Objective . Silent speech recognition based on surface electromyography has been studied for years. Though some progress in feature selection and classification has been achieved, one major problem remains: how to provide confident or reliable prediction. Approach . Inductive conformal prediction (ICP) is a suitable and effective method to tackle this problem. This paper applies ICP with the underlying

Objective. The purpose of this study was to determine the effects of spinal cord injury (SCI) on spike activity evoked in the hindlimb spinal cord of the rat from cortical electrical stimulation. Approach. Adult, male, Sprague Dawley rats were randomly assigned to a Healthy or SCI group. SCI rats were given a 175 kDyn dorsal midline contusion injury at the level of the T8 vertebrae. At 4 weeks post-SCI

Objective. The goal of this work is to identify the spatio-temporal facets of state-of-the-art electroencephalography (EEG)-based continuous neurorobotics that need to be addressed, prior to deployment in practical applications at home and in the clinic. Approach. Nine healthy human subjects participated in five sessions of one-dimensional (1D) horizontal (LR), 1D vertical (UD) and two-dimensional

Objective. There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus—a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based

Objective. The spatial resolution of an implantable neural stimulator can be improved by creation of virtual channels (VCs). VCs are commonly achieved through synchronized stimulation of multiple electrodes. It remains unknown whether asynchronous stimulation is able to generate comparable VC performance in retinal stimulation, and how VC can be optimized by re-designing stimulation settings. This

In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have

Objective. For nonstationarity of neural recordings, daily retraining is required in the decoder calibration of intracortical brain-machine interfaces (iBMIs). Domain adaptation (DA) has started to be applied in iBMIs to solve the problem of daily retraining by taking advantage of historical data. However, previous DA studies used only a single source domain, which might lead to performance instability

Objective . To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the ‘hand knob’ area

Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of interest is first selected and dynamic analysis then proceeds at this hypothesised level of complexity. If large-scale functional networks or states are

Objective. Our study aims to investigate the feasibility of in-ear sensing for human–computer interface. Approach. We first measured the agreement between in-ear biopotential and scalp-electroencephalogram (EEG) signals by channel correlation and power spectral density analysis. Then we applied EEG compact network (EEGNet) for the classification of a two-class motor task using in-ear electrophysiological

Objective . While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. Approach . Here we present a technology platform combining

Objective. Brain-computer interface (BCI) technology based on motor imagery (MI) control has become a research hotspot but continues to encounter numerous challenges. BCI can assist in the recovery of stroke patients and serve as a key technology in robot control. Current research on MI almost exclusively focuses on the hands, feet, and tongue. Therefore, the purpose of this paper is to establish a

Objective . Greater occipital nerve (GON) block may provide substantial relief for headache in the occipital location. This study tested the feasibility of focused ultrasound (FUS) to induce the conduction block of GONs in rats. Approach . For in vitro experiments, the nerve was dissected and cut from C2 to the site near the ear of the rats and preserved in Ringer’s solution. Pulsed FUS was used for

Objective. Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network’s local and global connectivity to these patterns and information processing remains largely unknown. Approach

Objective. Compared to electrical stimulation, optogenetic stimulation has the potential to improve the spatial precision of neural activation in neuroprostheses, but it requires intense light and has relatively poor temporal kinetics. We tested the effect of hybrid stimulation, which is the combination of subthreshold optical and electrical stimuli, on spectral and temporal fidelity in the cochlea

Objective . One of the main goals in brain–computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information

Objective. Retinal prostheses hold the potential for artificial vision in blind patients suffering from outer retinal dystrophies. The optimal number, density and coverage of the electrodes that a retinal prosthesis should have to provide adequate artificial vision in daily activities is still an open question and an important design parameter needed to develop better implants. Approach. To address

Objective. Pattern-recognition-based myocontrol can be unreliable, which may limit its use in the clinical practice and everyday activities. One cause for this is the poor generalization of the underlying machine learning models to untrained conditions. Acquiring the training data and building the model more interactively can reduce this problem. For example, the user could be encouraged to target

Objective . The construction of in vitro three-dimensional (3D) neural tissue has to overcome two main types of challenges: (1) How to obtain enough number of functional neurons from stem cells in 3D culture; (2) How to wire those lately developed neurons into functional neural networks. Here, we describe the potential of using direct current (DC) electric field (EF) together with basic fibroblast

Objective . The main objective of this research was to study the coupling between neural circuits and the vascular network in the cortex of small rodents from system engineering point of view and generate a mathematical model for the dynamics of neurovascular coupling. The model was adopted to implement closed-loop blood flow control algorithms. Approach. We used a combination of advanced technologies

