Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels.

Grasping is a complex task routinely performed in an anticipatory (feedforward) manner, where sensory feedback is responsible for learning and updating the internal model of grasp dynamics. This study aims at evaluating whether providing a proportional tactile force feedback during the myoelectric control of a prosthesis facilitates learning a stable internal model of the prosthesis force control. Ten able-bodied subjects controlled a sensorized myoelectric prosthesis performing four blocks of consecutive grasps at three levels of target force (30, 50, and 70%), repeatedly closing the fully opened hand.

GLIMPSE: Google Glass interface for sensory feedback in myoelectric hand prostheses.

Objective: Providing sensory feedback to the user of the prosthesis is an important challenge. The common approach is to use tactile stimulation, which is easy to implement but requires training and has limited information bandwidth. In this study, we propose an alternative approach based on augmented reality.

Electrotactile EMG feedback improves the control of prosthesis grasping force.

Objective: A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate.

Approach: In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup.

Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis.

Objective: Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control.

Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use.

Pattern recognition and regression methods applied to the surface EMG have been used for estimating the user intended motor tasks across multiple degrees of freedom (DOF), for prosthetic control. While these methods are effective in several conditions, they are still characterized by some shortcomings. In this study we propose a methodology that combines these two approaches for mutually alleviating their limitations.

EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis.

Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing.

The impact of the stimulation frequency on closed-loop control with electrotactile feedback.

Background: Electrocutaneous stimulation can restore the missing sensory information to prosthetic users. In electrotactile feedback, the information about the prosthesis state is transmitted in the form of pulse trains. The stimulation frequency is an important parameter since it influences the data transmission rate over the feedback channel as well as the form of the elicited tactile sensations.

Covariate shift adaptation in EMG pattern recognition for prosthetic device control.

Ensuring robustness of myocontrol algorithms for prosthetic devices is an important challenge. Robustness needs to be maintained under nonstationarities, e.g.

Extending mode switching to multiple degrees of freedom in hand prosthesis control is not efficient.

In recent years, many sophisticated control strategies for multifunctional dexterous hand prostheses have been developed. It was indeed assumed that control mechanisms based on switching between degrees of freedom, which are in use since the 1960's, could not be extended to efficient control of more than two degrees of freedom. However, quantitative proof for this assumption has not been shown.

A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees.

Functional replacement of upper limbs by means of dexterous prosthetic devices remains a technological challenge. While the mechanical design of prosthetic hands has advanced rapidly, the human-machine interfacing and the control strategies needed for the activation of multiple degrees of freedom are not reliable enough for restoring hand function successfully. Machine learning methods capable of inferring the user intent from EMG signals generated by the activation of the remnant muscles are regarded as a promising solution to this problem.

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges.

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.

Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control.

Pattern recognition methods for classifying user motion intent based on surface electromyography developed by research groups in well-controlled laboratory conditions are not yet clinically viable for upper limb prosthesis control, due to their limited robustness in users' real-life situations. To address this problem, a novel postprocessing algorithm, aiming to detect and remove misclassifications of a pattern recognition system of forearm and hand motions, is proposed. Using the maximum likelihood calculated by a classifier and the mean global muscle activity of the forearm, an artificial neural network was trained to detect potentially erroneous classification decisions.

Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control?

In this paper, we present a systematic analysis of the relationship between the accuracy of the mapping between EMG and hand kinematics and the control performance in goal-oriented tasks of three simultaneous and proportional myoelectric control algorithms: nonnegative matrix factorization (NMF), linear regression (LR), and artificial neural networks (ANN). The purpose was to investigate the impact of the precision of the kinematics estimation by a myoelectric controller for accurately complete goal-directed tasks. Nine naïve subjects performed a series of goal-directed myoelectric control tasks using the three algorithms, and their online performance was characterized by 6 indexes.

Long term stability of surface EMG pattern classification for prosthetic control.

Long-term functioning of a hand prosthesis is crucial for its acceptance by patients with upper limb deficit. In this study the reliability over days of the performance of pattern classification approaches based on surface electromyography (sEMG) signal for the control of upper limb prostheses was investigated. Recordings of sEMG from the forearm muscles were obtained across five consecutive days from five healthy subjects.

Intuitive, online, simultaneous, and proportional myoelectric control over two degrees-of-freedom in upper limb amputees.

We propose an approach for online simultaneous and proportional myoelectric control of two degrees-of-freedom (DoF) of the wrist, using surface electromyographic signals. The method is based on the nonnegative matrix factorization (NMF) of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables. This procedure does not need a training set of signals for which the kinematics is known (labeled dataset) and is thus unsupervised (although it requires an initial calibration without labeled signals).

Real time simultaneous and proportional control of multiple degrees of freedom from surface EMG: Preliminary results on subjects with limb deficiency.

We present the real time simultaneous and proportional control of two degrees of freedom (DoF), using surface electromyographic signals from the residual limbs of three subject with limb deficiency. Three subjects could control a virtual object in two dimensions using their residual muscle activities to achieve goal-oriented tasks. The subjects indicated that they found the control intuitive and useful.

Effect of arm position on the prediction of kinematics from EMG in amputees.

Myoelectric control has been extensively applied in multi-function hand/wrist prostheses. The performance of this type of control is however, influenced by several practical factors that still limit its clinical applicability. One of these factors is the change in arm posture during the daily use of prostheses.

Spatial filtering for robust myoelectric control.

Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects.

Myoelectric control in neurorehabilitation.

A myoelectric signal, or electromyogram (EMG), is the electrical manifestation of a muscle contraction. Through advanced signal processing techniques, information on the neural control of muscles can be extracted from the EMG, and the state of the neuromuscular system can be inferred. Because of its easy accessibility and relatively high signal-to-noise ratio, EMG has been applied as a control signal in several neurorehabilitation devices and applications, such as multi-function prostheses and orthoses, rehabilitation robots, and functional electrical stimulation/therapy.

Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients.

A number of electroencephalographic (EEG) studies report on motor event-related desynchronization and synchronization (ERD/ERS) in the beta band, i.e. a decrease and increase of spectral amplitudes of central beta rhythms in the range from 13 to 35 Hz.

Online control of a brain-computer interface using phase synchronization.

Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes).

Quantification and visualization of event-related changes in oscillatory brain activity in the time-frequency domain.

In this chapter we review the traditional approach for ERD/ERS quantification and a more recent approach based on wavelet transform. In particular, we address the visualization of these phenomena and the validation of the results through statistical significance testing. Furthermore, we report on preprocessing using independent component analysis (ICA) and introduce a novel ERD/ERS maximization method.

A comparison of common spatial patterns with complex band power features in a four-class BCI experiment.

We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future.

Phase relationships between different subdural electrode recordings in man.

Almost all brain-computer interfaces (BCIs) ignore information related to the phase coupling between electroencephalogram (EEG) or electrocorticogram (ECoG) recordings from different electrodes. This paper investigates whether additional information can be found when calculating the amount of synchronization between two electrode channels by using a phase locking measurement called the phase locking value (PLV). Special emphasis is put on the beta band (around 20 Hz) as well as the gamma band (high frequencies up to 95 Hz), which can only be used when subdural electrode recordings are available.