Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders.

Objective: In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilized to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognized. Here, we seek to emphasize the extent of residual EMG contamination of EEG.

Fast and effective removal of contamination from scalp electrical recordings.

Objective: To present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings.

Method: We used spectral and temporal characteristics of different sources recorded during a typical scalp electrical recording in order to improve a fast and effective artefact removal approach. Our experiments show that correlation coefficient and spectral gradient of brain components differ from artefactual components.

Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures.

In this paper, we perform the first comparison of a large variety of effective connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength of interdependence between time series are compared: information theoretic measures, model-based multivariate measures in the time and frequency domains, and phase-based measures. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG.

Detecting synchrony in EEG: A comparative study of functional connectivity measures.

In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG.

Resting cranial and upper cervical muscle activity is increased in patients with migraine.

Objective: To compare comprehensive measures of scalp-recorded muscle activity in migraineurs and controls.

Method: We used whole-of-head high-density scalp electrical recordings, independent component analysis (ICA) and spectral slope of the derived components, to define muscle (electromyogram-containing) components. After projecting muscle components back to scalp, we quantified scalp spectral power in the frequency range, 52-98 Hz, reflecting muscle activation.

Evaluation of a minimum-norm based beamforming technique, sLORETA, for reducing tonic muscle contamination of EEG at sensor level.

Background: Cranial and cervical muscle activity (electromyogram, EMG) contaminates the surface electroencephalogram (EEG) from frequencies below 20 through to frequencies above 100Hz. It is not possible to have a reliable measure of cognitive tasks expressed in EEG at gamma-band frequencies until the muscle contamination is removed.

New Method: In the present work, we introduce a new approach of using a minimum-norm based beamforming technique (sLORETA) to reduce tonic muscle contamination at sensor level.

Electroencephalographic correlates of states of concentrative meditation.

Meditative techniques aim for and meditators report states of mental alertness and focus, concurrent with physical and emotional calm. We aimed to determine the electroencephalographic (EEG) correlates of five states of Buddhist concentrative meditation, particularly addressing a correlation with meditative level. We studied 12 meditators and 12 pair-matched meditation-naïve participants using high-resolution scalp-recorded EEG.

Constitutive spectral EEG peaks in the gamma range: suppressed by sleep, reduced by mental activity and resistant to sensory stimulation.

Objective: In a systematic study of gamma activity in neuro-psychiatric disease, we unexpectedly observed distinctive, apparently persistent, electroencephalogram (EEG) spectral peaks in the gamma range (25-100 Hz). Our objective, therefore, was to examine the incidence, distribution and some of the characteristics of these peaks.

Methods: High sample-rate, 128-channel, EEG was recorded in 603 volunteers (510 with neuropsychiatric disorders, 93 controls), whilst performing cognitive tasks, and converted to power spectra.