Efficient De-noising Performance of a Combined Algorithm of Translation Invariant (TI) Wavelets and Independent Component Analysis over TI Wavelets for Speech-Auditory Brainstem Responses
In this paper we have presented a research for de-noising the EEG collected Brainstem Speech Evoked Potentials data collected in an audiology lab in University of Ottawa, from 10 different human subjects. Here the de-noising techniques we have considered are Yule-Walker Multiband Filter, Cascaded Yu...
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| Published in | Procedia computer science Vol. 54; pp. 829 - 837 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2015.06.097 |
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| Abstract | In this paper we have presented a research for de-noising the EEG collected Brainstem Speech Evoked Potentials data collected in an audiology lab in University of Ottawa, from 10 different human subjects. Here the de-noising techniques we have considered are Yule-Walker Multiband Filter, Cascaded Yule-Walker-Comb Filter, Conventional Wavelet Transform estimation filters: Daubechies, Symlet, Coiflet Wavelet families, Translation Invariant (TI) Wavelet Transform estimation filter, FAST Independent Component Analysis (FASTICA) De-noising Technique, Combined algorithm of “Translation Invariant (TI) Wavelets and Independent Component Analysis” De-noising technique. The performance measures we have considered are Mean Square Error (MSE) and Signal-to-Noise-Ratio (SNR) values. Out of these techniques we found that cascading of Yule-Walker filter and Comb-Peak filter gave better De-noising performance than Yule-Walker Multiband Filter. Then conventional Wavelets performed far better than the cascaded filter, in those Daubechies family of wavelets worked better than all. Then FASTICA Algorithm worked near to the performance of Conventional Wavelets but far better than cascaded filter. Then we have utilized Translation Invariant (TI) wavelet algorithm which provided the excellent performance than above all. Then we have utilized combined Algorithm of “Translation Invariant (TI) Wavelets and Independent Component Analysis – CSTIICA” algorithm which found to be, it may perform better than TI wavelets algorithm. Ultimately TI and CSTIICA algorithms are found to be may be the best auditory artifact removal techniques and can be highly useful in auditory EEG data analysis to the best. |
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| AbstractList | In this paper we have presented a research for de-noising the EEG collected Brainstem Speech Evoked Potentials data collected in an audiology lab in University of Ottawa, from 10 different human subjects. Here the de-noising techniques we have considered are Yule-Walker Multiband Filter, Cascaded Yule-Walker-Comb Filter, Conventional Wavelet Transform estimation filters: Daubechies, Symlet, Coiflet Wavelet families, Translation Invariant (TI) Wavelet Transform estimation filter, FAST Independent Component Analysis (FASTICA) De-noising Technique, Combined algorithm of “Translation Invariant (TI) Wavelets and Independent Component Analysis” De-noising technique. The performance measures we have considered are Mean Square Error (MSE) and Signal-to-Noise-Ratio (SNR) values. Out of these techniques we found that cascading of Yule-Walker filter and Comb-Peak filter gave better De-noising performance than Yule-Walker Multiband Filter. Then conventional Wavelets performed far better than the cascaded filter, in those Daubechies family of wavelets worked better than all. Then FASTICA Algorithm worked near to the performance of Conventional Wavelets but far better than cascaded filter. Then we have utilized Translation Invariant (TI) wavelet algorithm which provided the excellent performance than above all. Then we have utilized combined Algorithm of “Translation Invariant (TI) Wavelets and Independent Component Analysis – CSTIICA” algorithm which found to be, it may perform better than TI wavelets algorithm. Ultimately TI and CSTIICA algorithms are found to be may be the best auditory artifact removal techniques and can be highly useful in auditory EEG data analysis to the best. |
