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Analysis of Matching Pursuit Features of EEG Signal for Mental Tasks Classification

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dc.contributor.author Lwin, Zin Mar
dc.contributor.author Thaw, Mie Mie
dc.date.accessioned 2019-10-25T07:56:22Z
dc.date.available 2019-10-25T07:56:22Z
dc.date.issued 2015-02-05
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2344
dc.description.abstract Brain Computer Interface (BCI) Systems have developed for new way of communication between computer and human who are suffer from severe motor disabilities and difficult to communicate with their environment. BCI let them for communication by non muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which are recorded from the human‘s brain by mean of electrode. Electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. In translating human’s thought, it needs to classify acquired EEG signal accurately. Independent Component analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks in the recorded mental task EEG signal. For features extraction, the Time and Frequency features of non stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of mental tasks is performed by Multi_Class Support Vector Machine (SVM). en_US
dc.language.iso en_US en_US
dc.publisher Thirteenth International Conference On Computer Applications (ICCA 2015) en_US
dc.subject BCI en_US
dc.subject EEG en_US
dc.subject ICA en_US
dc.subject SVM en_US
dc.title Analysis of Matching Pursuit Features of EEG Signal for Mental Tasks Classification en_US
dc.type Article en_US

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