Functional Electrical Stimulation and Brain-Machine Interfaces for Simultaneous Control of Wrist and Finger Flexion.

Brain-machine interface (BMI) controlled functional electrical stimulation (FES) is a promising treatment to restore hand movements to people with cervical spinal cord injury. Recent intracortical BMIs have shown unprecedented successes in decoding user intentions, however the hand movements restored by FES have largely been limited to predetermined grasps. Restoring dexterous hand movements will require continuous control of many biomechanically linked degrees-of-freedom in the hand, such as wrist and finger flexion, that would form the basis of those movements. Here we investigate the ability to restore simultaneous wrist and finger flexion, which would enable grasping with a controlled hand posture and assist in manipulating objects once grasped. We demonstrate that intramuscular FES can enable monkeys with temporarily paralyzed hands to move their fingers and wrist across a functional range of motion, spanning an average 88.6 degrees at the metacarpophalangeal joint flexion and 71.3 degrees of wrist flexion, and intramuscular FES can control both joints simultaneously in a real-time task. Additionally, we demonstrate a monkey using an intracortical BMI to control the wrist and finger flexion in a virtual hand, both before and after the hand is temporarily paralyzed, even achieving success rates and acquisition times equivalent to able-bodied control with BMI control after temporary paralysis in two sessions. Together, this outlines a method using an artificial brain-to-body interface that could restore continuous wrist and finger movements after spinal cord injury.

Effects of voluntary exercise and electrical muscle stimulation on reaction time in the Go/No-Go task.

INTRODUCTION: Acute exercise improves cognitive performance. However, it remains unclear what triggers cognitive improvement. Electrical muscle stimulation (EMS) facilitates the examination of physiological changes derived from peripheral muscle contraction during exercise. Thus, we compared the effects of EMS and voluntary exercise at low- or moderate-intensity on reaction time (RT) in a cognitive task to understand the contribution of central and peripheral physiological factors to RT improvement. METHODS: Twenty-four young, healthy male participants performed a Go/No-Go task before and after EMS/exercise. In the EMS condition, EMS was applied to the lower limb muscles. In the low-intensity exercise condition, the participants cycled an ergometer while maintaining their heart rate (HR) at the similar level during EMS. In the moderate-intensity exercise condition, exercise intensity corresponded to ratings of perceived exertion of 13/20. The natural log-transformed root mean square of successive differences between adjacent inter-beat (R-R) intervals (LnRMSSD), which predominantly reflects parasympathetic HR modulation, was calculated before and during EMS/exercise. RESULTS: RT improved following moderate-intensity exercise (p = 0.002, Cohen' d = 0.694), but not following EMS (p = 0.107, Cohen' d = 0.342) and low-intensity exercise (p = 0.076, Cohen' d = 0.380). Repeated measures correlation analysis revealed that RT was correlated with LnRMSSD (Rrm(23) = 0.599, p = 0.002) in the moderate-intensity exercise condition. CONCLUSION: These findings suggest that the amount of central neural activity and exercise pressor reflex may be crucial for RT improvement. RT improvement following moderate-intensity exercise may, at least partly, be associated with enhanced sympathetic nervous system activity.

Combined effects of electrical muscle stimulation and cycling exercise on cognitive performance.

The purpose of this study was to investigate whether a combination of electrical muscle stimulation (EMS) and cycling exercise is beneficial for improving cognitive performance. Eighteen participants (7 females and 11 males) performed a Go/No-Go task before and 2 min after i) cycling exercise (EX), ii) a combination of EMS and cycling (EMS + EX) and iii) a control (rest) intervention in a randomized controlled crossover design. In the EX intervention, the participants cycled an ergometer for 20 min with their heart rate maintained at ∼120 beats·min-1. In the EMS + EX intervention, the participants cycled an ergometer simultaneously with EMS for 20 min, with heart rate maintained at ∼120 beats·min-1. In the Control intervention, the participants remained at rest while seated on the ergometer. Cognitive performance was assessed by reaction time (RT) and accuracy. There was a significant interaction between intervention and time (p = 0.007). RT was reduced in the EX intervention (p = 0.054, matched rank biserial correlation coefficient = 0.520). In the EMS + EX intervention, RT was not altered (p = 0.243, Cohen's d = 0.285) despite no differences in heart rate between the EX and EMS + EX interventions (p = 0.551). RT was increased in the Control intervention (p = 0.038, Cohen's d = -0.529). These results indicate that combining EMS and cycling does not alter cognitive performance despite elevated heart rate, equivalent to a moderate intensity. The present findings suggest that brain activity during EMS with cycling exercise may be insufficient to improve cognitive performance when compared to exercise alone.

