Classification of EEG Signals Recorded During Smelling of Lotus Flower and Valerian Olfaction

Brain response of information coming from the sense organs can be analyzed with different measurement methods. one of these existing methods is electroencephalography (eeg) which is the most preferred because this technique is not painful and easy to apply. the response of the human brain to olfaction has been analyzed in recent years. particularly, it has not been exactly proved how the human brain gives response to different odors because of the limited kind of odor usage and different kinds of proposed methods. the present study demonstrates the effect of lotus flower and valerian odors on eeg signals, which were recorded from 5 healthy subjects at the eyes open and eyes closed conditions. in order to show the effectiveness of the proposed method, we categorized the eeg trials into two classes between lotus flower and valerian odors. in order to represent the eeg trials, we extracted features by using fast fourier transform and skewness on eyes open condition and extracted features by using fast fourier transform on eyes closed condition. the extracted features were classified by k-nearest neighbor algorithm. the achieved results showed that 97.28% and 90.97% average classification accuracy rates were achieved on the eyes open and closed conditions, respectively. obtained results show that suggested feature extraction and classification method for these olfaction signs has great potential.