Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements

Wang, W; Degenhart, Alan D.; Collinger, Jennifer L.; Vinjamuri, Ramana; Sudre, Gustavo; Adelson, P. David; Holder, Deborah; Leuthardt, Eric C.; Moran, Daniel W.; Boninger, Michael L.; Schwartz, Andrew B.; Crammond, Donald J.; Tyler-Kabara, Elizabeth C.; Weber, Douglas J.

doi:10.1109/iembs.2009.5333704

Cited by 107 publications

(128 citation statements)

References 14 publications

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“…These changes in low and high frequency bands are in agreement with previous MEG (Waldert et al 2008) and ECoG studies (Leuthardt et al 2004;K. J. Miller et al 2007;Wang et al 2009). In addition, Wang et al (Wang, Sudre, et al 2010) demonstrated that high-gamma band activity captured by MEG showed directional modulation similar to what was observed previously using invasive recordings in humans (ECoG) (Leuthardt et al 2004) and non-human primates (local field potentials) (Heldman et al 2006) and has been used for BMI control.…”

Section: Meg For Bmi Technology Research and Developmentsupporting

confidence: 81%

“…Also, implantable electrode arrays typically cover only a small cortical area making accurate pre-surgical localization of the targeted implantation site critical. For example, intracortical microelectrode arrays typically cover only a small area of cortex (4×4mm 2 (Hochberg et al 2006)) and high density ECoG grids (Wang et al 2009) may cover an area only slightly larger (15×15mm 2 ). Another direct role for rtMEG is in pre-surgical training of patients who are scheduled to have electrodes implanted for BMI applications.…”

Section: Meg For Bmi Technology Research and Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

Foldes¹,

Wang²,

Collinger³

et al. 2011

Magnetoencephalography

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Section: Meg For Bmi Technology Research and Developmentsupporting

confidence: 81%

Section: Meg For Bmi Technology Research and Developmentmentioning

confidence: 99%

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

Foldes¹,

Wang²,

Collinger³

et al. 2011

Magnetoencephalography

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“…DFA has been shown to give optimal results when the sample sizes are on the order of 50 or less per condition (25). The manner in which DFA was applied in this work was closely related to linear discriminant analysis (LDA), which has recently been applied to classifying ECoG data based on spectral response (26). On high-dimensional data, a principal component analysis (PCA) decomposition gives a low-dimensional representation of the time series data, and it is common to use DFA in this lowdimensional space.…”

Section: Methodsmentioning

confidence: 99%

Fast-scale network dynamics in human cortex have specific spectral covariance patterns

Freudenburg¹,

Gaona²,

Sharma³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Whether measured by MRI or direct cortical physiology, infraslow rhythms have defined state invariant cortical networks. The time scales of this functional architecture, however, are unlikely to be able to accommodate the more rapid cortical dynamics necessary for an active cognitive task. Using invasively monitored epileptic patients as a research model, we tested the hypothesis that faster frequencies would spectrally bind regions of cortex as a transient mechanism to enable fast network interactions during the performance of a simple hear-and-repeat speech task. We term these short-lived spectrally covariant networks functional spectral networks (FSNs). We evaluated whether spectrally covariant regions of cortex, which were unique in their spectral signatures, provided a higher degree of task-related information than any single site showing more classic physiologic responses (i.e., single-site amplitude modulation). Taken together, our results showing that FSNs are a more sensitive measure of task-related brain activation and are better able to discern phonemic content strongly support the concept of spectrally encoded interactions in cortex. Moreover, these findings that specific linguistic information is represented in FSNs that have broad anatomic topographies support a more distributed model of cortical processing. T he brain's intrinsic functional architecture of correlated fluctuations in resting state metabolic and electrophysiologic activity has been well established (1, 2). This functional architecture has been shown to be present in the absence of a task, during all stages of sleep, and even under anesthesia (3). How anatomically distributed regions of cortex interact during the performance of a cognitive task is less understood. Due to slower time scales associated with the hemodynamic response of current neuroimaging techniques (4) and their electrophysiologic correlates (1), the more static networks are not adequate to accommodate the more rapid dynamics associated with many behavioral tasks. Given the limitations of the described time scales, we hypothesized that faster frequencies would "spectrally bind" regions of cortex as a transient mechanism to enable fast network interactions that accommodate the flexible use of neuronal resources. Beyond previously described notions that single higher-frequency synchronization enables neuronal interactions (5), we postulated that dynamic networks are represented by a multitude of spectral characteristics. These transient spectrally covariant networks, which we term functional spectral networks (FSNs), would enable a higher level of fidelity in the transmission of cortical-cortical information.Using invasively monitored epileptic patients as a research model, we tested this hypothesis in the setting of a simple hearand-repeat task. Given that human speech processing involves a widely distributed area located predominantly in perisylvian regions (6), this provided a robust model to evaluate networkderived behavior. We evaluated whether spectrally covariant...

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“…Even with these barriers investigators have achieved multiple degree of freedom control in non-human primate studies and in human studies. 20,21 It seems likely that higher degrees of motor control will be achieved in humans in the next 10 years and that a clinically viable product will be available. Creating a stable interface will likely still be the subject of investigation for electrodes that penetrate the brain.…”

Section: Direct Brain Interfacesmentioning

confidence: 99%

Technology for mobility in SCI 10 years from now

et al. 2012

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Objectives: To identify technological advances and that are likely to have a great impact on the quality of life and participation in individuals with spinal cord injury (SCI). Methods: In this paper we use the International Classification of Function to frame a discussion on how technology is likely to impact SCI in 10 years. In addition, we discuss the implication of technological advances on future research. Results/Conclusion: Although technology advances are exciting, a large challenge for the research community will be how to effectively apply and deploy this technology. Advances occurring in the next 10 years that reduce cost of technology may be more important to the population with SCI than brand new technologies. Social context is everything. As a research community we must advocate for better systems of care. Advocating now for better care will lead to a world in 2020 that is ready to adopt new technologies that are truly transformative.

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Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements

Cited by 107 publications

References 14 publications

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

Fast-scale network dynamics in human cortex have specific spectral covariance patterns

Technology for mobility in SCI 10 years from now

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