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

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.

Error related EEG potentials evoked by visuo-motor rotations.

Electroencephalographic (EEG) correlates of errors, known as error-related potentials (ErrPs), provide promising tools to investigate error processing in the brain and to detect and correct errors induced by brain-computer interfaces (BCIs). Visuo-motor rotation (VMR) is a well-known experimental paradigm to introduce visuo-motor errors that closely mimics directional errors induced by BCIs. However, investigations of ErrPs during VMR experiments are limited and reveals different ErrPs depending on task and synchronization.

Shifts in Estimated Preferred Directions During Simulated BMI Experiments With No Adaptation.

Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis.

Policy gradient optimization of controllers for natural dynamic mono-pedal gait.

We have previously suggested a biologically-inspired natural dynamic controller for biped locomotion, which applies torque pulses to the different joints at particular phases of an internal phase variable. The parameters of the controller, including the timing and magnitude of the torque pulses and the dynamics of the phase variable, can be kept constant in open loop or adapted to the environment in closed loop. Here we demonstrate the implementation of this approach to a mono-ped robot and the optimization of the controller parameters to enhance robustness via policy gradient.

Effects of model inaccuracies on reaching movements with intermittent control.

Background And Objectives: Human motor control (HMC) has been hypothesized to involve state estimation, prediction and feedback control to overcome noise, delays and disturbances. However, the nature of communication between these processes, and, in particular, whether it is continuous or intermittent, is still an open issue. Depending on the nature of communication, the resulting control is referred to as continuous control (CC) or intermittent control (IC).

Vib-bracelet: a passive absorber for attenuating forearm tremor.

Tremor is a rhythmic, involuntary, oscillatory movement of a limb produced by alternating contractions of reciprocally innervated muscles. More than 4% of the population over 40 years old suffer from tremor. There is no cure for most tremors, and while psychological therapy is sometimes helpful, tremors are usually treated with either medication or invasive surgery including thalamotomy and deep brain stimulation.

Attenuating Tremor Using Passive Devices.

Limb tremor is treated with either medication or surgery, both of which may have adverse effects. This paper presents two passive devices for tremor attenuation: One for attenuating pronation/supination tremor of the forearm using a dynamic vibration absorber, and the other for attenuating flexion/extension tremor of the hand using a rotational damper.

Identification of cell proliferation, immune response and cell migration as critical pathways in a prognostic signature for HER2+:ERα- breast cancer.

Background: Multi-gene prognostic signatures derived from primary tumor biopsies can guide clinicians in designing an appropriate course of treatment. Identifying genes and pathways most essential to a signature performance may facilitate clinical application, provide insights into cancer progression, and uncover potentially new therapeutic targets. We previously developed a 17-gene prognostic signature (HTICS) for HER2+:ERα- breast cancer patients, using genes that are differentially expressed in tumor initiating cells (TICs) versus non-TICs from MMTV-Her2/neu mammary tumors.

Distinct electroencephalographic responses to disturbances and distractors during continuous reaching movements.

Discrepancies between actual and appropriate motor commands, dubbed low-level errors, have been shown to elicit a P300 like component. P300 has been studied extensively in cognitive tasks using, in particular, the three-stimulus oddball paradigm. This paradigm revealed two sub-components, known as P3a and P3b, whose relative contributions depend on saliency and task-relevance, respectively.

Minimal feedback to a rhythm generator improves the robustness to slope variations of a compass biped.

In recent years there has been a growing interest in the field of dynamic walking and bio-inspired robots. However, while walking and running on a flat surface have been studied extensively, walking dynamically over terrains with varying slope remains a challenge. Previously we developed an open loop controller based on a central pattern generator (CPG).

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments.

Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking.

What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise.

Robust Satisficing Linear Regression: performance/robustness trade-off and consistency criterion.

Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known.

Cortical modulations increase in early sessions with brain-machine interface.

Background: During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs) extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood.

Coordinative structure of manipulative hand-movements facilitates their recognition.

Manipulative hand movements involve coordinated movements of the fingers to manipulate an object within the hand, and are classified as either simultaneous or sequential. Simultaneous hand movements are characterized by single coordinated patterns of digit movements, while sequential hand movements involve sequences of such patterns. Here, we investigate the extent of the coordination among 15 hand-joints during simultaneous hand movements, and demonstrate that it leads to a concise representation that facilitates movement recognition.

Temporal decoding by phase-locked loops: unique features of circuit-level implementations and their significance for vibrissal information processing.

Rhythmic active touch, such as whisking, evokes a periodic reference spike train along which the timing of a novel stimulus, induced, for example, when the whiskers hit an external object, can be interpreted. Previous work supports the hypothesis that the whisking-induced spike train entrains a neural implementation of a phase-locked loop (NPLL) in the vibrissal system. Here we extend this work and explore how the entrained NPLL decodes the delay of the novel, contact-induced stimulus and facilitates object localization.

Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface.

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood.