A new method for detecting state changes in the EEG: exploratory application to sleep data

Abstract

A new statistical method is described for detecting state changes in the electroencephalogram (EEG), based on the ongoing relationships between electrode voltages at different scalp locations. An EEG sleep recording from one NREM-REM sleep cycle from a healthy subject was used for exploratory analysis. A dimensionless function defined at discrete times t i , u(t i ), was calculated by determining the log-likelihood of observing all scalp electrode voltages under the assumption that the data can be modeled by linear combinations of stationary relationships between derivations. The u(t i ), calculated by using independent component analysis, provided a sensitive, but non-specific measure of changes in the global pattern of the EEG. In stage 2, abrupt increases in u(t i ) corresponded to sleep spindles. In stages 3 and 4, low frequency (& 0.6 Hz) oscillations occurred in u(t i ) which may correspond to slow oscillations described in cellular recordings and the EEG of sleeping cats. In stage 4 sleep, additional irregular very low frequency (& 0.05±0.2 Hz) oscillations were observed in u(t i ) consistent with possible cyclic changes in cerebral blood flow or changes of vigilance and muscle tone. These preliminary results suggest that the new method can detect subtle changes in the overall pattern of the EEG without the necessity of making tenuous assumptions about stationarity.

Imaging brain dynamics using independent component analysis

“…Spatial stationarity of the component scalp maps, assumed in ICA, is compatible with the observation made in large numbers of functional imaging reports that performance of particular tasks increases blood flow within small ( cm ), discrete brain regions [41]. ERP sources reflecting task-related information processing are generally assumed to sum activity from spatially stationary generators, although stationarity might not apply to subcentimeter scales or to some spontaneous macroscopic EEG phenomena such as spreading depression or sleep spindles [42]. Our results to date suggest that most EEG oscillations, including alpha rhythms, can be better modeled as composed of temporally independent islands of coherent cortical activity, rather than as traveling waves [32].…”

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

“…In general, there is no reason to believe that cerebral and artifactual sources in the spontaneous EEG necessarily remain ®xed over time or occurrences. Examples of non-®xed sources may include spreading sleep spindles (McKeown et al, 1998). However, in our studies of averaged and unaveraged data from normal control subjects in these experiments (Jung et al, 1999), the relatively small numbers of obtained components showing stimulus-locked, response-locked, and non-phase-locked categories, each accounting for activity occurring across sets of 500 or more 1 s trials, suggests that the brain areas generating our data were primarily ®xed.…”

Independent Component Analysis of Ictal EEG in Medial Temporal Lobe Epilepsy

“…‫ס‬ {log 10 [P(theta) left /P(theta) right ]} where P(theta) left is the power of the 4-to 6-Hz rhythm and [log 10 P(theta) left ] is the mean of common logarithmic power of the 4-to 6-Hz wave from each channel of the left hemisphere. P(theta) right and [log 10 P(theta) right ] have the equivalent meanings from the right hemisphere.…”