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Objective . A number of tissue penetrating opto-electrodes to simultaneously record and optogenetically influence brain activity have been developed. For experiments at the surface of the brain, such as electrocorticogram (ECoG) recordings and surface optogenetics, fewer devices have been described and no device has found widespread adoption for neuroscientific experiments. One issue slowing adoption

The vast majority of techniques to study the physiology of the nervous system involve inserting probes into the brain for stimulation, recording, or sampling. Research is increasingly uncovering the fine microstructure of the brain, each of its regions with dedicated functions. Accurate knowledge of the placement of probes interrogating these regions is critical. We have developed a customizable concentric

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Objective. Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific

Objective. Developing a new neuromodulation method for epilepsy treatment requires a large amount of time and resources to find effective stimulation parameters and often fails due to inter-subject variability in stimulation effect. As an alternative, we present a novel data-driven surrogate approach which can optimize the neuromodulation efficiently by investigating the stimulation effect on surrogate

Objective . In this study, we demonstrate practical applications of a novel 3-dimensional neural probe for simultaneous electrophysiological recordings from the surface of the brain as well as deep intra-cortical tissue. We used this 3D probe to investigate signal propagation mechanisms between neuronal cells and their responses to stimuli in a 3D fashion. Approach . This novel probe leverage 2D thin-film

Objective. fMRI-based neurofeedback (NF) interventions represent the method of choice for the neuromodulation of localized brain areas. Although we have already validated an fMRI-NF protocol targeting the facial expressions processing network (FEPN), its dissemination is hampered by the economical and logistical constraints of fMRI-NF interventions, which may be however surpassed by transferring it

Objective. For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability. Approach. In this paper, a brain state evaluation network, termed BSE-NET

Objective . The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study

Objective . Low-intensity transcranial ultrasound stimulation (TUS) is emerging as a non-invasive brain stimulation technique with superior spatial resolution and the ability to reach deep brain areas. Medical image-based computational modeling could be an important tool for individualized TUS dose control and targeting optimization, but requires further validation. This study aims to assess the impact

Objective. Retinal prosthesis recipients require rehabilitative training to learn the non-intuitive nature of prosthetic ‘phosphene vision’. This study investigated whether the addition of auditory cues, using The vOICe sensory substitution device (SSD), could improve functional performance with simulated phosphene vision. Approach. Forty normally sighted subjects completed two visual tasks under three

Objective. Body-Machine Interfaces (BoMIs) establish a way to operate a variety of devices, allowing their users to extend the limits of their motor abilities by exploiting the redundancy of muscles and motions that remain available after spinal cord injury or stroke. Here, we considered the integration of two types of signals, motion signals derived from inertial measurement units (IMUs) and muscle

Objective. Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain’s functional organization. Understanding links between structural and functional

Objective. Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. The aim of this work is to propose and validate a deep learning model to decode gait phases from Electroenchephalography (EEG). Approach. A Long-Short

Objective. Real-time approaches for transcranial magnetic stimulation (TMS) based on a specific EEG phase are a promising avenue for more precise neuromodulation interventions. However, optimal approaches to reliably extract the EEG phase in a frequency band of interest to inform TMS are still to be identified. Here, we implement a new real-time phase detection method for closed-loop EEG-TMS for robust

Objective. The vagus nerve has been implicated in a variety of immune responses, and the number of studies using mouse models to unravel key mechanisms has increased. However, as of yet, there is no electrode that can chronically record neural activity from the mouse vagus nerve due to its small diameter. Such recordings are critical to understand the role of these biomarkers for translational research

Objective . Neural interfacing technologies could significantly improve quality of life for people living with the loss of a limb. Both motor commands and sensory feedback must be considered; these complementary systems are segregated from one another in the spinal nerve. Approach . The dorsal root ganglion-ventral root (DRG-VR) complex was targeted chronically with floating microelectrode arrays designed

Objective. Micro-fabricated neural interfaces based on polyimide (PI) are achieving increasing importance in translational research. The ability to produce well-defined micro-structures with properties that include chemical inertness, mechanical flexibility and low water uptake are key advantages for these devices. Approach. This paper reports the development of the transverse intrafascicular multichannel

Objective . To infer information flow in the white matter of the brain and recover cortical activity using functional MRI, diffusion MRI, and MEG without a manual selection of the white matter connections of interest. Approach . A Bayesian network which encodes the priors knowledge of possible brain states is built from imaging data. Diffusion MRI is used to enumerate all possible connections between

Objective. Cortical vision prostheses aim to restore visual percepts to those who have lost sight by delivering electrical stimulation to the visual cortex. These devices need to be implanted intracranially using subdural or intracortical microelectrodes, and should preferably dispense with the need of transcranial wiring. The risks of cortical tissue injury from mechanical trauma, material biocompatibility

Objective. A fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC). Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC’s contribution

Objective. Several training programs have been developed in the past to restore motor functions after stroke. Their efficacy strongly relies on the possibility to assess individual levels of impairment and recovery rate. However, commonly used clinical scales rely mainly on subjective functional assessments and are not able to provide a complete description of patients’ neuro-biomechanical status.