Human dopaminergic system in the exercise-cognition link.

While the dopaminergic system is important for cognitive processes, it is also sensitive to the influence of physical activity (PA). We summarize current evidence on whether PA-related changes in the human dopaminergic system are associated with alterations in cognitive performance, discuss recent advances, and highlight challenges and opportunities for future research.

The effects of sleep deprivation and extreme exertion on cognitive performance at the world-record breaking Suffolk Back Yard Ultra-marathon.

Using a prospective observational design, this study investigated the hypothesis that competing in the Suffolk Back Yard Ultra-marathon, would result in impaired cognitive performance and examined whether pre-race sleep patterns could mitigate this. Fifteen runners (1 female) volunteered to undertake this study and eleven males were included in the final analysis. Before the race and after withdrawal participants completed the following cognitive performance tasks: 2 Choice Reaction Time (2CRT), Stroop, and the Tower Puzzle. Pre-race sleep strategies were subjectively recorded with a 7-day sleep diary. Following race withdrawal, reaction time increased (Δ 77±68 ms; p = 0.004) in the 2CRT and executive function was impaired in the Stroop task (Interference score Δ -4.3±5.6 a.u.; p = 0.028). Decision making was not affected in the Tower Puzzle task. There was a significant correlation between the pre-race 7-day average sleep scores and both 2CRT Δ throughput (r = 0.61; p = 0.045) and 2CRT Δ RT (r = -0.64; p = 0.034). This study supports the hypothesis that running an ultra-marathon, which includes at least one night of sleep deprivation, impairs cognitive performance and provides novel evidence suggesting good sleep quality, in the week prior to an ultra-marathon, could minimise these effects.

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods.

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks. Teaser: A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.

The neuromodulatory role of dopamine in improved reaction time by acute cardiovascular exercise.

Acute cardiovascular physical exercise improves cognitive performance, as evidenced by a reduction in reaction time (RT). However, the mechanistic understanding of how this occurs is elusive and has not been rigorously investigated in humans. Here, using positron emission tomography (PET) with [11 C]raclopride, in a multi-experiment study we investigated whether acute exercise releases endogenous dopamine (DA) in the brain. We hypothesized that acute exercise augments the brain DA system, and that RT improvement is correlated with this endogenous DA release. The PET study (Experiment 1: n = 16) demonstrated that acute physical exercise released endogenous DA, and that endogenous DA release was correlated with improvements in RT of the Go/No-Go task. Thereafter, using two electrical muscle stimulation (EMS) studies (Experiments 2 and 3: n = 18 and 22 respectively), we investigated what triggers RT improvement. The EMS studies indicated that EMS with moderate arm cranking improved RT, but RT was not improved following EMS alone or EMS combined with no load arm cranking. The novel mechanistic findings from these experiments are: (1) endogenous DA appears to be an important neuromodulator for RT improvement and (2) RT is only altered when exercise is associated with central signals from higher brain centres. Our findings explain how humans rapidly alter their behaviour using neuromodulatory systems and have significant implications for promotion of cognitive health. KEY POINTS: Acute cardiovascular exercise improves cognitive performance, as evidenced by a reduction in reaction time (RT). However, the mechanistic understanding of how this occurs is elusive and has not been rigorously investigated in humans. Using the neurochemical specificity of [11 C]raclopride positron emission tomography, we demonstrated that acute supine cycling released endogenous dopamine (DA), and that this release was correlated with improved RT. Additional electrical muscle stimulation studies demonstrated that peripherally driven muscle contractions (i.e. exercise) were insufficient to improve RT. The current study suggests that endogenous DA is an important neuromodulator for RT improvement, and that RT is only altered when exercise is associated with central signals from higher brain centres.

