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On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and

Nina Omejc1,2, Manca Peskar3,4, Aleksandar Miladinović5

  • 1Department of Knowledge Technologies, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.

Life (Basel, Switzerland)
|February 25, 2023
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Summary

Brain-computer interfaces (BCIs) using electroencephalogram (EEG) data face challenges with age-related variability. Temporal EEG features show better classification performance and are less impacted by age than statistical event-related potential (ERP) features.

Keywords:
BCIEEGagingclassificationmachine learningvisual oddball study

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalogram (EEG) is a common input for brain-computer interfaces (BCIs).
  • Age-related variability in event-related potentials (ERPs) presents a challenge for EEG-based BCIs.
  • ERPs are frequently used as primary signal features in EEG BCI applications.

Purpose of the Study:

  • To investigate the impact of aging on EEG signal classification for BCIs.
  • To compare the effectiveness of temporal versus time-independent statistical ERP features for BCI classification across age groups.
  • To evaluate how different classifiers are affected by age-related differences in EEG features.

Main Methods:

  • A visual oddball study was conducted with 27 young and 43 older healthy adults.
  • 32-channel EEG data were collected while participants passively viewed frequent and rare stimuli.
  • Two types of datasets were created: one with amplitude and spectral features, and another with statistical ERP features.

Main Results:

  • Linear classifiers demonstrated the best performance among nine tested classifiers.
  • Classification performance varied significantly between the temporal and statistical ERP feature datasets.
  • Temporal features yielded higher maximum individual performance scores, lower variance, and were less affected by age-related differences.

Conclusions:

  • Feature extraction and selection are critical for robust BCI performance, especially considering age-related variability.
  • Temporal EEG features are more resilient to age-related performance degradation in BCIs compared to statistical ERP features.
  • The choice of classifier and its feature ranking mechanism influence the impact of aging on BCI performance.