Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.     

Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.     

Coarse Electrocorticographic Decoding of Ipsilateral Reach in Patients with Brain Lesions

In patients with unilateral upper limb paralysis from strokes and other brain lesions, strategies for functional recovery may eventually include brain-machine interfaces (BMIs) using control signals from residual sensorimotor systems in the damaged hemisphere. When voluntary movements of the contralateral limb are not possible due to brain pathology, initial training of such a BMI may require use of the unaffected ipsilateral limb. We conducted an offline investigation of the feasibility of decoding ipsilateral upper limb movements from electrocorticographic (ECoG) recordings in three patients with different lesions of sensorimotor systems associated with upper limb control. We found that the first principal component (PC) of unconstrained, naturalistic reaching movements of the upper limb could be decoded from ipsilateral ECoG using a linear model. ECoG signal features yielding the best decoding accuracy were different across subjects. Performance saturated with very few input features. Decoding performances of 0.77, 0.73, and 0.66 (median Pearson''s r between the predicted and actual first PC of movement using nine signal features) were achieved in the three subjects. The performance achieved here with small numbers of electrodes and computationally simple decoding algorithms suggests that it may be possible to control a BMI using ECoG recorded from damaged sensorimotor brain systems.     

Development and Application of Stereo Camera-Based Upper Extremity Workspace Evaluation in Patients with Neuromuscular Diseases

Background

The concept of reachable workspace is closely tied to upper limb joint range of motion and functional capability. Currently, no practical and cost-effective methods are available in clinical and research settings to provide arm-function evaluation using an individual’s three-dimensional (3D) reachable workspace. A method to intuitively display and effectively analyze reachable workspace would not only complement traditional upper limb functional assessments, but also provide an innovative approach to quantify and monitor upper limb function.

Methodology/Principal Findings

A simple stereo camera-based reachable workspace acquisition system combined with customized 3D workspace analysis algorithm was developed and compared against a sub-millimeter motion capture system. The stereo camera-based system was robust, with minimal loss of data points, and with the average hand trajectory error of about 40 mm, which resulted to ∼5% error of the total arm distance. As a proof-of-concept, a pilot study was undertaken with healthy individuals (n = 20) and a select group of patients with various neuromuscular diseases and varying degrees of shoulder girdle weakness (n = 9). The workspace envelope surface areas generated from the 3D hand trajectory captured by the stereo camera were compared. Normalization of acquired reachable workspace surface areas to the surface area of the unit hemi-sphere allowed comparison between subjects. The healthy group’s relative surface areas were 0.618±0.09 and 0.552±0.092 (right and left), while the surface areas for the individuals with neuromuscular diseases ranged from 0.03 and 0.09 (the most severely affected individual) to 0.62 and 0.50 (very mildly affected individual). Neuromuscular patients with severe arm weakness demonstrated movement largely limited to the ipsilateral lower quadrant of their reachable workspace.

Conclusions/Significance

The findings indicate that the proposed stereo camera-based reachable workspace analysis system is capable of distinguishing individuals with varying degrees of proximal upper limb functional impairments.     

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.     

An Electrocorticographic Brain Interface in an Individual with Tetraplegia

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.     

Trunk Muscle Activation at the Initiation and Braking of Bilateral Shoulder Flexion Movements of Different Amplitudes

The aim of this study was to investigate if trunk muscle activation patterns during rapid bilateral shoulder flexions are affected by movement amplitude. Eleven healthy males performed shoulder flexion movements starting from a position with arms along sides (0°) to either 45°, 90° or 180°. EMG was measured bilaterally from transversus abdominis (TrA), obliquus internus (OI) with intra-muscular electrodes, and from rectus abdominis (RA), erector spinae (ES) and deltoideus with surface electrodes. 3D kinematics was recorded and inverse dynamics was used to calculate the reactive linear forces and torque about the shoulders and the linear and angular impulses. The sequencing of trunk muscle onsets at the initiation of arm movements was the same across movement amplitudes with ES as the first muscle activated, followed by TrA, RA and OI. All arm movements induced a flexion angular impulse about the shoulders during acceleration that was reversed during deceleration. Increased movement amplitude led to shortened onset latencies of the abdominal muscles and increased level of activation in TrA and ES. The activation magnitude of TrA was similar in acceleration and deceleration where the other muscles were specific to acceleration or deceleration. The findings show that arm movements need to be standardized when used as a method to evaluate trunk muscle activation patterns and that inclusion of the deceleration of the arms in the analysis allow the study of the relationship between trunk muscle activation and direction of perturbing torque during one and the same arm movement.     