The effects of sleep deprivation, acute hypoxia, and exercise on cognitive performance: A multi-experiment combined stressors study.

INTRODUCTION: Both sleep deprivation and hypoxia have been shown to impair executive function. Conversely, moderate intensity exercise is known to improve executive function. In a multi-experiment study, we tested the hypotheses that moderate intensity exercise would ameliorate any decline in executive function after i) three consecutive nights of partial sleep deprivation (PSD) (Experiment 1) and ii) the isolated and combined effects of a single night of total sleep deprivation (TSD) and acute hypoxia (Experiment 2). METHODS: Using a rigorous randomised controlled crossover design, 12 healthy participants volunteered in each experiment (24 total, 5 females). In both experiments seven executive function tasks (2-choice reaction time, logical relations, manikin, mathematical processing, 1-back, 2-back, 3-back) were completed at rest and during 20 min semi-recumbent, moderate intensity cycling. Tasks were completed in the following conditions: before and after three consecutive nights of PSD and habitual sleep (Experiment 1) and in normoxia and acute hypoxia (FIO2 = 0.12) following one night of habitual sleep and one night of TSD (Experiment 2). RESULTS: Although the effects of three nights of PSD on executive functions were inconsistent, one night of TSD (regardless of hypoxic status) reduced executive functions. Significantly, regardless of sleep or hypoxic status, executive functions are improved during an acute bout of moderate intensity exercise. CONCLUSION: These novel data indicate that moderate intensity exercise improves executive function performance after both PSD and TSD, regardless of hypoxic status. The key determinants and/or mechanism(s) responsible for this improvement still need to be elucidated. Future work should seek to identify these mechanisms and translate these significant findings into occupational and skilled performance settings.

Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.

Objective.While brain-machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e. consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.Mainresults.First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below5%, it was possible to achieve mean true positive rate of28.1%online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.Significance.Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements may be obtained by enhancing classification and correction strategies.

Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces.

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns.

The impact of task context on predicting finger movements in a brain-machine interface.

A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that decoders trained in one context did not generalize well to other contexts, leading to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either the decoder training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts. This shift in neural activity possibly explains biases to off-context kinematic predictions and suggests a feature that could help predict different magnitude muscle activations while producing similar kinematics.

The effects of manipulating the visual environment on thermal perception: A structured narrative review.

When exposed to ambient temperatures that cause thermal discomfort, a human's behavioral responses are more effective than autonomic ones at compensating for thermal imbalance. These behavioral thermal responses are typically directed by an individual's perception of the thermal environment. Perception of the environment is a holistic amalgamation of human senses, and in some circumstances, humans prioritize visual information. Existing research has considered this in the specific case of thermal perception, and this review investigates the state of the literature examining this effect. We identify the frameworks, research rationales, and potential mechanisms that underpin the evidence base in this area. Our review identified 31 experiments, comprising 1392 participants that met the inclusion criteria. Methodological heterogeneity was observed in the assessment of thermal perception, and a variety of methods were employed to manipulate the visual environment. However, the majority of the included experiments (80%) reported a difference in thermal perception after the visual environment was manipulated. There was limited research exploring any effects on physiological variables (e.g. skin and core temperature). This review has wide-ranging implications for the broad discipline of (thermo)physiology, psychology, psychophysiology, neuroscience, ergonomics, and behavior.

Thalamic Segmentation and Neural Activation Modeling Based on Individual Tissue Microstructure in Deep Brain Stimulation for Essential Tremor.

