4 research outputs found
New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method
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 Parkinsonâs, Alzheimerâs, 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 patientsâ 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 âad
hocâ 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
A time frequency approach to blind deconvolution in multipath underwater channels
Blind deconvolution is studied in the underwater acoustic channel
context, by time-frequency (TF) processing. The acoustic propagation
environment is modelled by ray tracing and mathematically
described by a multipath propagation channel. Representation of
the received signal by means of a signal-dependent TF distribution
(radially Gaussian kernel distribution) allowed to visualize the
resolved replicas of the emitted signal, while signi cantly attenuating
the inherent interferences of classic quadratic TF distributions.
The source signal instantaneous frequency estimation was the starting
point for both source and channel estimation. Source signature
estimation was performed by either TF inversion, based on
the Wigner-Ville distribution of the received signal, or a subspace-
-based method. The channel estimate was obtained either via a
TF formulation of the conventional matched- lter, or via matched-
- ltering with the previously obtained source estimate. A shallow
water realistic scenario is considered, comprising a 135-m depth water
column and an acoustic source located at 90-m depth and 5.6-km
range from the receiver. For the corresponding noiseless simulated
data, the quality of the best estimates was 0.856 for the source
signal, and 0.9664 and 0.9996 for the amplitudes and time-delays
of the impulse response, respectively. Application of the proposed
deconvolution method to real data of the INTIMATE '96 sea trial
conduced to source and channel estimates with the quality of 0.530
and 0.843, respectively. TF processing has proved to remove the
typical ill-conditioning of single sensor deterministic deconvolution
techniques