Electrocorticography and the early maturation of high-frequency suppression within the default mode network.

OBJECTIVE The acquisition and refinement of cognitive and behavioral skills during development is associated with the maturation of various brain oscillatory activities. Most developmental investigations have identified distinct patterns of low-frequency electrophysiological activity that are characteristic of various behavioral milestones. In this investigation, the authors focused on the cross-sectional developmental properties of high-frequency spectral power from the brain's default mode network (DMN) during goal-directed behavior.

Multistep model for predicting upper-limb 3D isometric force application from pre-movement electrocorticographic features.

Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals.

Ownership of an artificial limb induced by electrical brain stimulation.

Replacing the function of a missing or paralyzed limb with a prosthetic device that acts and feels like one's own limb is a major goal in applied neuroscience. Recent studies in nonhuman primates have shown that motor control and sensory feedback can be achieved by connecting sensors in a robotic arm to electrodes implanted in the brain. However, it remains unknown whether electrical brain stimulation can be used to create a sense of ownership of an artificial limb.

Task-Specific Somatosensory Feedback via Cortical Stimulation in Humans.

Cortical stimulation through electrocorticographic (ECoG) electrodes is a potential method for providing sensory feedback in future prosthetic and rehabilitative applications. Here, we evaluate human subjects' ability to continuously modulate their motor behavior based on feedback from direct surface stimulation of the somatosensory cortex. Subjects wore a dataglove that measured their hand aperture position and received one of three stimuli over the hand sensory cortex based on their current hand position as compared to a target aperture position.

Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations.

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings.

Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans.

Objective: The purpose of this study is to determine the relationship between cortical electrophysiological (CE) signals recorded from the surface of the brain (subdural electrocorticography, or ECoG) and signals recorded extracranially from the subgaleal (SG) space.

Methods: We simultaneously recorded several hours of continuous ECoG and SG signals from 3 human pediatric subjects, and compared power spectra of signals between a differential SG montage and several differential ECoG montages to determine the nature of the transfer function between them.

Results: We demonstrate the presence of CE signals in the SG montage in the high-gamma range (HG, 70-110 Hz), and the transfer function between 70 and 110 Hz is best characterized as a linear function of frequency.

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface.

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75-200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance.

Distributed cortical adaptation during learning of a brain-computer interface task.

The majority of subjects who attempt to learn control of a brain-computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task.