OBJECTIVE: Thalamic deep brain stimulation (DBS) is the primary surgical therapy for essential tremor (ET). Thalamic DBS traditionally uses an atlas-based targeting approach, which, although nominally accurate, may obscure individual anatomic differences from population norms. The objective of this study was to compare this traditional atlas-based approach with a novel quantitative modeling methodology grounded in individual tissue microstructure (N-of-1 approach). MATERIALS AND METHODS: The N-of-1 approach uses individual patient diffusion tensor imaging (DTI) data to perform thalamic segmentation and volume of tissue activation (VTA) modeling. For each patient, the thalamus was individually segmented into 13 nuclei using DTI-based k-means clustering. DBS-induced VTAs associated with tremor suppression and side effects were then computed for each patient with finite-element electric-field models incorporating DTI microstructural data. Results from N-of-1 and traditional atlas-based modeling were compared for a large cohort of patients with ET treated with thalamic DBS. RESULTS: The size and shape of individual N-of-1 thalamic nuclei and VTAs varied considerably across patients (N = 22). For both methods, tremor-improving therapeutic VTAs showed similar overlap with motor thalamic nuclei and greater motor than sensory nucleus overlap. For VTAs producing undesirable sustained paresthesia, 94% of VTAs overlapped with N-of-1 sensory thalamus estimates, whereas 74% of atlas-based segmentations overlapped. For VTAs producing dysarthria/motor contraction, the N-of-1 approach predicted greater spread beyond the thalamus into the internal capsule and adjacent structures than the atlas-based method. CONCLUSIONS: Thalamic segmentation and VTA modeling based on individual tissue microstructure explain therapeutic stimulation equally well and side effects better than a traditional atlas-based method in DBS for ET. The N-of-1 approach may be useful in DBS targeting and programming, particularly when patient neuroanatomy deviates from population norms.

Evidence that heat acclimation training may alter sleep and incidental activity.

This randomized cross-over study tested the hypothesis that heat acclimation training would detrimentally affect sleep variables and alter incidental physical activity compared to a thermoneutral training control condition. Eight recreationally trained males (V̇O2peak 49±4.9 mL.kg-1.min-1) completed two separate interventions separated by at least 31 days: 5 consecutive day training blocks of moderate-intensity cycling (60 min·day-1 at 50% peak power output) in a hot (34.9±0.7 °C and 53±4 % relative humidity) and a temperate (22.2±2.6 °C; 65±8 % relative humidity) environment. Wrist-mounted accelerometers were worn continuously for the length of the training blocks and recorded physical activity, sleep quality and quantity. Data were analysed in a Bayesian framework, with the results presented as the posterior probability that a coefficient was greater or less than zero. Compared to the temperate training environment, heat acclimation impaired sleep efficiency (Pr ß<0 = .979) and wake on sleep onset (Pr ß>0 = .917). Daily sedentary time was, on average, 35 min longer (Pr ß>0 = .973) and light physical activity time 18 min shorter (Pr ß>0 = .960) during the heat acclimation period. No differences were observed between conditions in sleep duration, subjective sleep quality, or moderate or vigorous physical activity. These findings may suggest that athletes and coaches need to be cognisant that heat acclimation training may alter sleep quality and increase sedentary behaviour.HighlightsFive consecutive days of heat training negatively affected some objective measures of sleep quality and incidental physical activity in recreationally trained athletes.Athletes and coaches need to be aware of the potential unintended consequences of using heat acclimation on sleep behaviours.

The effects of normobaric and hypobaric hypoxia on cognitive performance and physiological responses: A crossover study.

This partially randomised controlled, crossover study sought to investigate the effects of normobaric hypoxia (NH) and hypobaric hypoxia (HH) on cognitive performance, the physiological response at rest and after a 3-min step-test. Twenty healthy participants (10 females and 10 males, 27.6±6.2yrs, 73.6±13.7kg, 175.3±8.9cm) completed a cognitive performance test, followed by the modified Harvard-step protocol, in four environments: normobaric normoxia (NN; PiO2: 146.0±1.5mmHg), NH (PiO2: 100.9±1.3mmHg), HH at the first day of ascent (HH1: PiO2 = 105.6±0.4mmHg) and HH after an overnight stay (HH2: PiO2 = 106.0±0.5mmHg). At rest and/or exercise, SpO2, NIRS, and cardiovascular and perceptual data were collected. The cerebral tissue oxygenation index and the cognitive performance (throughput, accuracy, and reaction time) were not different between the hypoxic conditions (all p>0.05). In NH, SpO2 was higher compared to HH1 (ΔSpO2 NH vs HH1: 1.7±0.5%, p = 0.003) whilst heart rate (ΔHR NH vs HH2: 5.8±2.6 bpm, p = 0.03) and sympathetic activation (ΔSNSi NH vs HH2: 0.8±0.4, p = 0.03) were lower in NH compared to HH2. Heart rate (ΔHR HH1 vs HH2: 6.9±2.6 bpm, p = 0.01) and sympathetic action (ΔSNSi HH1 vs HH2: 0.9±0.4, p = 0.02) were both lower in HH1 compared to HH2. In conclusion, cognitive performance and cerebral oxygenation didn't differ between the hypoxic conditions. SpO2 was only higher in NH compared to HH1. In HH2, heart rate and sympathetic activation were higher compared to both NH and HH1. These conclusions account for a PiO2 between 100-106 mmHg.