Studying brain functions with mesoscopic measurements: Advances in electrocorticography for non-human primates

Our brain is organized in a modular structure. Information in different modalities is processed within distinct cortical areas. However, individual cortical areas cannot enable complex cognitive functions without interacting with other cortical areas. Electrocorticography (ECoG) has recently become an important tool for studying global network activity across cortical areas in animal models. With stable recordings of electrical field potentials from multiple cortical areas, ECoG provides an opportunity to systematically study large-scale cortical activity at a mesoscopic spatiotemporal resolution under various experimental conditions. Recent developments in thin, flexible ECoG electrodes permit recording field potentials from not only gyral but intrasulcal cortical surfaces. Our review here focuses on the recent advances of ECoG applications to non-human primates.     

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., <1 cm/<1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning¹, auditory processing² and visual-spatial attention³. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function^1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user''s intentions to enhance or improve communication⁶ and control⁷. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform^8,9 and the SIGFRIED¹⁰ method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.

Prerequisites and Planning:

Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient''s brain, and on relevant factors of the patient''s history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient''s performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital''s institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient''s endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient''s eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5^th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software^8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers'' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient''s electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping¹⁰.The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.     

The three-dimensional structure of alpha1-purothionin in solution: combined use of nuclear magnetic resonance, distance geometry and restrained molecular dynamics

The determination of the three-dimensional solution structure of α1-purothionin using a combination of metric matrix distance geometry and restrained molecular dynamics calculations based on n.m.r. data is presented. The experimental data comprise complete sequence-specific proton resonance assignments, a set of 310 approximate interproton distance restraints derived from nuclear Overhauser effects, 27 Ø backbone torsion angle restraints derived from vicinal coupling constants, 4 distance restraints from hydrogen bonds and 12 distance restraints from disulphide bridges. The average atomic rms difference between the final nine converged structures and the mean structure obtained by averaging their coordinates is 1.5 ± 0.1 å for the backbone atoms and 2.0 ± 0.1 å for all atoms. The overall shape of α1-purothionin is that of the capital letter L, similar to that of crambin, with the longer arm comprising two approximately parallel α-helices and the shorter arm a strand and a mini anti-parallel β sheet.     

Eye-Hand Coordination during Visuomotor Adaptation with Different Rotation Angles: Effects of Terminal Visual Feedback

This study examined adaptive changes of eye-hand coordination during a visuomotor rotation task under the use of terminal visual feedback. Young adults made reaching movements to targets on a digitizer while looking at targets on a monitor where the rotated feedback (a cursor) of hand movements appeared after each movement. Three rotation angles (30°, 75° and 150°) were examined in three groups in order to vary the task difficulty. The results showed that the 30° group gradually reduced direction errors of reaching with practice and adapted well to the visuomotor rotation. The 75° group made large direction errors of reaching, and the 150° group applied a 180° reversal shift from early practice. The 75°and 150° groups, however, overcompensated the respective rotations at the end of practice. Despite these group differences in adaptive changes of reaching, all groups gradually adapted gaze directions prior to reaching from the target area to the areas related to the final positions of reaching during the course of practice. The adaptive changes of both hand and eye movements in all groups mainly reflected adjustments of movement directions based on explicit knowledge of the applied rotation acquired through practice. Only the 30° group showed small implicit adaptation in both effectors. The results suggest that by adapting gaze directions from the target to the final position of reaching based on explicit knowledge of the visuomotor rotation, the oculomotor system supports the limb-motor system to make precise preplanned adjustments of reaching directions during learning of visuomotor rotation under terminal visual feedback.     

Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users

PurposeTo assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs).MethodsSeventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr^-1) and propulsion at 4km·hr^-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.ResultsAccelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.ConclusionThe results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.     

Electrical activation in the coronary sinus branches as a guide to cardiac resynchronisation therapy: rationale for a coordinate system

Background

For successful cardiac resynchronisation therapy (CRT) a spatial and electrical separation of right and left ventricular electrodes is essential. The spatial distribution of electrical delays within the coronary sinus (CS) tributaries has not yet been identified.

