I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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Biomechanics And Motor Control Of Human Movement

Background
Physical therapy (PT) is one of the key disciplines in interdisciplinary stroke rehabilitation. The aim of this systematic review was to provide an update of the evidence for stroke rehabilitation interventions in the domain of PT.

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What is the evidence for physical therapy poststroke? A systematic review and meta-analysis.

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

/pdf/surface-electromyography-signal-processing-and-138ym9ds1t.pdf

Surface Electromyography Signal Processing and Classification Techniques

This paper compares the fuzzy controllers trained with three different data-based methodologies for the control of the active knee joint of a powered transfemoral prosthesis. For each methodology a different inference system structure and number of generated rules was produced. The control is optimized using adaptive neuro-fuzzy optimization techniques and it is aimed to work at different speeds within normal gait.

Comparison of Different Fuzzy Controllers for Powered Prosthetic Gait

Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, DLR–German Aerospace Center, Institute of Robotics and Mechatronics, Weßling, Germany, Department of Psychology, University of Zurich, Zurich, Switzerland, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark

/pdf/editorial-embodiment-and-co-adaptation-through-human-machine-197tul7n.pdf

Editorial: Embodiment and Co-adaptation Through Human-Machine Interfaces: At the Border of Robotics, Neuroscience and Psychology

The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of the latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: HO, robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component of the system is used to analyze the closed-loop latency and decide upon the optimal compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, for systems whose latency budget consists of many components.

Statistical Characterization of Closed-Loop Latency at the Mobile Edge

Detection of movement intention is a critical step for the development of rehabilitation systems and the induction of plasticity using brain-computer interfacing. The movement-related cortical potential (MRCP), obtained from electroencephalography (EEG) signals, is an attractive modality for movement detection as it can be generated 1–2 s prior to the movement execution. In the present study, monopolar EEG signals were recorded from ten channels in five Amyotrophic Lateral Sclerosis (ALS) patients and ten healthy participants while performing hand movement (hand extension/flexion). Movements were detected offline by using classification (Support Vector Machine) between movement and rest epochs with three different groups of time-domain features. The first group of features were time samples of filtered down-sampled EEG (raw features) while the second group (computed features) included the features calculated from extracted MRCPs and rest epochs (e.g., slope, peak negativity and variations in different time segments). In the third condition, the two groups of features were combined. The results revealed that detection accuracy obtained from raw features $(88\pm 3\%)$ was higher than either computed features $(83\pm 3\%)$ or a combination of the two $(84\pm 3\%)$ . Therefore, the time samples of EEG signals seem to be a better choice for movement detection using MRCPs.

Selection of Temporal Features for the Detection of Movement Intention in patients with Amyotrophic Lateral Sclerosis

Mind controlled bionic limbs promise to replace mechanical function of lost biological extremities and restore amputees’ motor capacity. State of the art approaches use machine learning for establishing a mapping function between electromyography (EMG) and joint kinematics. However, current approaches require frequent recalibration with lack of robustness, thus providing control paradigms that are sensitive to external conditions. This paper presents an alternative method based on the authors’ recent findings. That is, a biomimetic decoder comprising a computational model that explicitly synthesizes the dynamics of the musculoskeletal system as controlled by EMG-derived neural activation signals.

Strahinja Dosen

Papers

Comparison of Different Fuzzy Controllers for Powered Prosthetic Gait

Editorial: Embodiment and Co-adaptation Through Human-Machine Interfaces: At the Border of Robotics, Neuroscience and Psychology

Statistical Characterization of Closed-Loop Latency at the Mobile Edge

Selection of Temporal Features for the Detection of Movement Intention in patients with Amyotrophic Lateral Sclerosis

Decoding Phantom Limb Neuro-Mechanical Function for a New Paradigm of Mind-Controlled Bionic Limbs