Removal of Eye-Blink Artifact from EEG Using LDA and Pre-trained RBF Neural Network
Electroencephalography (EEG) data are highly susceptible to noise and are frequently corrupted with eye-blink artifacts. Methods based on independent component analysis (ICA) and discrete wavelet transform (DWT) have been used as a standard for removal of such kinds of artifacts. However, these methods often require visual inspection and appropriate thresholding for identifying and removing artifactual components from the EEG signal. The proposed method presents a windowed method, where an LDA classifier is used for identification of artifacts and RBF neural network is used for correcting artifacts. In the present work, we propose a robust and automated method for identification and removal of artifacts from EEG signals, without the need for any visual inspection or threshold selection. Using test data contaminated with eye-blink artifacts, it is observed that our proposed method performs better in identifying and removing artifactual components from EEG data than the existing thresholding methods and does not require the application of ICA for identification of artifacts and can also be applied to any number of channels.
KeywordsArtifact removal Brain–computer interface (BCI) Electroencephalogram (EEG) Eye-blink Linear discriminant analysis (LDA) Radial basis function neural network (RBFNN)
This work was supported by the Ministry of Communications & Information Technology No. 13(13)/2014-CC&BT, Government of India.
Data were recorded under the project entitled “Analysis of Brain Waves and Development of Intelligent Model for Silent Speech Recognition” aimed at analyzing brainwaves to recognize silent speech from EEG at NIT Silchar. Adequate permission was obtained from the authority and informed consent was obtained from the subjects after explaining the details and possible outcomes of the study.
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