Publication: New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method
Loading...
Identifiers
Publication date
2011-10
Defense date
2011-10-06
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
Localizing the bioelectric phenomena originating from the cerebral cortex
and evoked by auditory and somatosensory stimuli are clear objectives to
both understand how the brain works and to recognize different pathologies.
Diseases such as Parkinson’s, Alzheimer’s, schizophrenia and epilepsy are intensively
studied to find a cure or accurate diagnosis.
Epilepsy is considered the disease with major prevalence within disorders
with neurological origin. The recurrent and sudden incidence of seizures can
lead to dangerous and possibly life-threatening situations. Since disturbance
of consciousness and sudden loss of motor control often occur without any
warning, the ability to predict epileptic seizures would reduce patients’ anxiety,
thus considerably improving quality of life and safety.
The common procedure for epilepsy seizure detection is based on brain
activity monitorization via electroencephalogram (EEG) data. This process
consumes a lot of time, especially in the case of long recordings, but the major
problem is the subjective nature of the analysis among specialists when
analyzing the same record. From this perspective, the identification of hidden
dynamical patterns is necessary because they could provide insight into
the underlying physiological mechanisms that occur in the brain.
Time-frequency distributions (TFDs) and adaptive methods have demonstrated
to be good alternatives in designing systems for detecting neurodegenerative
diseases. TFDs are appropriate transformations because they offer
the possibility of analyzing relatively long continuous segments of EEG data
even when the dynamics of the signal are rapidly changing. On the other
hand, most of the detection methods proposed in the literature assume a
clean EEG signal free of artifacts or noise, leaving the preprocessing problem
opened to any denoising algorithm.
In this thesis we have developed two proposals for EEG signal processing:
the first approach consists in electrooculogram (EOG) removal method based
on a combination of ICA and RLS algorithms which automatically cancels
the artifacts produced by eyes movement without the use of external “ad
hoc” electrode. This method, called ICA-RLS has been compared with other
techniques that are in the state of the art and has shown to be a good
alternative for artifacts rejection. The second approach is a novel method
in EEG features extraction called tracks extraction (LFE features). This
method is based on the TFDs and partial tracking. Our results in pattern
extractions related to epileptic seizures have shown that tracks extraction is
appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost.
Description
Keywords
Neuroengineering, Signal processing, Brain activity monitorization, Electroencephalogram, EEG, ICA-RLS