NDLI: EEG classification based on variance

Content Provider	IEEE Xplore Digital Library
Author	Machavarapu, S.C. Mukul, M.K. Kumar, D.
Copyright Year	2014
Description	Author affiliation: Dept. of Elec. & Comm. Eng., Birla Inst. of Technol., Ranchi, India (Machavarapu, S.C.; Mukul, M.K.; Kumar, D.)
Abstract	Brain computer interface (BCI) establishes a communication between human brain and an external assistive device via a computer. This communication system is mainly used for physically challenged individuals as well as it can be used to command robot to do task depending on mental thought. In this paper, we are working on the Electroencephalogram (EEG) signals based BCI system because the EEG signals having high temporal resolution and it is non-invasive. The objective of the paper is to enhance the classification accuracy for the movement imagery under the unsupervised learning. We have selected four preprocessing conventional filters namely Chebyshev filter, Butter worth filter, FIR bandpass filter and Elliptic filter to compare the performance in terms of classification accuracy. The filtered data is segmented and statistical parameter variance has been calculated in time domain. The difference of variance of the channels C3 and C4 has been taken as a feature. The Fisher linear discriminant analysis (FLDA) has been used to classify the feature matrix. The Elliptic filter achieves 84.3% classification accuracy for training data and 86.45% for testing data between the left and right hand movement imagination.
Starting Page	1
Ending Page	4
File Size	2611479
Page Count	4
File Format	PDF
e-ISBN	9781479949823
DOI	10.1109/ICGCCEE.2014.6922216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Band-pass filters Accuracy Finite impulse response filters BCI (Brain Computer Interface) EEG (ElectroEncephalogram) signals LC (Linear Classifier) Signal processing algorithms IIR filters FIR (Finite Impulse Response) Electroencephalography FLDA (Fisher Linear Discriminant Analysis) Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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