Analysis and simulation of brain signal data by EEG signal processing technique using MATLAB Abstract
Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the br ain. Also called Electrocardiography (ECG), Electromyography (EMG), Magnetoencephalography (MEG) or Electronystagmography (ENG). MATLAB provides an interactive graphic user interface (GUI) allowing users to flexibly.
EEG is most often used to diagnose epilepsy, ep ilepsy, which causes obvious abnormalities in EEG readings. It is also used to diagnose sleep disorders, coma, encephalopathies, and brain death. EEG used to be a first-line method of diagnosis for tumors, stroke and other focal brain disorders, but this use has decreased with the ad vent of high-resolution anatomical imaging techniques such as MRI and CT. Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis, especiall y when millisecond-range temporal resolution (not possible with CT or MRI) is required. Interactively process their high-density EEG dataset and other brain signal data different techniques such as independent component compon ent analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. Problem Statement In addition, clinical neuropsychology uses neuropsychology uses neuropsychological assessment to assessment to precisely identify and track problems in mental functioning, usually after some sort of brain injury or neurological impairment. The specific causes of neurological problems vary, but can include genetic disorders, disorders, congenital abnormalities or abnormalities or disorders disorders,, infections infections,, lifestyle lifestyle or or environmental environmental health problems health problems including malnutrition malnutrition,, and brain injury, injury, spinal cord injury or injury or nerve injury. injury. The problem may start in another body systemt systemt hat interacts with the nervous system. For example, cerebrovascular disorders involve disorders involve brain injury due to problems with the blood vessels( vessels(cardiovascular system) system) supplying the brain; autoimmune autoimmune disorders disorders involve damage caused by the body's own immune system; system ; lysosomal storage diseases such diseases such as Niemann-Pick disease can disease can lead to neurological deterioration.
Introduction The human brain is one of o f the most complex systems in the universe. un iverse. Nowadays various technologies exist to record brain waves and electroencephalography (EEG) is one of them. This is one of the brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. Signal processing is the enabling technology technolog y for the generation, transformation, and interpretation of information. At different stages of time our brain reacts differently. These brain signals used for various purposes so that it is possible to study the functionalities of brain
properly by generating, transforming and interpreting the collected signal. This process is known as brain signal processing.
Main Tasks and Applications
1. Development of advanced methods for brain data analysis and processing. 2. Development of generalized component analysis (ICA, SCA, NMF, SPCA) and blind source separation (BSS). 3. Semi-blind extraction and analysis of ERP transient signals. 4. Statistical analysis of multi-electrode signals- especially analysis of brain signals from EEG/MEG recording. 5. Developments of neural network models and their engineering applications. 6. Auditory signal processing and speech enhancement.
