GDL

Energy Computation for Bci Using Dct and Moving Average Window for Noise Smoothening

ISSN : 2230 - 9616
Journal from gdlhub / 2017-08-14 11:52:34
Oleh : Ch.Aparna1, J.V.R.Murthy2 and B.Raveendra Babu3, International Journal of Computer Science, Engineering and Applications
Dibuat : 2012-07-02, dengan 1 file

Keyword : BCI, EEG, Butterworth filters, Moving average spencer window, Discrete cosine transform (DCT), IBL.
Subjek : Energy Computation for Bci Using Dct and Moving Average Window for Noise Smoothening
Url : http://airccse.org/journal/ijcsea/papers/2112ijcsea02.pdf
Sumber pengambilan dokumen : Internet

Brain computer interface (BCI) is a fast evolving field of research enabling computers and machines to be


directly controlled by the human neural system. This enables people with muscular disability to directly


control machines using their thought process. The brain signals are recorded using Electro-


encephalography (EEG) and patterns extracted so that the BCI system should be able to classify various


patterns of brain signal accurately to perform different tasks. The raw EEG signal contains different kinds


of interference waveforms (artifacts) and noise. Thus raw signals cannot be directly used for classification,


the EEG signals has to undergo preprocessing, to remove artifacts and to extract the right attributes for


classification. In this paper it is proposed to extract the energies in the EEG signal and classify the signal


using Naïve Bayes and Instance based learners. The proposed method performs well for the two class


problem in the multiple datasets used..

Deskripsi Alternatif :




Brain computer interface (BCI) is a fast evolving field of research enabling computers and machines to be


directly controlled by the human neural system. This enables people with muscular disability to directly


control machines using their thought process. The brain signals are recorded using Electro-


encephalography (EEG) and patterns extracted so that the BCI system should be able to classify various


patterns of brain signal accurately to perform different tasks. The raw EEG signal contains different kinds


of interference waveforms (artifacts) and noise. Thus raw signals cannot be directly used for classification,


the EEG signals has to undergo preprocessing, to remove artifacts and to extract the right attributes for


classification. In this paper it is proposed to extract the energies in the EEG signal and classify the signal


using Naïve Bayes and Instance based learners. The proposed method performs well for the two class


problem in the multiple datasets used..

Beri Komentar ?#(0) | Bookmark

Properti	Nilai Properti
ID Publisher	gdlhub
Organisasi	International Journal of Computer Science, Engineering and Applications
Nama Kontak	Herti Yani, S.Kom
Alamat	Jln. Jenderal Sudirman
Kota	Jambi
Daerah	Jambi
Negara	Indonesia
Telepon	0741-35095
Fax	0741-35093
E-mail Administrator	elibrarystikom@gmail.com
E-mail CKO	elibrarystikom@gmail.com