Objective

Electrical delays within the CS are described during sinus rhythm (SR) and right ventricular pacing (RVP). A coordinate system grading the mitral ring from 0° to 360° and three vertical segments is proposed to define the lead positions irrespective of individual CS branch orientation.

Methods

In 13 patients undergoing implantation of a CRT device 6±2.5, (median 5) lead positions within the CS were mapped during SR and RVP. The delay to the onset and the peak of the local signal was measured from the earliest QRS activation or the pacing spike. Fluoroscopic positions were compared to localizations on a nonfluoroscopic electrode imaging system.

Results

During SR, electrical delays in the CS were inhomogenous in patients with or without left bundle branch block (LBBB). During RVP, the delays increased by 44±32 ms (signal onset from 36±33 ms to 95±30 ms; p<0.001, signal peak from 105±44 ms to 156±30 ms; p<0.001). The activation pattern during RVP was homogeneous and predictable by taking the grading on the CS ring into account: (% QRS) = 78−0.002 (grade−162)², p<0.0001. This indicates that 78% of the QRS duration can be expected as a maximum peak delay at 162° on the CS ring.

Conclusion

Electrical delays within the CS vary during SR, but prolong and become predictable during RVP. A coordinate system helps predicting the local delays and facilitates interindividual comparison of lead positions irrespective of CS branch anatomy.     

The Structure of Mytilus Smooth Muscle and the Electrical Constants of the Resting Muscle

The individual muscle fibers of the anterior byssus retractor muscle (ABRM) of Mytilus edulis L. are uninucleate, 1.2–1.8 mm in length, 5 µm in diameter, and organized into bundles 100–200 µm in diameter, surrounded by connective tissue. Some bundles run the length of the whole muscle. Adjacent muscle cell membranes are interconnected by nexuses at frequent intervals. Specialized attachments exist between muscle fibers and connective tissue. Electrical constants of the resting muscle membrane were measured with intracellular recording electrodes and both extracellular and intracellular current-passing electrodes. With an intracellular current-passing electrode, the time constant τ, was 4.3 ± 1.5 ms. With current delivered via an extracellular electrode τ was 68.3 ± 15 ms. The space constant, λ, was 1.8 mm ± 0.4. The membrane input resistance, R_eff, ranged from 23 to 51 MΩ. The observations that values of τ depend on the method of passing current, and that the value of λ is large relative to fiber length and diameter are considered evidence that the individual muscle fibers are electrically interconnected within bundles in a three-dimensional network. Estimations are made of the membrane resistance, R_m, to compare the values to fast and slow striated muscle fibers and mammalian smooth muscles. The implications of this study in reinterpreting previous mechanical and electrical studies are discussed.     

A Multimodal,SU-8 - Platinum - Polyimide Microelectrode Array for Chronic In Vivo Neurophysiology

Utilization of polymers as insulator and bulk materials of microelectrode arrays (MEAs) makes the realization of flexible, biocompatible sensors possible, which are suitable for various neurophysiological experiments such as in vivo detection of local field potential changes on the surface of the neocortex or unit activities within the brain tissue. In this paper the microfabrication of a novel, all-flexible, polymer-based MEA is presented. The device consists of a three dimensional sensor configuration with an implantable depth electrode array and brain surface electrodes, allowing the recording of electrocorticographic (ECoG) signals with laminar ones, simultaneously. In vivo recordings were performed in anesthetized rat brain to test the functionality of the device under both acute and chronic conditions. The ECoG electrodes recorded slow-wave thalamocortical oscillations, while the implanted component provided high quality depth recordings. The implants remained viable for detecting action potentials of individual neurons for at least 15 weeks.     

