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

Objective . To design a computationally efficient model for ultrasonic neuromodulation (UNMOD) of morphologically realistic multi-compartmental neurons based on intramembrane cavitation. Approach . A Spatially Extended Neuronal Intramembrane Cavitation model that accurately predicts observed fast Charge Oscillations (SECONIC) is designed. A regular spiking cortical Hodgkin-Huxley type nanoscale neuron

Objective. Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. Approach. An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus

Objective. Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model. Approach. We performed numerical simulations to investigate source localization errors caused

Objective. One of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches

Objective. High frequency oscillations (HFOs) are a promising biomarker of tissue that instigates seizures. However, ambiguous data and random background fluctuations can cause any HFO detector (human or automated) to falsely label non-HFO data as an HFO (a false positive detection). The objective of this paper was to identify quantitative features of HFOs that distinguish between true and false positive

Objective. Chronically-implanted neural microelectrodes are powerful tools for neuroscience research and emerging clinical applications, but their usefulness is limited by their tendency to fail after months in vivo . One failure mode is the degradation of insulation materials that protect the conductive traces from the saline environment. Approach. Studies have shown that material degradation is accelerated

Objective. Visual prostheses are designed to restore partial functional vision in patients with total vision loss. Retinal visual prostheses provide limited capabilities as a result of low resolution, limited field of view and poor dynamic range. Understanding the influence of these parameters in the perception results can guide prostheses research and design. Approach. In this work, we evaluate the

By recording neural activity directly from the human brain, researchers gain unprecedented insight into how neurocognitive processes unfold in real time. We first briefly discuss how intracranial electroencephalography (iEEG) recordings, performed for clinical practice, are used to study human cognition with the spatiotemporal and single-trial precision traditionally limited to non-human animal research

Objective. Cochleae of long-term cochlear implant users have shown evidence of particulate platinum (Pt) corroded from the surface of Pt electrodes. The pathophysiological effect of Pt within the cochlea has not been extensively investigated. We previously evaluated the effects of Pt corrosion at high charge densities and reported negligible pathophysiological impact. The present study extends this

Objective. High-fidelity vibrokinetic (HFVK) technology is widely used to enhance the immersiveness of audiovisual (AV) entertainment experiences. However, despite evidence that HFVK technology does subjectively enhance AV immersion, the underlying mechanism has not been clarified. Neurophysiological studies could provide important evidence to illuminate this mechanism, thereby benefiting HFVK stimulus

Objective . Touch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g. due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge

Objective . We aim to increase the number of neural spikes that can be detected in a single channel extracellular neural recording. Approach . We propose a pre-emphasis method facilitating stochastic resonance (SR), where we introduce the band-pass-filtered noisy extracellular recording to an overdamped Brownian particle in a monostable well. The x-position of the Brownian particle is the output of

Objective . There is growing interest in delivering kilohertz frequency (KHF) electrical signals to block conduction in peripheral nerves for treatment of various diseases. Previous studies used different KHF waveforms to achieve block, and it remains unclear how waveform affects nerve block parameters. Approach. We quantified the effects of waveform on KHF block of the rat tibial nerve in vivo and

Objective. Report simple reference structure fabrication and validate the precise localization of subdural micro- and standard electrodes in magnetic resonance imaging (MRI) in phantom experiments. Approach . Electrode contacts with diameters of 0.3 mm and 4 mm are localized in 1.5 T MRI using reference structures made of silicone and iron oxide nanoparticle doping. The precision of the localization

Age-related macular degeneration is an incurable chronic neurodegenerative disease, which causes progressive loss of the central vision and even blindness. All current treatments aim at slowing down the progression of the disease. In the present paper we provide in vitro evidence for the feasibility of layered biohybrid retinas built with silk silkworm fibroin and cultures of different types of cells:

OBJECTIVE The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework

Implantable neuroelectronic interfaces have enabled breakthrough advances in the clinical diagnosis and treatment of neurological disorders, as well as in fundamental studies of brain function, behavior, and disease. Intracranial electroencephalography (EEG) mapping with stereo-EEG (sEEG) depth electrodes is routinely adopted for precise epilepsy diagnostics and surgical treatment, while deep brain

