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Limited By:	Subject = Magnetoencephalography;

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Displaying Results 1 - 5 of 5 on page 1 of 1

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Blind source separation of multichannel neuromagnetic responses (2000)

Tang, Akaysha; Pearlmutter, Barak A.; Zibulevsky, Michael; Carter, Scott A.

Blind source separation of multichannel neuromagnetic responses (2000)

Tang, Akaysha; Pearlmutter, Barak A.; Zibulevsky, Michael; Carter, Scott A.

Abstract:

Magnetoencephalography (MEG) is a functional brain imaging technique with millisecond temporal resolution and millimeter spatial sensitivity. The high temporal resolution of MEG compared to fMRI and PET (milliseconds vs. seconds and tens of seconds) makes it ideal for measuring the precise time of neuronal responses, thereby o!ering a powerful tool for studying temporal dynamics. We applied blind-source separation (BSS) to continuous 122-channel human magnetoencephalographic data from two subjects and "ve tasks. We demonstrate that without using any domain-speci"c knowledge and without making the common assumption of single- or multiple-current dipole sources, BSS is capable of separating non-neuronal noise sources from neuronal responses and also of separating neuronal responses from di!erent sensory modalities, and from di!erent processing stages within a given modality

http://mural.maynoothuniversity.ie/5535/

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Independent Components of Magnetoencephalography: Localization (2002)

Tang, Akaysha; Pearlmutter, Barak A.; Malaszenko, Natalie A.; Phung, Dan B.; Reeb, Beth...

Independent Components of Magnetoencephalography: Localization (2002)

Tang, Akaysha; Pearlmutter, Barak A.; Malaszenko, Natalie A.; Phung, Dan B.; Reeb, Bethany C.

Abstract:

We applied second-order blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI’s ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole (ECD) modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios tha...

http://mural.maynoothuniversity.ie/5506/

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Independent Components of Magnetoencephalography: Single-Trial Response Onset Times (2002)

Tang, Akaysha; Pearlmutter, Barak A.; Malaszenko, Natalie A.; Phung, Dan B.

Independent Components of Magnetoencephalography: Single-Trial Response Onset Times (2002)

Tang, Akaysha; Pearlmutter, Barak A.; Malaszenko, Natalie A.; Phung, Dan B.

Abstract:

We recently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al., 2002, Neural Comput. 14, 1827–1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly f...

http://mural.maynoothuniversity.ie/5531/

Marked Mark

Localization of Independent Components from Magnetoencephalography (2000)

Tang, Akaysha; Phung, Dan B.; Pearlmutter, Barak A.; Christner, Robert

Localization of Independent Components from Magnetoencephalography (2000)

Tang, Akaysha; Phung, Dan B.; Pearlmutter, Barak A.; Christner, Robert

Abstract:

Blind source separation (BSS) decomposes a multidimensional time series into a set of sources, each with a one-dimensional time course and a xed spatial distribution. For EEG and MEG, the former corresponds to the simultaneously separated and temporally overlapping signals for continuous non-averaged data; the latter corresponds to the set of attenuations from the sources to the sensors. These sensor projection vectors give information on the spatial locations of the sources. Here we use standard Neuromag dipole-tting software to localize BSS-separated components of MEG data collected in several tasks in which visual, auditory, and somatosensory stimuli all play a role. We found that BSS-separated components with stimulusor motor-locked responses can be localized to physiological and anatomically meaningful locations within the brain.

http://mural.maynoothuniversity.ie/8122/

Marked Mark

Single-trial detection in EEG and MEG: Keeping it linear (2003)

Parra, Lucas C.; Alvino, Chris; Tang, Akaysha; Pearlmutter, Barak A.; Yeung, Nick; Osma...

Single-trial detection in EEG and MEG: Keeping it linear (2003)

Parra, Lucas C.; Alvino, Chris; Tang, Akaysha; Pearlmutter, Barak A.; Yeung, Nick; Osman, Allen; Sajda, Paul

Abstract:

Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on averaging over multiple trials to extract statistically relevant di7erences between two or more experimental conditions. We demonstrate that by linearly integrating information over multiple spatially distributed sensors within a prede9ned time window, one can discriminate conditions on a trial-by-trial basis with high accuracy. We restrict ourselves to a linear integration as it allows the computation of a spatial distribution of the discriminating source activity. In the present set of experiments the resulting source activity distributions correspond to functional neuroanatomy consistent with the task (e.g. contralateral sensory-motor cortex and anterior cingulate).

http://mural.maynoothuniversity.ie/5538/

Displaying Results 1 - 5 of 5 on page 1 of 1