BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI)
| Main Authors: | Dalila Trad, Tarik Al-Ani, Mohamed Jemni |
|---|---|
| Format: | info Image eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2016
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| Subjects: | |
| Online Access: |
https://www.edusoft.ro/brain/index.php/brain/article/view/591/652 |
| ctrlnum |
1173504 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Dalila Trad</creator><creator>Tarik Al-Ani</creator><creator>Mohamed Jemni</creator><date>2016-06-15</date><description>One major challenge of our BCI system is to describe the signals EEG by a few relevant values called features i.e. step 3 in Fig (1). The success of the mental imagery classification depends on the choice of features used to characterize the raw EEG signals. These features can then be used in step 4 in order to classify the user’s mental state. Several approaches for feature extraction have been proposed in literature. </description><description>https://www.edusoft.ro/brain/index.php/brain/article/view/591/652</description><identifier>https://zenodo.org/record/1173504</identifier><identifier>10.5281/zenodo.1173504</identifier><identifier>oai:zenodo.org:1173504</identifier><language>eng</language><relation>doi:10.5281/zenodo.1173503</relation><relation>url:https://zenodo.org/communities/academiaedusoft</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/</rights><source>BRAIN. Broad Research in Artificial Intelligence and Neuroscience 7(2) 5-16</source><subject>Brain Computer Interface</subject><subject>motor imagery</subject><subject>Bandpower</subject><subject>Empirical Mode Decomposition</subject><subject>Hidden Markov Model</subject><subject>Support Vector Machines</subject><title>BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI)</title><type>Other:info:eu-repo/semantics/other</type><type>Image:Image</type><recordID>1173504</recordID></dc>
|
| language |
eng |
| format |
Other:info:eu-repo/semantics/other Other Image:Image Image Journal:eJournal Journal |
| author |
Dalila Trad Tarik Al-Ani Mohamed Jemni |
| title |
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI) |
| publishDate |
2016 |
| topic |
Brain Computer Interface motor imagery Bandpower Empirical Mode Decomposition Hidden Markov Model Support Vector Machines |
| url |
https://www.edusoft.ro/brain/index.php/brain/article/view/591/652 |
| contents |
One major challenge of our BCI system is to describe the signals EEG by a few relevant values called features i.e. step 3 in Fig (1). The success of the mental imagery classification depends on the choice of features used to characterize the raw EEG signals. These features can then be used in step 4 in order to classify the user’s mental state. Several approaches for feature extraction have been proposed in literature. |
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Universitas PGRI Palembang |
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Perpustakaan Universitas PGRI Palembang |
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587 |
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Marga Life in South Sumatra in the Past: Puyang Concept Sacrificed and Demythosized |
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17403 |
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KOTA PALEMBANG |
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SUMATERA SELATAN |
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IOS17403 |
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