| Author | Narayanam, Ranganadh |
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| References_xml | – reference: , vol. 1, no. 3, (2008). – reference: , pp. 125-150, (1995). – reference: S. Julier and J. K. Uhlmann, Unscented Filtering and Nonlinear Estimation, – reference: M. I. Bhatti, A. Pervaiz and M. H. Baig, EEG Signal Decomposition and Improved Spectral Analysis using Wavelet Transform, In – reference: , (2012). – reference: , vol. 92, no. 3, pp. 401-421, (2004). – reference: P. Senthil Kumar, R. Arumuganathan, K. Sivakumar and C. Vimal, Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel, – reference: K. L. Johnson, G.T. Nicol and N. Kraus, Brain Stem Response to Speech: A Biological Marker of Auditory Processing. Ear & Hearing, vol. 26, pp. 424-434, (2005). – reference: G. C. Galbraith, P.W. Arbagey and R. Branski, Intelligible Speech Encoded in the Human Brain Stem Frequency Following Response, vol. 6, pp. 2363-2367, (2004). – reference: , vol. 8, no. 9, pp. 87-92, (2008). – reference: , pp. 229-259, (2005). – reference: , vol. 2, pp. 1862-1864, (2008). – reference: C. S. Herrmann, M. Grigutsch and N. A. Busch, EEG Oscillations and Wavelet Analysis. Event-related Potentials: A Methods Handbook,. – reference: R. R. Coifman and D. L. Donoho, Translation Invariant Denoising, – reference: R. H. Dajani, D. Purcell, W. Wong, H. Kunov and T. W. Picton, Recording Human Evoked Potentials that Follow the Pitch Contour of a Natural Vowel, – reference: , pp. 1524-1529, (2007). – reference: P. Senthil Kumar, R. Arumuganathan, K. Sivakumar and C. Vimal, A Wavelet based Statistical Method for De-noising of Ocular Artifacts in. EEG Signals, – reference: , pp. 1614-1618, (2005). – reference: , (2014). – reference: , vol. 115, (2010). – reference: Ranganadh Narayanam, Brain-Activity-Filters: Efficient Performance of Translation-Invariant (TI) Wavelets Approach for Speech-Auditory Brainstem Responses of Human Subjects, – reference: Ranganadh Narayanam, An Efficient Peak Valley Detection based VAD Algorithm for Robust Detection of Speech Auditory Brainstem Responses, – reference: Ranganadh Narayanam, Robust Detection of Speech Auditory Brainstem Responses using Voice Activity Detection (VAD) Algorithms, – reference: , (2013). – reference: G. Inuso, F. La Foresta, N. Mammone and F. C. Morabito, Wavelet-ICA Methodology for Efficient Artifact Removal from Electroencephalographic Recordings, In the – reference: N. Russo, T. Nicol, G. Musacchia and N. Kraus, Brainstem Responses to Speech Syllables, – ident: 10.1016/j.procs.2015.06.097_bib0035 doi: 10.1097/00001756-199511270-00021 – ident: 10.1016/j.procs.2015.06.097_bib0015 doi: 10.1109/TBME.2005.851499 – ident: 10.1016/j.procs.2015.06.097_bib0040 doi: 10.1109/IEMBS.2001.1020587 – ident: 10.1016/j.procs.2015.06.097_bib0050 – ident: 10.1016/j.procs.2015.06.097_bib0045 – ident: 10.1016/j.procs.2015.06.097_bib0070 – ident: 10.1016/j.procs.2015.06.097_bib0010 doi: 10.5121/csit.2013.3636 – ident: 10.1016/j.procs.2015.06.097_bib0030 doi: 10.1016/j.clinph.2004.04.003 – ident: 10.1016/j.procs.2015.06.097_bib0055 doi: 10.1109/IJCNN.2007.4371184 – ident: 10.1016/j.procs.2015.06.097_bib0025 doi: 10.1097/01.aud.0000179687.71662.6e – ident: 10.1016/j.procs.2015.06.097_bib0060 doi: 10.1109/JPROC.2003.823141 – ident: 10.1016/j.procs.2015.06.097_bib0065 – ident: 10.1016/j.procs.2015.06.097_bib0020 – ident: 10.1016/j.procs.2015.06.097_bib0005 |
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| Title | Efficient De-noising Performance of a Combined Algorithm of Translation Invariant (TI) Wavelets and Independent Component Analysis over TI Wavelets for Speech-Auditory Brainstem Responses |
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