Novel Approach for EEG Signal Processing Based on Gradient Bilateral Support Vector Machine for Bioengineering Applications
Keywords:
Electroencephalogram (EEG), neural network, deep learning method, machine learning, bioengineeringAbstract
Electroencephalography (EEG) is one of the most effective methods in the area of bioengineering for comprehending how the brain works in humans. Analysing and understanding the electrical signals collected from the head depends heavily on EEG processing of signals. This non-invasive method offers insightful information on how the brain functions and has numerous uses in clinical diagnostics, neurology, and brain-computer interfaces. The electric possibilities produced by the brain's millions of cells working in unison are what the EEG analyses. An array of electrodes carefully positioned on the scalp can be used to record these electrical signals, often known as brain waves. The resulting EEG signal is a complicated time series that contains extensive data about the functioning of the brain, including details about cognitive processes, emotions, and other neurological conditions. High temporal accuracy of the raw EEG signal enables researchers and physicians to observe quick changes in brain activity. However, a number of noise sources, such as skeletal artefacts, eye motions, and influence from surroundings, also taint it. The EEG data must therefore be processed effectively in order to retrieve pertinent data. EEG signal processing has helped develop numerous fields in bioengineering applications. It has illuminated the systems underpinning perception, attention, memory, and sleep in neuroscience studies. EEG analysis helps in the diagnosis and follow-up of epilepsy, sleep problems, brain traumas, and neurodegenerative illnesses in medical settings. By allowing people with movement limitations to control external gadgets with their brain activity, brain-computer interfaces based on EEG have expanded the field of neurorehabilitation and assisted technology.
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