating dopaminergic synaptic connections; and further from PD to tremor and tremor-off by modulating projections between the thalamic reticular nucleus (TRN), anterior ventrolateral nucleus (VLa), and posterior ventrolateral nucleus (VLp).Results.Sustained oscillations in the frequency range of PD tremor emerged in thalamic VLp (5 Hz) and cerebellar dentate nucleus (3 Hz). Increasing self-inhibition in the thalamus through dopaminergic modulation significantly decreased tremor amplitude.Conclusion/Significance.Our results confirm the mechanistic power of the FDS theory in describing the PD tremor phenomenon and emphasize the role of dopaminergic modulation on thalamic self-inhibition. These insights pave the way for novel therapeutic strategies aimed at reducing the tremor by strengthening thalamic self-inhibition, particularly in dopamine-resistant patients.

Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries.This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction. For example, it allows to read out movement intentions from recordings of brain signals for controlling a prosthesis or an exoskeleton. To enable this, computationally complex algorithms are used in research that cannot be executed on-chip or on embedded systems. Therefore, an optimization of the end-to-end processing pipeline, from the signal condition on the electrode array over the analog pre-processing to spike-sorting and finally the neural decoding process, is necessary for hardware inference in order to enable a local signal processing in real-time and to enable a compact system for achieving a high comfort level. This paper presents a survey of system architectures and algorithms for end-to-end signal processing pipelines of neural activity on the hardware of such neural devices, including (i) on-chip signal pre-processing, (ii) spike-sorting on-chip or on embedded hardware and (iii) neural decoding on workstations. A particular focus for the hardware implementation is on low-power electronic design and artifact-robust algorithms with low computational effort and very short latency. For this, current challenges and possible solutions with support of novel machine learning techniques are presented in brief. In addition, we describe our future vision for next-generation BCIs.

Objective.For prosthesis users, sensory feedback that appears to come from the missing limb can improve function, confidence, and phantom limb pain. Numerous pre-clinical studies have considered stimulation via penetrating microelectrodes at the dorsal root ganglion (DRG) as a potential approach for somatosensory neuroprostheses. However, to develop clinically translatable neuroprosthetic devices, a less invasive approach, such as stimulation via epineural macroelectrodes, would be preferable. This work explores the feasibility of using such electrodes to deliver focal sensory feedback by examining the mechanisms of selective activation in response to stimulation via epineural electrodes compared with penetrating electrodes.Approach.We developed computational models of the DRG, representing the biophysical properties of the DRG and surrounding tissue to evaluate neural responses to stimulation via penetrating microelectrodes and epineural macroelectrodes. To assess the role of properties such as neuron morphology and spatial arrangement we designed three models, including one that contained only axons (axon only), one with pseudounipolar neurons arranged randomly (random), and one with pseudounipolar neurons placed according to a realistic spatial distribution (realistic).Main results.Our models demonstrate that activation in response to stimulation via epineural electrodes in a realistic model is commonly initiated in the axon initial segment adjacent to the cell body, whereas penetrating electrodes commonly elicit responses in t-junctions and axons. Moreover, we see a wider dynamic range for epineural electrodes compared with penetrating electrodes. This difference appears to be driven by the spatial organization and neuron morphology of the realistic DRG.Significance.We demonstrate that the anatomical features of the DRG make it a potentially effective target for epineural stimulation to deliver focal sensations from the limbs. Specifically, we show that epineural stimulation at the DRG can be highly selective thanks to the neuroanatomical arrangement of the DRG, making this a promising approach for future neuroprosthetic development.

Objective.Attention is a multifaceted cognitive process, with nonlinear dynamics playing a crucial role. We investigated the involvement of nonlinear processes in top-down visual attention.Approach.The research paradigm employed a contrast-modulated sequence of letters and numerals, encircled by a consistently flickering white square on a black background-a setup that generated steady-state visually evoked potentials. Nonlinear processes are recognized for eliciting and modulating the harmonics of constant frequencies. Using the rhythmic entrainment source separation technique, we examined the fundamental and harmonic frequencies of each stimulus to evaluate the underlying nonlinear dynamics during stimulus processing.Main results.In line with prior research, our findings indicate that the power spectrum density of electroencephalogram responses is influenced by both task presence and stimulus contrast. We discovered that actively searching for a target within a letter stream heightened the amplitude of the fundamental frequency and harmonics related to the background flickering stimulus. While the fundamental frequency amplitude remained unaffected by the stimulus contrast, a lower contrast led to an increase in the second harmonic's amplitude. We assessed the relationship between the contrast response function and the nonlinear-based harmonic responses.Significance.Our findings contribute to a more nuanced understanding of the nonlinear processes impacting top-down visual attention.

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.