The Impact of Mspca Signal De-Noising In Real-Time Wireless Brain Computer Interface System

Jasmin Kevric, Abdulhamit Subasi


This paper presents the practical implementation of the motor imagery BCI system using MATLAB GUI. EEG signals were recorded using Mindwave Mobile Headset from one subject for two motor imagery tasks: right hand and left hand. The offline analysis showed decent performance of the combination between MSPCA de-noising of EEG signals and statistical features extracted from WPD sub-bands. The best classifier from the offline analysis was used in the online assessment to classify new motor imagery EEG signals. The overall results show that the desirable de-noising results are obtained if MSPCA is applied on a data matrix containing signals that belong to one particular class.


Brain Computer Interface (BCI), Mindwave Mobile, Multiscale PCA (MSPCA), Wavelet Packet Decomposition (WPD), Graphical User Interface (GUI), Electroencephalograph (EEG)

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Copyright (c) 2015 Jasmin Kevric

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

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This work is licensed under a Creative Commons Attribution 4.0 International License