Objective . This paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulator’s ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency

Objective . Brain-computer interfaces that activate exoskeletons based on decoded movement-related activity have been shown to be useful for stroke rehabilitation. With the advances in the development of exoskeletons it is possible to replicate a number of different functional movements that are relevant to rehabilitate after stroke. In this study, the aim is to detect and classify six different movement

Objective . The auditory brainstem response can be recorded non-invasively from scalp electrodes and serves as an important clinical measure of hearing function. We have recently shown how the brainstem response at the fundamental frequency of continuous, non-repetitive speech can be measured, and have used this measure to demonstrate that the response is modulated by selective attention. However,

Objective. We aimed at characterizing, in non-invasive human brain recordings, the large-scale, coordinated activation of distant brain regions thought to occur during conscious perception. This process is termed ignition in the Global Workspace Theory, and integration in Integrated Information Theory, which are two of the major theories of consciousness. Approach. Here, we provide evidence for this

Objective. The development of electrode arrays able to reliably record brain electrical activity is a critical issue in brain machine interface (BMI) technology. In the present study we undertook a comprehensive physico-chemical, physiological, histological and immunohistochemical characterization of new single-walled carbon nanotubes (SWCNT)-based electrode arrays grafted onto medium-density polyethylene

Objective . In this paper, we report on the development of an easy-to-fabricate three-dimensional Micro-Electrode Array (3D-MEA) specifically designed for brain-on-a-dish applications. Approach . The proposed device consists of pillar-shaped gold microelectrodes realized by electroplating directly on top of a standard MEA, making this approach highly versatile and convenient for batch fabrication.

Objective . Retinal prostheses aim to restore vision in patients with retinal degenerative diseases, such as age-related macular degeneration and retinitis pigmentosa. By implanting an array of microelectrodes, such a device creates percepts in patients through electrical stimulation of surviving retinal neurons. A challenge for retinal prostheses when trying to return high quality vision is the unintended

Objective . Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two non-invasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural

Objective. Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one’s internal mental state still lacks a notion of the third dimension of time

Objective. A passive brain-computer interface recognizes its operator’s cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded

Objective . Orientation selectivity is one of the significant characteristics of neurons in the primary visual cortex (V1). Some neurons in extrastriate visual cortical areas also exhibit certain orientation selectivity. But it is still not well understood that how the orientation selectivity generates. Most previous studies about the orientation selectivity are based on the spike firing rate. However

Objective. Recording electrical activity from individual cells in vivo is a key technology for basic neuroscience and has growing clinical applications. To maximize the number of independent recording channels as well as the longevity, and quality of these recordings, researchers often turn to small and flexible electrodes that minimize tissue damage and can isolate signals from individual neurons

Objective . Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates. Approach . The classification performance was evaluated on quiet sleep (QS) and active sleep

Objective . Statistical methods that simultaneously model temporal and spatial variations of fMRI data are promising tools for dynamic functional connectivity (FC) estimation. Although different approaches are available, they need to manually set the parameters, or may disregard some important fMRI features such as the autocorrelation. In addition, no reliable method exists for the validation of dynamic

Objective. Event Related Potentials (ERPs) reflecting cognitive response to external stimuli, are widely used in brain computer interfaces. ERP waveforms are characterized by a series of components of particular latency and amplitude. The classical ERP decoding methods exploit this waveform characteristic and thus achieve a high performance only if there is sufficient time- and phase-locking across

ObjectiveConnectivity networks are crucial to understand the brain resting-state activity using functional magnetic resonance imaging (rs-fMRI). Alterations of these brain networks may highlight important findings concerning the resilience of the brain to different disorders. The focus of this paper is to evaluate the robustness of brain network estimations, discriminate them under anesthesia and compare

Objective. In this report we present the fEEGrid, an electrode array applied to the forehead that allows convenient long-term recordings of electroencephalography (EEG) signals over many hours. Approach. Twenty young, healthy participants wore the fEEGrid and completed traditional EEG paradigms in two sessions on the same day. The sessions were eight hours apart, participants performed the same tasks

