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Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

ABSTRACT Systems biology postulates the balance between energy production and conservation in optimizing locomotion. Here, we analyzed how mechanical energy production and conservation influenced metabolic energy expenditure in stroke survivors during treadmill walking at different speeds. We used the body center of mass (BCoM) and segmental center of mass to calculate mechanical energy production: external and each segment's mechanical work (Wseg). We also estimated energy conservation by applying the pendular transduction framework (i.e. energy transduction within the step; Rint). Energy conservation was likely optimized by the paretic lower-limb acting as a rigid shaft while the non-paretic limb pushed the BCoM forward at the slower walking speed. Wseg production was characterized by greater movements between the limbs and body, a compensatory strategy used mainly by the non-paretic limbs. Overall, Wseg production following a stroke was characterized by non-paretic upper-limb compensation, but also by an exaggerated lift of the paretic leg. This study also highlights how post-stroke subjects may perform a more economic gait while walking on a treadmill at preferred walking speeds. Complex neural adaptations optimize energy production and conservation at the systems level, and may fundament new insights onto post-stroke neurorehabilitation. This article has and associated First Person interview with the first author of the paper.

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

Wireless body sensor networks (WBSNs) mostly consist of low‐cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle‐computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity‐driven green‐oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128‐bit RN, which is much more efficient when compared to the electrocardiography‐based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes.

Water management is critical to optimize the operation of polymer electrolyte membrane fuel cells. At present, numerical models are employed to guide water management in such fuel cells. Accurate measurements of water content variation in polymer electrolyte membrane fuel cells are required to validate these models and to optimize fuel cell behavior. We report a direct water content measurement across the Nafion membrane in an operational polymer electrolyte membrane fuel cell, employing double half k-space spin echo single point imaging techniques. The MRI measurements with T2 mapping were undertaken with a parallel plate resonator to avoid the effects of RF screening. The parallel plate resonator employs the electrodes inherent to the fuel cell to create a resonant circuit at RF frequencies for MR excitation and detection, while still operating as a conventional fuel cell at DC. Three stages of fuel cell operation were investigated: activation, operation and dehydration. Each profile was acquired in 6 min, with 6 microm nominal resolution and a SNR of better than 15.

Inter-segmental coordination patterns and mechanical energy transfer were compared between vertical jumping tasks which possess different countermovement characteristics. Thirteen participants completed squat (SJ), countermovement (CMJ) and drop (DVJ) vertical jumps. Inter-segmental coordination patterns became more out-of-phase with increases in countermovement velocity (DVJ > CMJ > SJ), at the ankle, hip and lumbar spine (all p  0.05 for the ankle and lumbar spine). The relationship between mechanical energy transfer and inter-segmental coordination patterns during vertical jumping is not straightforward since the responses to these varying countermovement demands were not consistent across all joints.

To determine longitudinal effects of experiences related to the Fukushima nuclear disaster on March 11, 2011, on insomnia risk among workers of nuclear power plants on identified disaster-related potential risk factors for the subtypes of insomnia.We included a total of 1403 workers who responded to a questionnaire on insomnia-related symptoms measured by the Athens Insomnia Scale from 2011 to 2014 and on disaster-related experiences in 2011. We examined the longitudinal relationships between disaster-related experiences and insomnia using mixed-effect logistic regression models. We also used path analysis to examine the potential effects of disaster-related experiences on the subtypes of insomnia (i.e. initial, sleep maintenance, and early morning awakening).All disaster-related experiences were significantly associated with insomnia, except death of a family member or colleague. We found that most of these types of traumatic exposure were associated with the risk of insomnia in a time-independent way. However, the effect of experiencing life-threatening danger decreased with time. Based on the results of the path analysis, we found that life-threatening experiences, such as experiences of life-threatening danger or witnessing the explosion, may conjure up disturbing scenes that hamper sleep initiation. On the other hand, early morning awakening may be related to life uncertainty. We found that social discrimination/slurs was associated with all three types of insomnia and was also influenced by other experiences, such as life-threatening danger, property loss, and colleague death.Our findings emphasize the importance of comprehensive psychosocial support for workers with disaster-related experiences.

Recent theories and data suggest that adapted behavior involves economic computations during which multiple trade-offs between reward value, accuracy requirement, energy expenditure, and elapsing time are solved so as to obtain rewards as soon as possible while spending the least possible amount of energy. However, the relative impact of movement energy and duration costs on perceptual decision-making and movement initiation is poorly understood. Here, we tested 31 healthy subjects on a perceptual decision-making task in which they executed reaching movements to report probabilistic choices. In distinct blocks of trials, the reaching duration (“Time” condition) and energy (“Effort” condition) costs were independently varied compared to a “Reference” block, while decision difficulty was maintained similar at the block level. Participants also performed a simple delayed-reaching (DR) task aimed at estimating movement initiation duration in each motor condition. Results in that DR task show that long duration movements extended reaction times (RTs) in most subjects, whereas energy-consuming movements led to mixed effects on RTs. In the decision task, about half of the subjects decreased their decision durations (DDs) in the Time condition, while the impact of energy on DDs were again mixed across subjects. Decision accuracy was overall similar across motor conditions. These results indicate that movement duration and, to a lesser extent, energy expenditure, idiosyncratically affect perceptual decision-making and action initiation. We propose that subjects who shortened their choices in the time-consuming condition of the decision task did so to limit a drop of reward rate.