  The amount of power in different frequency bands of the electroencephalogram
(EEG) carries information about the behavioral state of a subject. Hence,
neurologists treating epileptic patients monitor the temporal evolution of the
different bands. We propose a covariance-based method to detect and
characterize epileptic seizures operating on the band-filtered EEG signal. The
algorithm is unsupervised, and performs a principal component analysis of
intra-cranial EEG recordings, detecting transient fluctuations of the power in
each frequency band. Its simplicity makes it suitable for online
implementation. Good sampling of the non-ictal periods is required, while no
demands are imposed on the amount of data during ictal activity. We tested the
method with 32 seizures registered in 5 patients. The area below the resulting
receiver-operating characteristic curves was 87\% for the detection of seizures
and 91\% for the detection of recruited electrodes. To identify the
behaviorally relevant correlates of the physiological signal, we identified
transient changes in the variance of each band that were correlated with the
degree of loss of consciousness, the latter assessed by the so-called
Consciousness Seizure Scale, summarizing the performance of the subject in a
number of behavioral tests requested during seizures. We concluded that those
crisis with maximal impairment of consciousness tended to exhibit an increase
of variance approximately 40 seconds after seizure onset, with predominant
power in the theta and alpha bands, and reduced delta and beta activity.
