Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings

Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to be vital for the study of neural interactions in electrophysiological recordings. Conventional methods establish time-frequency coherence by smoothing the cross and power spectra using identical smoothing procedures. Smoothing entails a trade-off between time-frequency resolution and statistical consistency and is critical for detecting instantaneous coherence in single-trial data. Here, we propose a generalized method to estimate time-frequency coherency by using different smoothing procedures for the cross spectra versus power spectra. This novel method has an improved trade-off between time resolution and statistical consistency compared to conventional methods, as verified by two simulated data sets. The methods are then applied to single-trial surface encephalography recorded from human subjects for comparative purposes. Our approach extracted robust alpha- and gamma-band synchronization over the visual cortex that was not detected by conventional methods, demonstrating the efficacy of this method

Upregulation of cortico-cerebellar functional connectivity after motor learning

Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity

Active Bio-sensor System, Compatible with Arm Muscle Movement or Blinking Signals in BCI Application

This paper addresses a bionic active sensor system for the BCI application. Proposed system involves analog and digital parts. Two types of accurate sensors are used to pickup the blinking and muscle movement signals. A precision micro-power instrumentation amplifier with the adjustable gain, a sixth order low pass active filter with cutoff frequency 0.1 Hz, and a sixth order band pas filter with the bandwidth of 2-6 Hz are constructed to provide the clean blinking and arm muscle movement signals. TMS320C25 DSP processor is used for independent and unique command signals which are prepared for BCI application by a power amplifier and driver

Dynamic networks in the brain inferred from the analysis of neurophysiology data

The human brain is a hierarchical complex network − always active and evolvingdynamically. Its functional organization can be studied by investigating dynamicchanges in brain network interactions. In this thesis, I investigate dynamic changesin large-scale neuronal network to address the organization of the brain. In two complementaryconditions, resting-state and action-perception, functional connectivitypatterns in electrophysiological data are investigated to identify principles of networkdynamics.I first develop a time-frequency method to measure the temporal evolution of neuronalsynchronization. The second study uses this method to detect the dynamicsof neuronal synchronization in resting-state electroencephalography. I show sevenrobust networks with distinct topographic organizations over multiple frequencies,which build up and decay over much slower timescale, capturing the low-dimensionallinear subspaces in which resting-state activities unfold.The third study explores the role of neural dynamics in human motor control. I showthat corticomuscular coherence in the beta-band is replaced by alpha- and gammabandcoherence during a transition between sensorimotor states. Analyzing the phasespectra of the dual-band coherence reveals a↵erent and e↵erent corticospinal interactions,interpreting parallel streams of corticospinal processing in parsing predictionerrors and generating new predictions.Thereafter, I turn attention towards the identification of patterns of neuronal synchronizationin cortical source-level. The problems of volume conduction and thereference electrode ensure that this is a non-trivial endeavor. The fourth study exploresthe organization of the spatial patterns of neuronal communication in restingstateEEG source reconstruction. I show significant patterns of spatial functionalconnectivity using envelope correlation and phase synchronization, providing complementaryinsights into the timescales of neuronal dynamics. In final chapter, I showfurther approaches that can be built on those presently obtained to further elucidatethe orthogonal linear subspaces of synchronous networks at distinct frequencies.In sum, the modulation of synchronous networks at multiple frequencies suggests thebrain undergoes sequential metastable states: There arise spontaneously at rest andare reshaped by task conditions. The combined studies demonstrate a few principlesof spatiotemporally reorganization of brain networks synchronization, mirroring thecomplexity of brain activity