Titlebook: Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning; Saeed Mian Qaisar,Humaira Nisar,Abdulhamit Subasi

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https://doi.org/10.1007/978-1-4020-6189-9ization algorithm. The Selected feature set is then processed by the classifiers. The performance of k-Nearest Neighbor, Support Vector Machine and Artificial Neural Network is compared for the categorization of motor imagery tasks by processing the selected feature set. The suggested method secures

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Actors and Purposes in Comparative Education an example of application of anomaly detection in biomedical signal analysis, the problem of identifying the subtle deviations from the population norm based on the ECG is presented. The time-magnitude features derived from six leads of Signal Averaged ECG are used in the Isolation Forest anomaly (

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Mark Bray,Bob Adamson,Mark Masonzophrenia detection. These case studies use an architecture which combines deep learning with traditional ML techniques and compare their results. Using this hybrid model, an accuracy of 94.9% is obtained based on EEG signals obtained from epileptic and normal subjects, while an accuracy of 98% accu

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https://doi.org/10.1007/978-3-319-05594-7d to reduce variability and deliver biofeedback process according to the natural brain waves relations and principles, proposing DL models oriented to fill the actual vacuum of precision in current neurofeedback (NFB) devices and practice.