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Classification of mental tasks using de-noised EEG signals

Daud, Salwani and Yunus, Jasmy (2004) Classification of mental tasks using de-noised EEG signals. In: ICSP 2004.

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Abstract

The wsvelet based de-noising can bc eiiiployed ivi[li ihc combii.~ation of different kind of threshold parameters. tlwesliold operators. mother wavelets and timsliold rescaling methods. The central issue i.n wavelet bassd de-noising method is the selection of an appropriate ilircshold paraiiwters. If the tlmsliold is too small. the signal is still noisy but if it is too large. iiiiportant signal Cc;itiires might lost. This study will inyestigate the cfl'ectivcness of Tour Qpes of threshold parameters i.e. t lircshold selections based on Stein's Unbiased Risk Esliriiate (SURE,). Universal. Heuristic and Minimax. Auioregressiite Burg model with order sis is employed to cstmct rcle\*ani features froin the clean signals. These Icmrcs are classificd into five classes of mental tasks via :in iinificial neural nctwork. Tlie results show that the rate of correct classification varies with different tlucsliolds. From this study. it shows that the de-noised EEG signal tvitli heuristic threshold selection outperform lhe others. Soft tl~esholding procedure and sym8 as the mother U aixlcl arc adoptcd in this study.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:EEG;de-noised; wavelet shrinkage; threshold
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:2124
Deposited By: Dr Zaharuddin Mohamed
Deposited On:26 Mar 2007 01:30
Last Modified:01 Jun 2010 03:01

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