3 research outputs found
The Effective Brain Areas in Recognition of Dyslexia
Background: The brain has four lobes consist of frontal, parietal, occipital, and temporal. Most researchers have reported that the left occipitotemporal region of the brain, which is the combined region of the occipital and temporal lobes, is less active in children with dyslexia like Sklar, Glaburda, Ashkenazi and Leisman.Methods: There are different methods and tools to investigate how the brain works, such as magnetic resonance imaging (MRI), positron emission tomography (PET), magneto-encephalography (MEG) and electroencephalography (EEG). Among these, EEG determines the electrical activity of the brain with the electrodes placed on the special areas on the scalp. In this research, we processed the EEG signals of dyslexic children and healthy ones to determine what the areas of the brain are most likely to cause the disease.Results: For this purpose, we extracted 43 features, including relative spectral power (RSP) features, mean, standard deviation, skewness, kurtosis, Hjorth, and AR parameters. Then an SVM classifier is used to separate two classes. Finally, we show the particular brain activation pattern by calculating the correlation coefficients and co-occurrence matrices, which suggests the activation of the working memory region as an active area.Conclusion: By identifying the brain areas involved in reading activity, it has expected that psychologists and physicians will be able to design the therapeutic exercises to activate this part of the brain
Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems
Brain-Computer Interface (BCI) system provides a channel for the brain to
control external devices using electrical activities of the brain without using the
peripheral nervous system. These BCI systems are being used in various medical
applications, for example controlling a wheelchair and neuroprosthesis devices for
the disabled, thereby assisting them in activities of daily living. People suffering
from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked
in are unable to perform any body movements because of the damage of the
peripheral nervous system, but their cognitive function is still intact. BCIs operate
external devices by acquiring brain signals and converting them to control
commands to operate external devices. Motor-imagery (MI) based BCI systems, in
particular, are based on the sensory-motor rhythms which are generated by the
imagination of body limbs. These signals can be decoded as control commands in
BCI application. Electroencephalogram (EEG) is commonly used for BCI applications
because it is non-invasive. The main challenges of decoding the EEG signal are
because it is non-stationary and has a low spatial resolution. The common spatial
pattern algorithm is considered to be the most effective technique for
discrimination of spatial filter but is easily affected by the presence of outliers.
Therefore, a robust algorithm is required for extraction of discriminative features
from the motor imagery EEG signals.
This thesis mainly aims in developing robust spatial filtering criteria which
are effective for classification of MI movements. We have proposed two approaches
for the robust classification of MI movements. The first approach is for the
classification of multiclass MI movements based on the thinICA (Independent
Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method.
The observed results indicate that these approaches can be a step towards the
development of robust feature extraction for MI-based BCI system.
The main contribution of the thesis is the second criterion, which is based on
Alpha- Beta logarithmic-determinant divergence for the classification of two class
MI movements. A detailed study has been done by obtaining a link between the AB
log det divergence and CSP criterion. We propose a scaling parameter to enable a
similar way for selecting the respective filters like the CSP algorithm. Additionally,
the optimization of the gradient of AB log-det divergence for this application was
also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence)
algorithm is proposed for the discrimination of two class MI movements. The
robustness of this algorithm is tested with both the simulated and real data from BCI
competition dataset. Finally, the resulting performances of the proposed algorithms
have been favorably compared with other existing algorithms