Objective . We explore the long-term performance and stability of seven percutaneous Utah Slanted Electrode Arrays (USEAs) and intramuscular recording leads (iEMGs) implanted chronically in the residual arm nerves and muscles of three human participants as a means to permanently restore sensorimotor function after transradial amputations. Approach . We quantify the number of functional recording and

Age-related macular degeneration is an incurable chronic neurodegenerative disease, causing progressive loss of the central vision and even blindness. Up-to-date therapeutic approaches can only slow down he progression of the disease. Objective. Feasibility study for a multilayered, silk fibroin-based, 3D biohybrid retina. Approach . Fabrication of silk fibroin-based biofilms; culture of different

Objective. This study employs a human head model with real skull to demonstrate the feasibility of transcranial acoustoelectric brain imaging (tABI) as a new modality for electrical mapping of deep dipole sources during treatment of epilepsy with much better resolution and accuracy than conventional mapping methods. Approach. This technique exploits an interaction between a focused ultrasound (US)

Objective . Brain-computer interface (BCI) systems read and interpret brain activity directly from the brain. They can provide a means of communication or locomotion for patients suffering from neurodegenerative diseases or stroke. However, non-stationarity of brain activity limits the reliable transfer of the algorithms that were trained during a calibration session to real-time BCI control. One source

Objective. The goal of this work is to develop and characterize an innovative experimental framework to design interconnected (i.e. modular) heterogeneous (cortical-hippocampal) neuronal cultures with a three-dimensional (3D) connectivity and to record their electrophysiological activity using micro-electrode arrays (MEAs). Approach. A two-compartment polymeric mask for the segregation of different

Objective . Direct electrical stimulation of the brain through intracranial electrodes is currently used to probe the epileptic brain as part of pre-surgical evaluation, and it is also being considered for therapeutic treatments through neuromodulation. In order to effectively modulate neural activity, a given neuromodulation design must elicit similar responses throughout the course of treatment.

Objective . This study aims to investigate alterations of brain connectivity using multivariate electroencephalographic data to provide new insights of the brain connectivity dynamics of dystonia. Approach . We recorded electroencephalography (EEG) of patients with right upper limb idiopathic focal dystonia and paired controls during resting state, writing-from-memory, and finger-tapping tasks. We

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not

Objective. Electrical stimulation via microelectrodes implanted in cortex has been suggested as a potential treatment for a wide range of neurological disorders. Despite some success however, the effectiveness of conventional electrodes remains limited, in part due to an inability to create specific patterns of neural activity around each electrode and in part due to challenges with maintaining a stable

Objective . It is important to improve identification accuracy for possible early intervention of major depressive disorder (MDD). Recently, effective connectivity (EC), defined as the directed influence of spatially distant brain regions on each other, has been used to find the dysfunctional organization of brain networks in MDD. However, little is known about the ability of whole-brain resting-state

Objective. Magnetoencephalography (MEG) based brain–computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily

A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve

Objective . Algorithms to detect changes in cognitive load using non-invasive biosensors (e.g. electroencephalography (EEG)) have the potential to improve human–computer interactions by adapting systems to an individual’s current information processing capacity, which may enhance performance and mitigate costly errors. However, for algorithms to provide maximal utility, they must be able to detect

Objective. Implantable electrodes, such as electrocorticography (ECoG) grids, are used to record brain activity in applications like brain computer interfaces. To improve the spatial sensitivity of ECoG grid recordings, electrode properties need to be better understood. Therefore, the goal of this study is to analyze the importance of including electrodes explicitly in volume conduction calculations

Objective. Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient

Objective. Automatic sleep staging models suffer from an inherent class imbalance problem (CIP), which hinders the classifiers from achieving a better performance. To address this issue, we systematically studied sleep electroencephalogram data augmentation (DA) approaches. Furthermore, we modified and transferred novel DA approaches from related research fields, yielding new efficient ways to enhance

Objective. A passive brain-computer interface (pBCI) is a system that continuously adapts human-computer interaction to the user’s state. Key to the efficacy of such a system is the reliable estimation of the user’s state via neural signals, acquired through non-invasive methods like electroencephalography (EEG) or near-infrared spectroscopy (fNIRS). Many studies to date have explored the detection

Infrared neuromodulation (INM) is a branch of photobiomodulation that offers direct or indirect control of cellular activity through elevation of temperature in a spatially confined region of the target tissue. Research on INM started about 15 ago and is gradually attracting the attention of the neuroscience community, as numerous experimental studies have provided firm evidence on the safe and reproducible