Objective . Hand movement is a crucial function for humans’ daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while

After decades of study in humans and animal models, there remains a lack of consensus regarding how the action of electrical stimulation on neuronal and non-neuronal elements - e.g. neuropil, cell bodies, glial cells, etc. - leads to the therapeutic effects of neuromodulation therapies. To further our understanding of neuromodulation therapies, there is a critical need for novel methodological approaches

Objective. Identifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson’s disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation

Objective . Retinal prostheses strive to restore vison to patients that are blind from retinal degeneration by electrically stimulating surviving retinal ganglion cells (RGCs). The quality of elicited percepts remains limited however and it is desirable to develop improved stimulation strategies. Here, we examine how the anatomical and biophysical properties of RGCs influence activation thresholds

Objective. Gel aspiration-ejection (GAE) has recently been developed for the rapid production of dense, anisotropic collagen gel scaffolds with adjustable collagen fibrillar densities. In this study, a GAE system was applied to produce aligned Schwann cells within a type-1 collagen matrix to generate GAE-engineered neural tissues (GAE-EngNT) for potential nerve tissue engineering applications. Approach

Objective . It is known that multi-site interleaved stimulation generates less muscle fatigue compared to single-site synchronous stimulation. However, in the limited number of studies in which intramuscular electrodes were used, the fatigue reduction associated with interleaved stimulation could not consistently be achieved. We hypothesize that this could be due to the inability to place the intramuscular

Objective. Degradations of transmitted speech have been shown to affect perceptual and cognitive processing in human listeners, as indicated by the P3 component of the event-related brain potential (ERP). However, research suggests that previously observed P3 modulations might actually be traced back to earlier neural modulations in the time range of the P1-N1-P2 complex of the cortical auditory evoked

Objective . A hearing aid’s noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on e

Objective . Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other

Objective . Recent studies suggest that the use of noninvasive closed-loop neuromodulation combining electroencephalography (EEG) and transcranial alternating current stimulation (tACS) may be a promising avenue for the treatment of neurological disorders. However, the attenuation of tACS artifacts in EEG data is particularly challenging, and computationally efficient methods are needed to enable closed-loop

Stroke is one of the leading causes of long-term disability among adults and contributes to major socio-economic burden globally. Stroke frequently results in multifaceted impairments including motor, cognitive and emotion deficits. In recent years, brain–computer interface (BCI)-based therapy has shown promising results for post-stroke motor rehabilitation. In spite of the success received by BCI-based

Objective . The hand—a complex effector comprising dozens of degrees of freedom of movement—endows us with the ability to flexibly, precisely, and effortlessly interact with objects. The neural signals associated with dexterous hand movements in primary motor cortex (M1) and somatosensory cortex (SC) have received comparatively less attention than have those associated with proximal upper limb control

Objective. Neural signals can be decoded and used to move neural prostheses with the purpose of restoring motor function in patients with mobility impairments. Such patients typically have intact eye movement control and visual function, suggesting that cortical visuospatial signals could be used to guide external devices. Neurons in parietal cortex mediate sensory-motor transformations, encode the

Objective . We used EEG to investigate cortical oscillatory activities during the tactile discrimination task and characterize the correlation between the EEG features and subjects’ discrimination performance. Approach . Transcutaneous electrical nerve stimulation (TENS) was applied on two finger areas (thumb and index for healthy hands, thumb and index-projected areas for disabled hands) to evoke

Objective. Artificial proprioceptive feedback from a myoelectric prosthesis is an important aspect in enhancing embodiment and user satisfaction, possibly lowering the demand for visual attention while controlling a prosthesis in everyday tasks. Contemporary myoelectric prostheses are advanced mechatronic systems with multiple degrees of freedom, and therefore, to communicate the prosthesis state,

Objective. Concurrent changes in physiological signals across multiple listeners (physiological synchrony—PS), as caused by shared affective or cognitive processes, may be a suitable marker of selective attentional focus. We aimed to identify the selective attention of participants based on PS with individuals sharing attention with respect to different stimulus aspects. Approach. We determined PS