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Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: To evaluate the functional use of sub-band modulations in somatosensory evoked potentials (SSEPs) to discriminate between the primary somatosensory (S1) and motor (M1) areas and contrast the states of consciousness., Methods: During routine intraoperative cortical mapping, SSEPs were recorded with electrocorticography (ECoG) grids from the sensorimotor cortex of eight patients in the anesthetized and awake states. We conducted a time-frequency analysis on the SSEP trace to extract the spectral modulations in each state and visualize their spatial topography., Results: We observed late gamma modulation (60-250 Hz) in all subjects approximately 50 ms after stimulation onset, extending up to 250 ms in each state. The late gamma activity enhancement was predominant in S1 in the awake state, where it discriminated S1 from M1 at a higher accuracy (92 %) than in the anesthetized state (accuracy = 70 %)., Conclusions: These results showed that sensorimotor mapping does not need to rely on only SSEP phase reversal. The long latency gamma modulation can serve as a biomarker for primary sensorimotor localization and monitor the level of consciousness in neurosurgical practice., Significance: While the intraoperative assessment of SSEP phase reversal with ECoG is widely employed to delineate the central sulcus, the median nerve stimulation-induced spatio-spectral patterns can distinctly localize it and distinguish between conscious states., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Electrocorticogram (ECoG) has been used as a reliable modality to control a brain machine interface (BMI). Recently, promising results of high-density ECoG have shown that non redundant information can be recorded with finer spatial resolution from the cortical surface. In this study, highdensity ECoG was recorded intraoperatively from two patients during awake brain surgery while performing instructed hand flexion and extension. Event related desynchronization (ERD) were found in the low frequency band (LFB: 8-32 Hz) band while event related synchronization (ERS) were found in the high frequency band (HFB: 60-200 Hz). The classification between hand flexion and extension was performed by using common spatial pattern (CSP) as a feature extraction technique and linear discriminant analysis (LDA) as a classifier. In order to compare the high-density ECoG and normal ECoG in terms of classifying between hand flexion and extension, we simulated a typical clinical ECoG (8 mm spacing) by averaging the neural activity of nearest four channels. The same classification methods were applied on the averaged recordings. In HFB, the classification error rate using simulated ECoG greatly increased and lagged the movement onset compared to the original highdensity ECoG. In LFB, the differences between them were not prominent. These results indicated that high-density ECoG is able to capture non-redundant task-related information from the motor cortex and potentially serves as a better modality to drive a neural prosthetic compared to typical clinical electrodes.

During awake brain surgeries, electrocorticogram (ECoG) was recorded using a high density electrode grid from the motor cortex of two subjects while they were asked to execute spontaneous hand extension and flexion. Firstly, we characterized the spatio-spectral patterns of high-density ECoG during the hand movements. In both subjects, we observed event related desynchronization (ERD) in low frequency band (LFB: 8-32 Hz) and event related synchronization (ERS) in high frequency band (HFB: 60-200 Hz) where HFB-ERS was more spatially localized and movement specific compared to LFB-ERD. In particular, improved spatial resolution of high density ECoG revealed HFB-ERS patterns with distinct timing in different anatomical regions. A few channels located anterior to the central sulcus were associated with HFB-ERS which started several hundred milliseconds prior to the movement onset. Several channels were associated with HFB-ERS which started close to the movement onset. Most importantly, only a small number of channels in the motor cortex regions exhibited long duration ERS which lasted while the subjects maintained their hand posture. A common spatial pattern (CSP) algorithm fused with linear discriminant analysis (LDA) was used to distinguish between hand extension and flexion at different time points based on subband features. ECoG data recorded from the channels located either anterior or posterior to the central sulcus were tested separately in classification. For both subjects, using channels located in motor area, HFB yielded almost 100% classification accuracy within 150-250 ms after the movement onset. The classification accuracies obtained from sensory areas were poor compared to motor areas and lagged the movement onset. These results suggest that spatial patterns of motor cortex captured with high-density ECoG in HFB can effectively drive a neural prosthetic to perform hand flexion and extension.

Background: The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages., Methodology/principal Findings: We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3-4 Hz) and high (48-200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week., Conclusions/significance: The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces.

It has been known for many years that the power of beta-band oscillatory activity in motor-related brain regions decreases during the preparation and execution of voluntary movements. However, it is not clear yet whether the amplitude of this desynchronization is modulated by any parameter of the motor task. Here, we examined whether the degree of uncertainty about the upcoming movement direction modulated beta-band desynchronization during motor preparation. To this end, we recorded whole-head neuromagnetic signals while human subjects performed an instructed-delay reaching task with one, two, or three possible target directions. We found that the reduction of power of beta-band activity (16-28 Hz) during motor preparation was scaled relative to directional uncertainty. Furthermore, we show that the change of beta-band power correlates with the change of latency of response associated with response uncertainty. Finally, we show that the main source of beta-band desynchronization was located in the peri-Rolandic region. The results establish directional uncertainty as an important determinant of beta-band power during motor preparation and indicate that neural activity in the sensorimotor cortex during motor preparation covaries with directional uncertainty.

We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces.

We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a brain computer interface task. The introduced method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. The algorithm implements an arbitrary time-frequency segmentation procedure by using a flexible local discriminant base algorithm for given multichannel brain activity recordings to extract subject specific ERD and ERS patterns. Extracted time-frequency features are processed by principal component analysis to reduce the feature set which is highly correlated due to volume conduction and the neighbor cortical regions. The reduced feature set is then fed to a linear discriminant analysis for classification. We give experimental results for 9 subjects to show the superior performance of the proposed method where the classification accuracy varied between 76.4% and 96.8% and the average classification accuracy was 84.9%