On the influence of aging on classification performance in the visual EEG oddball paradigm using statistical and temporal features

Nina Omejc (Avtor), Manca Peskar (Avtor), Aleksandar Miladinović (Avtor), Voyko Kavcic (Avtor), Sašo Džeroski (Avtor), Uroš Marušič (Avtor)

Povzetek

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.

Ključne besede

aging;elderly;machine learning;visual oddball study;brain-computer interface;

Podatki

Jezik:	Angleški jezik
Leto izida:	2023
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UP - Univerza na Primorskem
UDK:	612.67
COBISS:	140329987
ISSN:	2075-1729
Št. ogledov:	801
Št. prenosov:	410
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	staranje;starostniki;strojno učenje;gibanjeEEG;možgansko-računalniški vmesnik;
Komentar vira:	Nasl. z nasl. zaslona; Opis vira z dne 1. 2. 2023; Soavtorji: Manca Peskar, Aleksandar Miladinović, Voyko Kavcic, Sašo Džeroski, Uros Marusic;
Strani:	21 str.
Letnik:	ǂVol. ǂ13
Zvezek:	ǂiss ǂ2, [article no.] 391
Čas izdaje:	2023
DOI:	10.3390/life13020391
ID:	17899972