Objective. This work described a video information processing scheme for optogenetic forms of visual cortical prosthetics. Approach. The architecture is designed to perform a processing sequence: Initially simplifying the scene, followed by a pragmatic visual encoding scheme which assumes that initially optical stimulation will be stimulating bulk neural tissue rather than driving individual phosphenes

Objective . Multimodal measurements at the neuronal level allow for detailed insight into local circuit function. However, most behavioral studies focus on one or two modalities and are generally limited by the available technology. Approach . Here, we show a combined approach of electrophysiology recordings, chemical sensing, and histological localization of the electrode tips within tissue. The key

Objective . Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET) and several other movement disorders. One approach to improving DBS therapy is adaptive DBS (aDBS), in which stimulation parameters are modulated in real time based on biofeedback from either external or implanted sensors. Previously tested systems have fallen short of translational applicability

Objective. A current challenge of neurotechnologies is to develop speech brain-computer interfaces aiming at restoring communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features

Objective . Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms

Objective. When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact

Objective. While the positive correlation between impedance and noise of microelectrodes is well known, their quantitative relationship is too rarely described. Knowledge of this relationship provides useful information for both microsystems engineers and electrophysiologists. Approach. We discuss the physical basis of noise in recordings with microelectrodes, and compare measurements of impedance

Objective. In this paper, a novel methodology for feature extraction to enhance classification accuracy of functional near-infrared spectroscopy (fNIRS)-based two-class and three-class brain–computer interface (BCI) is presented. Approach. Novel features are extracted using vector-based phase analysis method. Changes in oxygenated ##IMG## [http://ej.iop.org/images/1741-2552/17/5/056025/jneabb417ieqn1

Objective. We present the design, implementation, and evaluation of a wearable multichannel haptic system. The device is a wireless closed-loop armband driven by surface electromyography (EMG) and provides sensory feedback encoding proprioception. The study is motivated by restoring proprioception information in upper limb prostheses. Approach. The armband comprises eight vibrotactile actuators that

Objective. The P300 speller is a classic brain–computer interface (BCI) paradigm that has the potential to restore impaired motor control function. However, previous studies have confirmed that the letter recognition accuracy (LRA) of the P300 speller is a challenge when performing other tasks. Approach. To address this, we implemented a dynamic stopping strategy (DSS) to maintain the P300 speller

Objective. Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive

Objective. Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically

Objective . Ultrasounds (US) use in neural engineering is so far mainly limited to ablation through high intensity focused ultrasound, but interesting preliminary results show that low intensity low frequency ultrasound could be used instead to modulate neural activity. However, the extent of this modulatory ability of US is still unclear, as in in vivo studies it is hard to disentangle the contribution

Objective. An important part of restoring motor control via a brain-computer interface is to close the sensorimotor feedback loop. As part of our investigations into vibrotactile kinaesthetic feedback of arm movements, we studied electroencephalographic signals in the δ , µ and β bands obtained during a center-out movement task with four conditions: movement with real-time kinaesthetic feedback, movement

Objective. The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. Approach. Our proposed

Objective. Transcranial Direct Current Stimulation (tDCS) is a technique where a weak current is passed through the electrodes placed on the scalp. The distribution of the electric current induced in the brain due to tDCS is provided by simulation toolbox like Realistic volumetric Approach based Simulator for Transcranial electric stimulation (ROAST). However, the procedure to estimate the total current

Objective. Conventional neural signal analysis methods assume that features of interest are linear, time-invariant signals confined to well-delineated spectral bands. However, new evidence suggests that neural signals exhibit important non-stationary characteristics with ill-defined spectral distributions. These features pose a need for signal processing algorithms that can characterize temporal and

Objective . Iridium oxide films are commonly used as a high charge-injection electrode material in neural devices. Yet, few studies have performed in-depth assessments of material performance versus film thickness, especially for films grown on three-dimensional (instead of planar) metal surfaces in neutral pH electrolyte solutions. Further, few studies have investigated the driving voltage requirements

Objective. Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use

Objective. Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal of this work is to frame these as a unified challenge and reconsider how transfer learning is used to overcome these difficulties. Approach . We present

Objective. Visual perception decoding plays an important role in understanding our visual systems. Recent functional magnetic resonance imaging (fMRI) studies have made great advances in predicting the visual content of the single stimulus from the evoked response. In this work, we proposed a novel framework to extend previous works by simultaneously decoding the temporal and category information of