User Training With Error Augmentation for sEMG-Based Gesture Classification.

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration; modified feedback, in which we applied a hidden augmentation of error to these probabilities; and no feedback.

Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography.

Modern neuroimaging modalities, particularly functional MRI (fMRI), can decode detailed human experiences. Thousands of viewed images can be identified or classified, and sentences can be reconstructed. Decoding paradigms often leverage encoding models that reduce the stimulus space into a smaller yet generalizable feature set.

Measuring neuronal activity with diffuse correlation spectroscopy: a theoretical investigation.

: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS.

Citizen Neuroscience: Brain-Computer Interface Researcher Perspectives on Do-It-Yourself Brain Research.

Devices that record from and stimulate the brain are currently available for consumer use. The increasing sophistication and resolution of these devices provide consumers with the opportunity to engage in do-it-yourself brain research and contribute to neuroscience knowledge. The rise of do-it-yourself (DIY) neuroscience may provide an enriched fund of neural data for researchers, but also raises difficult questions about data quality, standards, and the boundaries of scientific practice.

Advanced technologies for intuitive control and sensation of prosthetics.

The Department of Defense, Department of Veterans Affairs and National Institutes of Health have invested significantly in advancing prosthetic technologies over the past 25 years, with the overall intent to improve the function, participation and quality of life of Service Members, Veterans, and all United States Citizens living with limb loss. These investments have contributed to substantial advancements in the control and sensory perception of prosthetic devices over the past decade. While control of motorized prosthetic devices through the use of electromyography has been widely available since the 1980s, this technology is not intuitive.

DARPA investment in peripheral nerve interfaces for prosthetics, prescriptions, and plasticity.

Peripheral nerve interfaces have emerged as alternative solutions for a variety of therapeutic and performance improvement applications. The Defense Advanced Research Projects Agency (DARPA) has widely invested in these interfaces to provide motor control and sensory feedback to prosthetic limbs, identify non-pharmacological interventions to treat disease, and facilitate neuromodulation to accelerate learning or improve performance on cognitive, sensory, or motor tasks. In this commentary, we highlight some of the design considerations for optimizing peripheral nerve interfaces depending on the application space.

Brain-computer interface (BCI) researcher perspectives on neural data ownership and privacy.

Objective: Brain-computer interface (BCI) research and commercially available neural devices generate large amounts of neural data. These data have significant potential value to researchers and industry. Individuals from whose brains neural data derive may want to exert control over what happens to their neural data at study conclusion or as a result of using a consumer device.

A muscle-activity-dependent gain between motor cortex and EMG.

Whether one is delicately placing a contact lens on the surface of the eye or lifting a heavy weight from the floor, the motor system must produce a wide range of forces under different dynamical loads. How does the motor cortex, with neurons that have a limited activity range, function effectively under these widely varying conditions? In this study, we explored the interaction of activity in primary motor cortex (M1) and muscles (electromyograms, EMGs) of two male rhesus monkeys for wrist movements made during three tasks requiring different dynamical loads and forces. Despite traditionally providing adequate predictions in single tasks, in our experiments, a single linear model failed to account for the relation between M1 activity and EMG across conditions.

Cortical population activity within a preserved neural manifold underlies multiple motor behaviors.

Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task.

A cryptography-based approach for movement decoding.

Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases for which obtaining supervised data is difficult or impossible.

Representation of Stimulus Speed and Direction in Vibrissal-Sensitive Regions of the Trigeminal Nuclei: A Comparison of Single Unit and Population Responses.

The rat vibrissal (whisker) system is one of the oldest and most important models for the study of active tactile sensing and sensorimotor integration. It is well established that primary sensory neurons in the trigeminal ganglion respond to deflections of one and only one whisker, and that these neurons are strongly tuned for both the speed and direction of individual whisker deflections. During active whisking behavior, however, multiple whiskers will be deflected simultaneously.