Decoding olfactory stimuli in EEG data using nonlinear features: A pilot study

Background: While decoding visual and auditory stimuli using recorded EEG signals has enjoyed significant attention in the past decades, decoding olfactory sensory input from EEG data remains a novelty. Recent interest in the brains mechanisms of processing olfactory stimuli partly stems from the association of the olfactory system and its deficit with neurodegenerative diseases. New Methods: An olfactory stimulus decoder using features that represent nonlinear behavior content in the recorded EEG data has been introduced for classifying 4 olfactory stimuli in 5 healthy male subjects. Results: We show that by using nonlinear and chaotic features, a subject-specific classifier can be developed for identifying the odors that subjects perceive with an average accuracy of 96.71 % and 88.79 % in the eyes-open and eyes-closed conditions, respectively. We also employ our methodology in building cross-subject classifiers: once for identifying pleasant and unpleasant odors, and once for the classification of all four olfactory stimuli. The accuracy of our proposed methodology is 91.7 % and 82.1 % in the eyes-open and eyes-closed conditions, for the odor pleasantness classification. The accuracy of cross-subject classification of all odors is 64.3 % and 54.8 % for the eyes-open and eyes-closed conditions, respectively, which is well above chance level. Comparison with Existing Methods: Comparison with similar studies reveals that our proposed method outperforms other classification schemes in terms of accuracy. Conclusions: The results can help researchers design more accurate classifiers for the detection of perceived odors using EEG signals. These results can contribute to gaining more insight into the brains process of odor perception.