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.

Context-dependent relationship in high-resolution micro-ECoG studies during finger movements.

OBJECTIVE: The activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks. METHODS: Three neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70-230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording. RESULTS: In all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3-6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05). CONCLUSIONS: HG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements.

OBJECTIVE: Human voluntary movements are a final product of complex interactions between multiple sensory, cognitive and motor areas of central nervous system. The objective was to investigate temporal sequence of activation of premotor (PM), primary motor (M1) and somatosensory (S1) areas during cued finger movements. METHODS: Electrocorticography (ECoG) was used to measure activation timing in human PM, S1, and M1 neurons in preparation for finger movements in 5 subjects with subdural grids for seizure localization. Cortical activation was determined by the onset of high gamma (HG) oscillation (70-150Hz). The three cortical regions were mapped anatomically using a common brain atlas and confirmed independently with direct electrical cortical stimulation, somatosensory evoked potentials and detection of HG response to tactile stimulation. Subjects were given visual cues to flex each finger or pinch the thumb and index finger. Movements were captured with a dataglove and time-locked with ECoG. A windowed covariance metric was used to identify the rising slope of HG power between two electrodes and compute time lag. Statistical constraints were applied to the time estimates to combat the noise. Rank sum testing was used to verify the sequential activation of cortical regions across 5 subjects. RESULTS: In all 5 subjects, HG activation in PM preceded S1 by an average of 53±13ms (P=0.03), PM preceded M1 by 180±40ms (P=0.001) and S1 activation preceded M1 by 136±40ms (P=0.04). CONCLUSIONS: Sequential HG activation of PM, S1 and M1 regions in preparation for movements is reported. Activity in S1 prior to any overt body movements supports the notion that these neurons may encode sensory information in anticipation of movements, i.e., an efference copy. Our analysis suggests that S1 modulation likely originates from PM. SIGNIFICANCE: First electrophysiological evidence of efference copy in humans.

Short-time windowed covariance: a metric for identifying non-stationary, event-related covariant cortical sites.

BACKGROUND: Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible. NEW METHOD: In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware. RESULTS: Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10mm spacing) and high-resolution (3mm spacing) electrode arrays. COMPARISON WITH EXISTING METHODS: Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations. CONCLUSIONS: STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.

Localization and classification of phonemes using high spatial resolution electrocorticography (ECoG) grids.

We present results of cortical activity during phoneme pronunciation, recorded using miniaturized electrocorticography grids with high spatial resolution. A patient implanted with the miniature grid was instructed to audibly pronounce one of four phonemes. For each phoneme, we observed distinct spatial correlation patterns at the 3mm electrode spacing. We applied a support vector machine classification scheme and, for the first time, were able to distinguish discrete phonemes with high accuracy. In addition, we found that sub-regions of our miniature array were specific for distinct pairs of phonemes, showing that cortical phoneme processing occurs at a higher resolution than previously though.

Three cases of feature correlation in an electrocorticographic BCI.

Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.