On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features
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 ar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/13/2/391 |
| _version_ | 1797619843100311552 |
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| author | Nina Omejc Manca Peskar Aleksandar Miladinović Voyko Kavcic Sašo Džeroski Uros Marusic |
| author_facet | Nina Omejc Manca Peskar Aleksandar Miladinović Voyko Kavcic Sašo Džeroski Uros Marusic |
| author_sort | Nina Omejc |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:32:36Z |
| format | Article |
| id | doaj.art-8b8c18e5ab674aa68b424e00d54ed320 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-11T08:32:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-8b8c18e5ab674aa68b424e00d54ed3202023-11-16T21:40:37ZengMDPI AGLife2075-17292023-01-0113239110.3390/life13020391On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal FeaturesNina Omejc0Manca Peskar1Aleksandar Miladinović2Voyko Kavcic3Sašo Džeroski4Uros Marusic5Department of Knowledge Technologies, Jožef Stefan Institute, 1000 Ljubljana, SloveniaInstitute for Kinesiology Research, Science and Research Centre Koper, 6000 Koper, SloveniaDepartment of Ophthalmology, Institute for Maternal and Child Health-IRCCS Burlo Garofolo, 34137 Trieste, ItalyInstitute of Gerontology, Wayne State University, Detroit, MI 48202, USADepartment of Knowledge Technologies, Jožef Stefan Institute, 1000 Ljubljana, SloveniaInstitute for Kinesiology Research, Science and Research Centre Koper, 6000 Koper, SloveniaThe 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.https://www.mdpi.com/2075-1729/13/2/391agingEEGmachine learningclassificationBCIvisual oddball study |
| spellingShingle | Nina Omejc Manca Peskar Aleksandar Miladinović Voyko Kavcic Sašo Džeroski Uros Marusic On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features Life aging EEG machine learning classification BCI visual oddball study |
| title | On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features |
| title_full | On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features |
| title_fullStr | On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features |
| title_full_unstemmed | On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features |
| title_short | On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features |
| title_sort | on the influence of aging on classification performance in the visual eeg oddball paradigm using statistical and temporal features |
| topic | aging EEG machine learning classification BCI visual oddball study |
| url | https://www.mdpi.com/2075-1729/13/2/391 |
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