Strength-frequency curve for micromagnetic neurostimulation through excitatory postsynaptic potentials (EPSPs) on rat hippocampal neurons and numerical modeling of magnetic microcoil (μcoil)

Objective: The objective of this study was to measure the effect of micromagnetic stimulation (μMS) on hippocampal neurons, by using single microcoil (μcoil) prototype, Magnetic Pen (MagPen). MagPen will be used to stimulate the CA3 magnetically and excitatory post synaptic potential (EPSP) measurements will be made from the CA1. The threshold for μMS as a function of stimulation frequency of the current driving the µcoil will be demonstrated. Finally, the optimal stimulation frequency of the current driving the μcoil to minimize power will be estimated. Approach: A biocompatible prototype, MagPen was built, and customized such that it is easy to adjust the orientation of the μcoil over the hippocampal tissue in an in vitro setting. Finite element modeling (FEM) of the μcoil was performed to estimate the spatial profiles of the magnetic flux density (in T) and the induced electric fields (in V/m). The induced electric field profiles generated at different values of current applied to the µcoil whether can elicit a neuron response was validated by numerical modeling. The modeling settings were replicated in experiments on rat hippocampal neurons. Main results: The preferred orientation of MagPen over the Schaffer Collateral fibers was demonstrated such that they elicit a neuron response. The recorded EPSPs from CA1 due to μMS at CA3 were validated by applying tetrodotoxin (TTX). Finally, it was interpreted through numerical analysis that increasing frequency of the current driving the μcoil, led to a decrease in the current amplitude threshold for μMS. Significance: This work reports that μMS can be used to evoke population EPSPs in the CA1 of hippocampus. It demonstrates the strength-frequency curve for µMS and its unique features related to orientation dependence of the µcoils, spatial selectivity and distance dependence. Finally, the challenges related to µMS experiments were studied including ways to overcome them.

Visual field inhomogeneous in brain-computer interfaces based on rapid serial visual presentation

Objective. Visual attention is not homogeneous across the visual field, while how to mine the effective EEG characteristics that are sensitive to the inhomogeneous of visual attention and further explore applications such as the performance of brain-computer interface (BCI) are still distressing explorative scientists. Approach. Images were encoded into a rapid serial visual presentation (RSVP) paradigm, and were presented in three visuospatial patterns (central, left/right, upper/lower) at the stimulation frequencies of 10Hz, 15Hz and 20Hz. The comparisons among different visual fields were conducted in the dimensions of subjective behavioral and EEG characteristics. Furthermore, the effective features (e.g. SSVEP, N2pc and P300) that sensitive to visual-field asymmetry were also explored. Results. The visual fields had significant influences on the performance of RSVP target detection, in which the performance of central was better than that of peripheral visual field, the performance of horizontal meridian was better than that of vertical meridian, the performance of left visual field was better than that of right visual field, and the performance of upper visual field was better than that of lower visual field. Furthermore, stimuli of different visual fields had significant effects on the spatial distributions of EEG, in which N2pc and P300 showed left-right asymmetry in occipital and frontal regions, respectively. In addition, the evidences of SSVEP characteristics indicated that there was obvious overlap of visual fields on the horizontal meridian, but not on the vertical meridian. Significance. The conclusions of this study provide insights into the relationship between visual field inhomogeneous and EEG characteristics. In addition, this study has the potential to achieve precise positioning of the target's spatial orientation in RSVP-BCIs.

EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification

Objective. Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI). Approach. 14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm. Main results. ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01). Significance. This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

Low-cost and easy-fabrication lightweight drivable electrode array for multiple-regions electrophysiological recording in free-moving mice

Objective. Extracellular electrophysiology has been widely applied to neural circuit dissections. However, long-term multiregional recording in free-moving mice remains a challenge. Low-cost and easy-fabrication of elaborate drivable electrodes is required for their prevalence. Approach. A three-layer nested construct (OD ~1.80 mm, length ~10 mm, <0.1g) was recruited as a drivable component, which consisted of an ethylene-vinyl acetate copolymer (EVA) heat-shrinkable tube, non-closed loop ceramic bushing, and stainless ferrule with a bulge twining silver wire. The supporting and working components were equipped with drivable components to be assembled into a drivable microwire electrode array with a nested structure (drivable MEANS). Two drivable microwire electrode arrays were independently implanted for chronic recording in different brain areas at respective angles. An optic fiber was easily loaded into the drivable MEANS to achieve optogenetic modulation and electrophysiological recording simultaneously. Main results. The drivable MEANS had lightweight (~ 0.37 g), small (~ 15 mm ×15 mm × 4 mm), and low cost (≤ $64.62). Two drivable MEANS were simultaneously implanted in mice, and high-quality electrophysiological recordings could be applied ≥ 5 months after implantation in freely behaving animals. Electrophysiological recordings and analysis of the lateral septum (LS) and lateral hypothalamus (LH) in food-seeking behavior demonstrated that our drivable MEANS can be used to dissect the function of neural circuits. An optical fiber-integrated drivable MEANS (~ 0.47 g) was used to stimulate and record LS neurons, which suggested that changes in working components can achieve more functions than electrophysiological recordings, such as optical stimulation, drug release, and calcium imaging. Significance. Drivable MEANS is an easily fabricated, lightweight drivable microwire electrode array for multiple-region electrophysiological recording in free-moving mice. Our design is likely to be a valuable platform for both current and prospective users, as well as for developers of multifunctional electrodes for free-moving mice.