On the possibility of linear modelling the human arm neuromuscular apparatus

It has been widely claimed that linear models of the neuromuscular apparatus give very inaccurate approximations of human arm reaching movements. The present paper examines this claim by quantifying the contributions of the various non-linear effects of muscle force generation on the accuracy of linear approximation. We performed computer simulations of a model of a two-joint arm with six monarticular and biarticular muscles. The global actions of individual muscles resulted in a linear dependence of the joint torques on the joint angles and angular velocities, despite the great non-linearity of the muscle properties. The effect of time delay in force generation is much more important for model accuracy than all the non-linear effects, while ignoring this time delay in linear approximation results in large errors. Thus, the viscosity coefficients are rather underestimated and some of them can even be paradoxically estimated to be negative. Similarly, our computation showed that ignoring the time delay resulted in large errors in the estimation of the hand equilibrium trajectory. This could explain why experimentally estimated hand equilibrium trajectories may be complex, even during a simple reaching movement. The hand equilibrium trajectory estimated by a linear model becomes simple when the time delay is taken into account, and it is close to that actually used in the non-linear model. The results therefore provide a theoretical basis for estimating the hand equilibrium trajectory during arm reaching movements and hence for estimating the time course of the motor control signals associated with this trajectory, as set out in the equilibrium point hypothesis. Received: 17 February 1999 / Accepted in revised form: 22 October 1999     

Real-Time Decision Fusion for Multimodal Neural Prosthetic Devices

Background

The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user''s intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device.

Methodology/Principal Findings

Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.

Conclusions

Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.     

Neuron selection by relative importance for neural decoding of dexterous finger prosthesis control application

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain–machine interfaces for direct neural control of hand prostheses.     

fNIRS Exhibits Weak Tuning to Hand Movement Direction

Functional near-infrared spectroscopy (fNIRS) has become an established tool to investigate brain function and is, due to its portability and resistance to electromagnetic noise, an interesting modality for brain-machine interfaces (BMIs). BMIs have been successfully realized using the decoding of movement kinematics from intra-cortical recordings in monkey and human. Recently, it has been shown that hemodynamic brain responses as measured by fMRI are modulated by the direction of hand movements. However, quantitative data on the decoding of movement direction from hemodynamic responses is still lacking and it remains unclear whether this can be achieved with fNIRS, which records signals at a lower spatial resolution but with the advantage of being portable. Here, we recorded brain activity with fNIRS above different cortical areas while subjects performed hand movements in two different directions. We found that hemodynamic signals in contralateral sensorimotor areas vary with the direction of movements, though only weakly. Using these signals, movement direction could be inferred on a single-trial basis with an accuracy of ∼65% on average across subjects. The temporal evolution of decoding accuracy resembled that of typical hemodynamic responses observed in motor experiments. Simultaneous recordings with a head tracking system showed that head movements, at least up to some extent, do not influence the decoding of fNIRS signals. Due to the low accuracy, fNIRS is not a viable alternative for BMIs utilizing decoding of movement direction. However, due to its relative resistance to head movements, it is promising for studies investigating brain activity during motor experiments.     

Effects of Aging on Genioglossus Motor Units in Humans

The genioglossus is a major upper airway dilator muscle thought to be important in obstructive sleep apnea pathogenesis. Aging is a risk factor for obstructive sleep apnea although the mechanisms are unclear and the effects of aging on motor unit remodeled in the genioglossus remains unknown. To assess possible changes associated with aging we compared quantitative parameters related to motor unit potential morphology derived from EMG signals in a sample of older (n = 11; >55 years) versus younger (n = 29; <55 years) adults. All data were recorded during quiet breathing with the subjects awake. Diagnostic sleep studies (Apnea Hypopnea Index) confirmed the presence or absence of obstructive sleep apnea. Genioglossus EMG signals were analyzed offline by automated software (DQEMG), which estimated a MUP template from each extracted motor unit potential train (MUPT) for both the selective concentric needle and concentric needle macro (CNMACRO) recorded EMG signals. 2074 MUPTs from 40 subjects (mean±95% CI; older AHI 19.6±9.9 events/hr versus younger AHI 30.1±6.1 events/hr) were extracted. MUPs detected in older adults were 32% longer in duration (14.7±0.5 ms versus 11.1±0.2 ms; P  =  0.05), with similar amplitudes (395.2±25.1 µV versus 394.6±13.7 µV). Amplitudes of CNMACRO MUPs detected in older adults were larger by 22% (62.7±6.5 µV versus 51.3±3.0 µV; P<0.05), with areas 24% larger (160.6±18.6 µV.ms versus 130.0±7.4 µV.ms; P<0.05) than those detected in younger adults. These results confirm that remodeled motor units are present in the genioglossus muscle of individuals above 55 years, which may have implications for OSA pathogenesis and aging related upper airway collapsibility.