https://trace.tennessee.edu/cgi/viewcontent.cgi?article=3528&context=utk_graddiss

Peak alpha frequency: an electroencephalographic measure of cognitive preparedness.

We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.

/pdf/classification-of-movement-intention-by-spatially-filtered-47p16v1p53.pdf

Classification of movement intention by spatially filtered electromagnetic inverse solutions

https://hal.archives-ouvertes.fr/hal-00523200/document

Group independent component analysis of resting state EEG in large normative samples

Brain oscillatory correlates of spatial navigation were investigated using blind source separation (BSS) and standardized low resolution electromagnetic tomography (sLORETA) analyses of 62-channel EEG recordings. Twenty-five participants were instructed to navigate to distinct landmark buildings in a previously learned virtual reality town environment. Data from periods of navigation between landmarks were subject to BSS analyses to obtain source components. Two of these cortical sources were found to exhibit significant spectral power differences during navigation with respect to a resting eyes open condition and were subject to source localization using sLORETA. These two sources were localized as a right parietal component with gamma activation and a right medial-temporal-parietal component with activation in theta and gamma bandwidths. The parietal gamma activity was thought to reflect visuospatial processing associated with the task. The medial-temporal-parietal activity was thought to be more specific to the navigational processing, representing the integration of ego-and allo-centric representations of space required for successful navigation, suggesting theta and gamma oscillations may have a role in integrating information from parietal and medial-temporal regions. Theta activity on this medial-temporal-parietal source was positively correlated with more efficient navigation performance. Results are discussed in light of the depth and proposed closed field structure of the hippocampus and potential implications for scalp EEG data. The findings of the present study suggest that appropriate BSS methods are ideally suited to minimizing the effects of volume conduction in noninvasive recordings, allowing more accurate exploration of deep brain processes.

/pdf/brain-oscillatory-activity-during-spatial-navigation-theta-3gvfd9e0a3.pdf

Brain oscillatory activity during spatial navigation: Theta and gamma activity link medial temporal and parietal regions

The aim of this work is to study the coherence profile (dependence) of robust eyes-closed resting EEG sources isolated by group blind source separation (gBSS). We employ a test-retest strategy using two large sample normative databases (N = 57 and 84). Using a BSS method in the complex Fourier domain, we show that we can rigourously study the out-of-phase dependence of the extracted components, albeit they are extracted so as to be in-phase independent (by BSS definition). Our focus on lagged communication between components effectively yields dependence measures unbiased by volume conduction effects, which is a major concern about the validity of any dependence measures issued by EEG measurements. We are able to show the organization of the extracted components in two networks. Within each network components oscillate coherently with multiple-frequency dynamics, whereas between networks they exchange information at non-random multiple time-lag rates.

/pdf/on-the-dependence-of-independent-group-eeg-sources-an-eeg-3jyehseeoo.pdf

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

Resampling of digitized electroencephalographic data allows changing the sampling rate with minimal distortion of the signal. Useful applications of the procedure include compatibility among diverse hardware and software and the customization of data analysis. The natural cubic spline interpolation procedure is introduced in a discursive fashion. A formal presentation is provided in the appendix.

/pdf/notes-on-eeg-resampling-by-natural-cubic-spline-47xhimsmnh.pdf

Leslie Sherlin Ba

Papers

Notes on EEG Resampling by Natural Cubic Spline Interpolation