Brain-computer interface for olfaction: machine learning decoding odors from EEG

The final aim of our research is to develop a braincomputer interface (BCI) for olfaction. Our research program relies on modern olfactory displays and advanced processing of electroencephalography (EEG) and respiratory data in order to develop methods for robust olfactory BCI systems. Here we present the initial results from 17 subjects of our ongoing study. Applying k-nearest neighbors algorithm (k-NN) classification methods we achieve up to 79.9% accuracy for within subject classification of EEG signals for odor pairs. We propose some methods for further improvement of classification algorithm. EEG classification for different olfactory stimuli is the first step in this research, followed by the development of odor-imagery BCIs and odor-based neurofeedback.