3 research outputs found
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
Epilepsy is one of the common neurological disorders characterized by
recurrent and uncontrollable seizures, which seriously affect the life of
patients. In many cases, electroencephalograms signal can provide important
physiological information about the activity of the human brain which can be
used to diagnose epilepsy. However, visual inspection of a large number of
electroencephalogram signals is very time-consuming and can often lead to
inconsistencies in physicians' diagnoses. Quantification of abnormalities in
brain signals can indicate brain conditions and pathology so the
electroencephalogram (EEG) signal plays a key role in the diagnosis of
epilepsy. In this article, an attempt has been made to create a single
instruction for diagnosing epilepsy, which consists of two steps. In the first
step, a low-pass filter was used to preprocess the data and three separate
mid-pass filters for different frequency bands and a multilayer neural network
were designed. In the second step, the wavelet transform technique was used to
process data. In particular, this paper proposes a multilayer perceptron neural
network classifier for the diagnosis of epilepsy, that requires normal data and
epilepsy data for education, but this classifier can recognize normal
disorders, epilepsy, and even other disorders taught in educational examples.
Also, the value of using electroencephalogram signal has been evaluated in two
ways: using wavelet transform and non-using wavelet transform. Finally, the
evaluation results indicate a relatively uniform impact factor on the use or
non-use of wavelet transform on the improvement of epilepsy data functions, but
in the end, it was shown that the use of perceptron multilayer neural network
can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure
The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.</p
Developing artificial intelligence models for classification of brain disorder diseases based on statistical techniques
The Abstract is currently unavailable, due to the thesis being under Embargo