Calculate the Quality Measures on Classification of Continuous EEG without Trial Structure EEG Dataset | Zendy

J. Mangesh | Zendy; G. Mukta | Zendy; Ashutosh Pankaj | Zendy

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Calculate the Quality Measures on Classification of Continuous EEG without Trial Structure EEG Dataset

Author(s) -

J. Mangesh,

G. Mukta,

Ashutosh Pankaj

Publication year - 2016

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/ijca2016911197

Subject(s) - electroencephalography , computer science , quality (philosophy) , artificial intelligence , pattern recognition (psychology) , speech recognition , neuroscience , psychology , philosophy , epistemology

Quality measure is very significant method for signal processing. Using this processes we can evaluate the EEG signal to see whether the data are noisy or not. The quality measure is performed on BCI competition dataset, this dataset is having 14 EEG signal, 0.05-200 Hz, 1000 Hz sampling rates, 2 classes of 7 subjects. The resultant signal quality is verified by using different quality measures parameters like PSNR, MSE, MAXERR, and L2RAT. So it is conclude that quality of EEG signal has been enriched by using of median filter. Hence it is proved that the recognition rate is increases. General Terms Calculate the quality measures (PSNR, MSE, MAXERR, and L2RAT) on classification of continuous EEG without trial structure EEG dataset

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