Using chemosensory-induced EEG signals to identify patients with de novo Parkinson's disease

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Orkan Olcay - , Izmir Institute of Technology, Ege University (Autor:in)
  • Fatih Onay - , Izmir Institute of Technology, Bursa Technical University (Autor:in)
  • Guliz Akin Ozturk - , Dokuz Eylul University (Autor:in)
  • Adile Oniz - , Near East University (Autor:in)
  • Murat Ozgoren - , Near East University (Autor:in)
  • Thomas Hummel - , Klinik und Poliklinik für Hals-Nasen-Ohrenheilkunde, Universitätsklinikum Carl Gustav Carus Dresden (Autor:in)
  • Cagdas Guducu - , Dokuz Eylul University (Autor:in)


Objective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects’ behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100–700 and 200–800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects’ olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.


FachzeitschriftBiomedical signal processing and control
AusgabenummerPart B
Frühes Online-DatumSept. 2023
PublikationsstatusVeröffentlicht - Jan. 2024

Externe IDs

ORCID /0000-0001-9713-0183/work/146645747
WOS 001082128200001



  • Classification, Entropy, Feature extraction, Functional connectivity, Olfaction, Parkinson's disease