| Title: | On the influence of aging on classification performance in the visual EEG oddball paradigm using statistical and temporal features |
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| Authors: | ID Omejc, Nina (Author)
ID Peskar, Manca (Author)
ID Miladinović, Aleksandar (Author)
ID Kavcic, Voyko (Author)
ID Džeroski, Sašo (Author)
ID Marušič, Uroš (Author) |
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| Files: |  URL - Source URL, visit https://doi.org/10.3390/life13020391
 PDF - Presentation file, download (3,50 MB)
MD5: 9DE6C696FB26B8EF3D9629FD840C31E7
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  ZRS Koper - Science and Research Centre Koper
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| Abstract: | The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain–computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals’ performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice. |
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| Keywords: | aging, elderly, machine learning, visual oddball study, brain-computer interface |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Article acceptance date: | 28.01.2023 |
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| Publication date: | 31.01.2023 |
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| Year of publishing: | 2023 |
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| Number of pages: | 21 str. |
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| Numbering: | Vol. 13, iss 2, [article no.] 391 |
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| PID: | 20.500.12556/DiRROS-16172  |
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| UDC: | 612.67 |
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| ISSN on article: | 2075-1729 |
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| DOI: | 10.3390/life13020391 |
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| COBISS.SI-ID: | 140329987 |
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| Copyright: | © 2023 by the authors. |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 1. 2. 2023;
Soavtorji: Manca Peskar, Aleksandar Miladinović, Voyko Kavcic, Sašo Džeroski, Uros Marusic;
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| Publication date in DiRROS: | 01.02.2023 |
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| Views: | 526 |
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| Downloads: | 267 |
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