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.

Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.

The effects of acute high-intensity aerobic exercise on cognitive performance: A structured narrative review.

It is well established that acute moderate-intensity exercise improves cognitive performance. However, the effects of acute high-intensity aerobic exercise on cognitive performance have not been well characterized. In this review, we summarize the literature investigating the exercise-cognition interaction, especially focusing on high-intensity aerobic exercise. We discuss methodological and physiological factors that potentially mediate cognitive performance in response to high-intensity exercise. We propose that the effects of high-intensity exercise on cognitive performance are primarily affected by the timing of cognitive task (during vs. after exercise, and the time delay after exercise). In particular, cognitive performance is more likely to be impaired during high-intensity exercise when both cognitive and physiological demands are high and completed simultaneously (i.e., the dual-task paradigm). The effects may also be affected by the type of cognitive task, physical fitness, exercise mode/duration, and age. Second, we suggest that interactions between changes in regional cerebral blood flow (CBF), cerebral oxygenation, cerebral metabolism, neuromodulation by neurotransmitters/neurotrophic factors, and a variety of psychological factors are promising candidates that determine cognitive performance in response to acute high-intensity exercise. The present review has implications for recreational, sporting, and occupational activities where high cognitive and physiological demands are required to be completed concurrently.

A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry.

Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 µW/mm2 of light exposure. It achieves best-in-class power consumption of 0.57 µW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.

A low-power communication scheme for wireless, 1000 channel brain-machine interfaces.

Objective. Brain-machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating 'motes' which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption.Approach. To test PIM's ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption.Main results. We found that PIM at 1 kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels.Significance.These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.

Cognitive Improvement After Aerobic and Resistance Exercise Is Not Associated With Peripheral Biomarkers.

The role of peripheral biomarkers following acute physical exercise on cognitive improvement has not been systematically evaluated. This study aimed to explore the role of peripheral circulating biomarkers in executive performance following acute aerobic and resistance exercise. Nineteen healthy males completed a central executive (Go/No-Go) task before and after 30-min of perceived intensity matched aerobic and resistance exercise. In the aerobic condition, the participants cycled an ergometer at 40% peak oxygen uptake. In the resistance condition, they performed resistance exercise using elastic bands. Before and after an acute bout of physical exercise, venous samples were collected for the assessment of following biomarkers: adrenaline, noradrenaline, glucose, lactate, cortisol, insulin-like growth hormone factor 1, and brain-derived neurotrophic factor. Reaction time decreased following both aerobic exercise and resistance exercise (p = 0.04). Repeated measures correlation analysis indicated that changes in reaction time were not associated with the peripheral biomarkers (all p > 0.05). Accuracy tended to decrease in the resistance exercise condition (p = 0.054). Accuracy was associated with changes in adrenaline [r rm (18) = -0.51, p = 0.023], noradrenaline [r rm (18) = -0.66, p = 0.002], lactate [r rm (18) = -0.47, p = 0.035], and brain-derived neurotrophic factor [r rm (17) = -0.47, p = 0.044] in the resistance condition. These findings suggest that these peripheral biomarkers do not directly contribute to reduction in reaction time following aerobic or resistance exercise. However, greater sympathoexcitation, reflected by greater increase in noradrenaline, may be associated with a tendency for a reduction in accuracy after acute resistance exercise.