Supplemental Digital Content is available in the text.

Supplemental Digital Content is Available in the Text.

Objective Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space. Approach To investigate this, we designed a 3D center-out reaching task that was performed by 5 epileptic patients undergoing temporary placement of ECoG arrays. We used the ECoG signals within a hierarchical partial-least squares regression model to perform offline prediction of hand speed, velocity, and position. Main Results The hierarchical partial-least squares regression model enabled us to predict hand speed, velocity, and position during 3D reaching movements from held-out test sets with accuracies above chance in each patient with mean correlation coefficients between 0.31 and 0.80 for speed, 0.27 and 0.54 for velocity, and 0.22 and 0.57 for position. While beta band power changes were the most significant features within the model used to classify movement and rest, the local motor potential and high gamma band power changes, were the most important features in the prediction of kinematic parameters. Significance We believe that this study represents the first demonstration that truly three-dimensional movements can be predicted from ECoG recordings in human patients. Furthermore, this prediction underscores the potential to develop BCI systems with multiple degrees of freedom in human patients using ECoG.

Selective attention allows us to filter out irrelevant information in the environment and focus neural resources on information relevant to our current goals. Functional brain-imaging studies have identified networks of broadly distributed brain regions that are recruited during different attention processes; however, the dynamics by which these networks enable selection are not well understood. Here, we first used functional MRI to localize dorsal and ventral attention networks in human epileptic subjects undergoing seizure monitoring. We subsequently recorded cortical physiology using subdural electrocorticography during a spatialattention task to study network dynamics. Attention networks become selectively phase-modulated at low frequencies (δ, θ) during the same task epochs in which they are recruited in functional MRI. This mechanism may alter the excitability of task-relevant regions or their effective connectivity. Furthermore, different attention processes (holding vs. shifting attention) are associated with synchrony at different frequencies, which may minimize unnecessary cross-talk between separate neuronal processes.O ne of the hallmarks of effective behavior is the ability to flexibly attend to particular stimuli in the environment. Selective attention can be driven endogenously by one's current goals or by salient external stimuli. Human neuroimaging studies have identified two sets of fronto-parietal regions that are recruited during these two types of attention. A set of dorsal fronto-parietal regions (dorsal attention network or DAN) shows sustained activity during endogenous or goal-driven attention (1), and reorienting to unexpected targets transiently activates both the DAN and a second set of regions, the ventral attention network (VAN) (2). Although functional MRI (fMRI) has identified the brain regions that are involved in these attentional operations (3, 4), the slow nature of the hemodynamic response has severely limited the study of network dynamics at behaviorally relevant time scales (5). Here, we report results obtained by cortical surface (electrocorticography or ECoG) recordings in epilepsy patients undergoing clinical monitoring to identify seizure foci. Electrode locations for each subject were colocalized with functional brain networks, including the DAN and VAN identified in the same subjects using fMRI. This experimental paradigm allowed us to objectively link fast electrophysiological dynamics, during performance of an attention task, to well-studied functional brain networks.ECoG measures nonspiking, local field potential oscillations across a range of frequencies, which are thought to reflect fluctuations in local neuronal excitability (6, 7). Phase modulations of activity within a region and between regions may therefore affect, respectively, their ability to respond to inputs and to transfer information between one another (8, 9). In support of this theory, previous studies in both animals and humans have shown that either local or long-distance synchrony change in a tas...

High-gamma band (>60Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. In spite of discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60Hz. Based on non-uniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma band (60-500Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca's area, and superior temporal gyrus), these behavior- and location- dependent power changes evidenced nonuniform trends across the population. Taken together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.