Classification of mental tasks using de-noised EEG signals
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. I...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2004
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| Online Access: | http://eprints.utm.my/2124/1/Daud2004_ClassificationOfMentalTasksUsing.pdf |
| _version_ | 1825909186624487424 |
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| author | Daud, Salwani Yunus, Jasmy |
| author_facet | Daud, Salwani Yunus, Jasmy |
| author_sort | Daud, Salwani |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T17:58:25Z |
| format | Conference or Workshop Item |
| id | utm.eprints-2124 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T17:58:25Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | utm.eprints-21242017-09-10T08:30:10Z http://eprints.utm.my/2124/ Classification of mental tasks using de-noised EEG signals Daud, Salwani Yunus, Jasmy TK Electrical engineering. Electronics Nuclear engineering 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. 2004 Conference or Workshop Item NonPeerReviewed application/pdf en http://eprints.utm.my/2124/1/Daud2004_ClassificationOfMentalTasksUsing.pdf Daud, Salwani and Yunus, Jasmy (2004) Classification of mental tasks using de-noised EEG signals. In: ICSP 2004. http://ieeexplore.org |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Daud, Salwani Yunus, Jasmy Classification of mental tasks using de-noised EEG signals |
| title | Classification of mental tasks using de-noised EEG signals |
| title_full | Classification of mental tasks using de-noised EEG signals |
| title_fullStr | Classification of mental tasks using de-noised EEG signals |
| title_full_unstemmed | Classification of mental tasks using de-noised EEG signals |
| title_short | Classification of mental tasks using de-noised EEG signals |
| title_sort | classification of mental tasks using de noised eeg signals |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/2124/1/Daud2004_ClassificationOfMentalTasksUsing.pdf |
| work_keys_str_mv | AT daudsalwani classificationofmentaltasksusingdenoisedeegsignals AT yunusjasmy classificationofmentaltasksusingdenoisedeegsignals |