Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets.
Background: The impact of climate change on selected plant species from the hyper-arid landscape of United Arab Emirates (UAE) was assessed through modeling of their habitat suitability and distribution. Calotropis procera, Prosopis cineraria and Ziziphus spina-christi were used for this study. The specific objectives of this study were to identify the current and future (for 2050s and 2070s) suitable habitats distribution using MaxEnt, an Ecological Envelope Model. Methods: The adopted method consists of extraction of current and future bioclimatic variables together with their land use cover and elevation for the study area. MaxEnt species distribution model was then used to simulate the distribution of the selected species. The projections are simulated for the current date, the 2050s and 2070s using Community Climate System Model version 4 with representative concentration pathway RCP4.5. Results: The current distribution model of all three species evolved with a high suitable habitat towards the north eastern part of the country. For C. procera, an area of 1775 km2 is modeled under highly suitable habitat for the current year, while it is expected to increase for both 2050s and 2070s. The current high suitability of P. cinararia was around an area of 1335 km2 and the future projection revealed an increase of high suitability habitats. Z. spina-christi showed a potential area of 5083 km2 under high suitability and it might increase in the future. Conclusions: Precipitation of coldest quarter (BIO19) had the maximum contribution for all the three species under investigation.
the aim of this paper is to design and construct an electroencephalograph (EEG) based brain-controlled wheelchair to provide a communication bridge from the nervous system to the external technical device for people of determination or individuals suffering from partial or complete paralysis. EEG is a technique that reads the activity of the brain by capturing brain signals non-invasively using a special EEG headset. The signals acquired go through pre-processing, feature extraction and classification. This technique allows human thoughts alone to be converted to control the wheelchair. The commands used are moving to the right, left, forward, and backward and stop. The brain signals are acquired using the Emotiv Epoc headset. Discrete Wavelet Transform is used for feature extraction and Support Vector Machine (SVM) is used for classification.
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