A high-performance brain switch based on code-modulated visual evoked potentials

Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

Novel surface electrode design for preferential activation of cutaneous nociceptors

Objective Small area electrodes enable preferential activation of nociceptive fibers. It is debated, however, whether co-activation of large fibers still occurs for the existing electrode designs. Moreover, existing electrodes are limited to low stimulation intensities, for which behavioral and physiological responses may be considered less reliable. A recent optimization study showed that there is a potential for improving electrode performance and increase the range of possible stimulation intensities. Based on those results, the present study introduces and tests a novel planar concentric array electrode design for small fiber activation in healthy volunteers. Approach Volunteers received electrical stimulation with the planar concentric array electrode and a regular patch electrode. Perception thresholds were estimated at the beginning and the end of the experiment. Evoked cortical potentials were recorded in blocks of 30 stimuli. For the patch, stimulation intensity was set to two times perception threshold (PT), while three intensities, 2, 5, and 10 times PT, were applied with the planar concentric array electrode. Sensation quality, numerical-rating scores, and reaction times were obtained for each PT estimation and during each block of evoked potential recordings. Main results Stimulation with the patch electrode was characterized as dull, while stimulation with the planar concentric array electrode was characterized as sharp, with increased sharpness for increasing stimulus intensity. Likewise, NRS scores were higher for the planar concentric array electrode compared to the patch and increased with increasing stimulation intensity. Reaction times and ERP latencies were longer for the planar concentric array electrode compared to the patch. Significance The presented novel planar concentric array electrode is a small, non-invasive, and single-use electrode that has the potential to investigate small fiber neuropathy and pain mechanisms, as it is small fiber preferential for a wide range of stimulation intensities.

A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. Approach. Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. Main results. The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. Significance. The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

FB-CGANet: filter bank Channel Group Attention network for multi-class motor imagery classification

Objective. Motor imagery-based brain computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography (EEG) signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method Channel Group Attention (CGA) to build a lightweight neural network Filter Bank Channel Group Attention Network (FB-CGANet). Accompanied with FB-CGANet, the Band Exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.

Electrochemical microelectrode degradation monitoring: in situ investigation of platinum corrosion at neutral pH

Objective. The stability of platinum and other noble metal electrodes is critical for neural implants, electrochemical sensors, and energy sources. Beyond the acidic or alkaline environment found in most electrochemical studies, the investigation of electrode corrosion in neutral pH and chloride containing electrolytes is essential, particularly regarding the long-term stability of neural interfaces, such as brain stimulation electrodes or cochlear implants. In addition, the increased use of microfabricated devices demands the investigation of thin-film electrode stability. Approach. We developed a procedure of electrochemical methods for continuous tracking of electrode degradation in situ over the complete life cycle of platinum thin-film microelectrodes in a unique combination with simultaneous chemical sensing. We used chronoamperometry and cyclic voltammetry to measure electrode surface and analyte redox processes, together with accelerated electrochemical degradation. Main results. We compared degradation between thin-film microelectrodes and bulk electrodes, neutral to acidic pH, different pulsing schemes, and the presence of the redox active species oxygen and hydrogen peroxide. Results were confirmed by mechanical profilometry and microscopy to determine material changes on a nanometer scale. We found that electrode degradation is mainly driven by repeated formation and removal of the platinum surface oxide, also within the electrochemical stability window of water. There was no considerable difference between thin-film micro- and macroscopic bulk electrodes or in the presence of reactive species, whereas acidic pH or extending the potential window led to increased degradation. Significance. Our results provide valuable fundamental information on platinum microelectrode degradation under conditions found in biomedical applications. For the first time, we deployed a unified method to report quantitative data on electrode degradation up to a defined endpoint. Our method is a widely applicable framework for comparative long-term studies of sensor and neural interface stability.

Simultaneous control of natural and extra degrees of freedom by isometric force and electromyographic activity in the muscle-to-force null space

Objective. Muscle activation patterns in the muscle-to-force null space, i.e., patterns that do not generate task-relevant forces, may provide an opportunity for motor augmentation by allowing to control additional end-effectors simultaneously to natural limbs. Here we tested the feasibility of muscular null space control for augmentation by assessing simultaneous control of natural and extra degrees of freedom. Approach. We instructed eight participants to control translation and rotation of a virtual 3D end-effector by simultaneous generation of isometric force at the hand and null space activity extracted in real-time from the electromyographic signals recorded from 15 shoulder and arm muscles. First, we identified the null space components that each participant could control more naturally by voluntary co-contraction. Then, participants performed several blocks of a reaching and holding task. They displaced an ellipsoidal cursor to reach one of nine targets by generating force, and simultaneously rotated the cursor to match the target orientation by activating null space components. We developed an information-theoretic metric, an index of difficulty defined as the sum of a spatial and a temporal term, to assess individual null space control ability for both reaching and holding. Main Results. On average, participants could reach the targets in most trials already in the first block (72%) and they improved with practice (maximum 93%) but holding performance remained lower (maximum 43%). As there was a high inter-individual variability in performance, we performed a simulation with different spatial and temporal task conditions to estimate those for which each individual participants would have performed best. Significance. Muscular null space control is feasible and may be used to control additional virtual or robotics end-effectors. However, decoding of motor commands must be optimized according to individual null space control ability.