| Title: | Statistically significant features improve binary and multiple motor imagery task predictions from EEGs |
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| Authors: | ID Degirmenci, Murside (Author)
ID Yuce, Yilmaz Kemal (Author)
ID Perc, Matjaž (Author)
ID Isler, Yalcin (Author) |
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| Files: |  RAZ_Degirmenci_Murside_2023.pdf (1,15 MB)
MD5: 9E01048282ACF1CE653EB4C30E4D6F22
 //10.3389/fnhum.2023.1223307
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
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| Abstract: | In recent studies, in the field of Brain-Computer Interface (BCI), researchers have
focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram
(EEG) signals provide the interaction and communication between the paralyzed
patients and the outside world for moving and controlling external devices
such as wheelchair and moving cursors. However, current approaches in the
Motor Imagery-BCI system design require. |
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| Keywords: | brain-computer interfaces, electroencephalogram, feature selection, machine learning, task classification |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 15.05.2023 |
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| Article acceptance date: | 23.06.2023 |
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| Publication date: | 11.06.2023 |
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| Publisher: | Frontiers Media |
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| Year of publishing: | 2023 |
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| Number of pages: | 16 str. |
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| Numbering: | Vol. 17, [article no.] ǂ1223307 |
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| PID: | 20.500.12556/DKUM-88197  |
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| UDC: | 53:004.85 |
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| ISSN on article: | 1662-5161 |
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| COBISS.SI-ID: | 158876931 |
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| DOI: | 10.3389/fnhum.2023.1223307 |
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| Publication date in DKUM: | 10.09.2024 |
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| Views: | 201 |
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| Downloads: | 14 |
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| Metadata: |    |
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| Categories: | Misc.
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