Objective. Interferential stimulation (IFS) has generated considerable interest recently because of its potential to achieve focal electric fields in deep brain areas with transcranial currents. Conventionally, IFS applies sinusoidal currents through two electrode pairs with close-by frequencies. Here we propose to use an array of electrodes instead of just two electrode pairs; and to use algorithmic

Objective. Event-related potentials (ERPs) evoked by visual stimulations comprise several components, with different amplitudes and latencies. Among them, the N2 and N2pc components have been demonstrated to be a measure of subjects’ allocation of visual attention to possible targets and to be involved in the suppression of irrelevant items. Unfortunately, the N2 and N2pc components have smaller amplitudes

Objective . Brain-computer interface (BCI) has demonstrated its effectiveness in epilepsy treatment and control. In a BCI-aided epilepsy treatment system, therapic electrical stimulus is delivered in response to the prediction of upcoming seizure onsets, therefore timely and accurate seizure prediction algorithm plays an important role. However, unlike typical signatures such as slow or sharp waves

Objective . Intracortical electrical neural recording using solid-state electrodes is a prevalent approach in addressing neurophysiological queries and implementing brain-computer interfacing systems. As a variety of ultra-density microelectrode arrays (ultra-density MEAs) are being created more recently, this paper answers to the rising demand for a more rigorous theory concerning this new type of

Objective. To evaluate the electrochemical properties, biological response, and surface characterization of an electrodeposited Platinum-Iridium (Pt-Ir) electrode coating on cochlear implants subjected to chronic stimulation in vivo. Approach . Electrochemical impedance spectroscopy (EIS), charge storage capacity (CSC), charge injection limit (CIL), and voltage transient (VT) impedance were measured

Objective. The performance of brain-computer interface (BCI) systems is influenced by the user’s mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users’ attention during real-time movement execution. Approach. Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral

Objective . Brain Computer Interface (BCI) inefficiency indicates that there would be 10% to 50% of users are unable to operate Motor-Imagery-based BCI systems. Importantly, the almost all previous studieds on BCI inefficiency were based on tests of Sensory Motor Rhythm (SMR) feature. In this work, we assessed the occurrence of BCI inefficiency with SMR and Movement-Related Cortical Potential (MRCP)

Objective. Vagus nerve stimulation (VNS) is a nonpharmacologic therapeutic option for patients who have pharmaco-resistant Dravet syndrome (DS). Plentiful efforts have been made for delivering VNS to DS patients, but its effectiveness still requires further verification. We investigated the effectiveness of the VNS treatment of DS patients using brain connectivity analysis with electroencephalography

Objective. The appearance of commercial myoelectric armbands has greatly increased the portability and convenience of myoelectric controlled interfaces (MCIs). However, one limitation of the current state-of-the-art myoelectric control algorithms is that they have poor robustness against armband displacements, especially rotation, leading to great algorithmic performance degradation. The traditional

Objective. Focal epilepsy is a disorder affecting several brain networks; however, epilepsy surgery usually targets a restricted region, the so-called epileptic focus. There is a growing interest in embedding resting state (RS) connectivity analysis into pre-surgical workup. Approach . In this retrospective study, we analyzed Magnetoencephalography (MEG) long-range RS functional connectivity patterns

Objective. To explore the impact of different electrical stimulation profiles in human recipients of the Geneva-Maastricht vestibular implant prototypes. Approach. Four implanted patients were recruited for this study. We investigated the relative efficacy of systematic variations of the electrical stimulus profile (phase duration, pulse rate, baseline level, modulation depth) in evoking vestibulo-ocular

Objective. Over the recent years, several small area electrodes have been introduced as tools for preferential stimulation of small cutaneous nerve fibers. However, the performance of the electrodes is highly debated and have not previously been systematically compared. The electrodes have been developed empirically and little is known about the electrical potential they produce in the skin, and how

Objective. Characterize the role of the beta-band (13–30 Hz) in the human hippocampus during the execution of voluntary movement. Approach. We recorded electrophysiological activity in human hippocampus during a reach task using stereotactic electroencephalography (SEEG). SEEG has previously been utilized to study the theta band (3–8 Hz) in conflict processing and spatial navigation, but most studies

Objective. A deep convolutional neural network (CNN) is a method for deep learning (DL). It has a powerful ability to automatically extract features and is widely used in classification tasks with scalp electroencephalogram (EEG) signals. However, the small number of samples and low signal-to-noise ratio involved in scalp EEG with low spatial resolution constitute a limitation that might restrict potential

Objective . Spinal cord stimulation (SCS) is a common neurostimulation therapy to treat chronic pain. Computational models represent a valuable tool to study the potential mechanisms of action of SCS and to optimize the design and implementation of SCS technologies. However, it is imperative that these computational models include the appropriate level of detail to accurately predict the neural response