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. In this study on two human subjects, we show that ownership of an artificial hand can be induced via the electrical stimulation of the hand section of the somatosensory (SI) cortex in synchrony with touches applied to a rubber hand. Importantly, the illusion was not elicited when the electrical stimulation was delivered asynchronously or to a portion of the SI cortex representing a body part other than the hand, suggesting that multisensory integration according to basic spatial and temporal congruence rules is the underlying mechanism of the illusion. These findings show that the brain is capable of integrating "natural" visual input and direct cortical-somatosensory stimulation to create the multisensory perception that an artificial limb belongs to one's own body. Thus, they serve as a proof of concept that electrical brain stimulation can be used to "bypass" the peripheral nervous system to induce multisensory illusions and ownership of artificial body parts, which has important implications for patients who lack peripheral sensory input due to spinal cord or nerve lesions.

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. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.

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. METHODS The authors contrasted regionally specific, time-evolving high gamma power (HGP) in the lateral DMN cortex between 3 young children (age range 3-6 years) and 3 adults by use of electrocorticography (ECoG) recordings over the left perisylvian cortex during a picture-naming task. RESULTS Across all participants, a nearly identical and consistent response suppression of HGP, which is a functional signature of the DMN, was observed during task performance recordings acquired from ECoG electrodes placed over the lateral DMN cortex. This finding provides evidence of relatively early maturation of the DMN. Furthermore, only HGP relative to evoked alpha and beta band power showed this level of consistency across all participants. CONCLUSIONS Regionally specific, task-evoked suppression of the high-frequency components of the cortical power spectrum is established early in brain development, and this response may reflect the early maturation of specific cognitive and/or computational mechanisms.

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. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings.

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. Using cortical stimulation feedback, subjects adjusted their hand aperture to move towards the target aperture region. One subject was able to achieve accuracies and R2 values well above chance (best performance: R2 = 0.93; accuracy = 0.76/1). Performance dropped during the catch trial (same stimulus independent of the position) to below chance levels, suggesting that the subject had been using the varied sensory feedback to modulate their motor behavior. To our knowledge, this study represents one of the first demonstrations of using direct cortical surface stimulation of the human sensory cortex to perform a motor task, and is a first step towards developing closed-loop human sensorimotor brain-computer interfaces.

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. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning.

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. We also test an alternative transfer function, i.e. a single pole filter, to test the hypothesis of frequency dependent attenuation in that range, but find this model to be inferior to the linear model. CONCLUSIONS: Our findings indicate that SG electrodes are capable of recording HG signals without frequency distortion compared with ECoG electrodes. SIGNIFICANCE: HG signals could be recorded minimally invasively from outside the skull, which could be important for clinical care or brain-computer interface applications.

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. These findings may have implications in the design and implementation of BCI control algorithms.