, N=1000 for all other cases). . A single wave travels caudally during forward swimming, while two waves traveling towards each other are observed while hovering. Grayscale is used to enhance amplitude information. The rostral end of the fin is at 0% on the fin length axis.Two errors appeared in J. Exp. Biol. 216,[823][824][825][826][827][828][829][830][831][832][833][834] During data processing, the wrong scaling factor was used when converting the fin amplitude from pixels on the video to linear displacement in millimetres [term y(x) in Eqn 1]. This error caused the angular displacement to be underestimated by a factor that varied between 2.9 and 3.6 (mean ± s.d. = 3.28 ± 0.27) for the different data sets.The error is found in Figs 2, 4, 9 and 10. In the right panels of Fig. 2, both panels of Fig. 4, Fig. 9C and the color bar in Fig. 10, the range of the y-axis is approximately three times smaller than it should have been.In the Results and Discussion, we comment on the direction of the change in amplitude at different swimming speeds. The error does not affect these comments because the amplitude at all swimming speeds was scaled by approximately the same factor.A second error was introduced when calculating the ratio of the fin length to the fin base length. The measurement of the fin base length was performed only on the first frame of every data set, instead of averaging the length across all the frames as was done for the fin length. This caused the ratio of fin length to fin base length to be larger in the hovering condition and smaller when swimming at 6 cm s −1. After the correction, the ratio is more constant than before. 3766

Brain machine interfaces (BMIs) that decode control signals from motor cortex have developed tremendously in the past decade, but virtually all rely exclusively on vision to provide feedback. There is now increasing interest in developing an afferent interface to replace natural somatosensation, much as the cochlear implant has done for the sense of hearing. Preliminary experiments toward a somatosensory neuroprosthesis have mostly addressed the sense of touch, but proprioception, the sense of limb position and movement, is also critical for the control of movement. However, proprioceptive areas of cortex lack the precise somatotopy of tactile areas. We showed previously that there is only a weak tendency for neighboring neurons in area 2 to signal similar directions of hand movement. Consequently, stimulation with the relatively large currents used in many studies is likely to activate a rather heterogeneous set of neurons. Here, we have compared the effect of single-electrode stimulation at sub-threshold levels to the effect of stimulating as many as seven electrodes in combination. We found a mean enhancement in the sensitivity to the stimulus (d′) of 0.17 for pairs compared to individual electrodes (an increase of roughly 30%), and an increase of 2.5 for groups of seven electrodes (260%). We propose that a proprioceptive interface made up of several hundred electrodes may yield safer, more effective sensation than a BMI using fewer electrodes and larger currents.

Objective It is quite remarkable that Brain Machine Interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach We have developed a system that uses two independent Weiner filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The classifier combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

, N=1000 for all other cases). . A single wave travels caudally during forward swimming, while two waves traveling towards each other are observed while hovering. Grayscale is used to enhance amplitude information. The rostral end of the fin is at 0% on the fin length axis.Two errors appeared in J. Exp. Biol. 216,[823][824][825][826][827][828][829][830][831][832][833][834] During data processing, the wrong scaling factor was used when converting the fin amplitude from pixels on the video to linear displacement in millimetres [term y(x) in Eqn 1]. This error caused the angular displacement to be underestimated by a factor that varied between 2.9 and 3.6 (mean ± s.d. = 3.28 ± 0.27) for the different data sets.The error is found in Figs 2, 4, 9 and 10. In the right panels of Fig. 2, both panels of Fig. 4, Fig. 9C and the color bar in Fig. 10, the range of the y-axis is approximately three times smaller than it should have been.In the Results and Discussion, we comment on the direction of the change in amplitude at different swimming speeds. The error does not affect these comments because the amplitude at all swimming speeds was scaled by approximately the same factor.A second error was introduced when calculating the ratio of the fin length to the fin base length. The measurement of the fin base length was performed only on the first frame of every data set, instead of averaging the length across all the frames as was done for the fin length. This caused the ratio of fin length to fin base length to be larger in the hovering condition and smaller when swimming at 6 cm s −1. After the correction, the ratio is more constant than before.

Previous studies using robotic devices that focus on the wrist/fingers following stroke provide an incomplete picture of movement dysfunction because they do not consider the abnormal joint torque coupling that occurs during progressive shoulder abduction loading in the paretic upper limb. This letter introduces a device designed to measure isometric flexion/extension forces generated by the fingers, wrist, and thumb during robot-mediated 3-D dynamic movements of the upper limb. Validation data collected from eight participants with chronic hemiparetic stroke are presented in this paper.