Brain-computer interface for olfaction: machine learning decoding odors from EEG
- Resource Type
- Conference
- Authors
- Ninenko, Ivan; Gritsenko, Georgy; Bukreev, Nikita; Ossadtchi, Alexey; Lebedev, Mikhail
- Source
- 2021 Third International Conference Neurotechnologies and Neurointerfaces (CNN) Neurotechnologies and Neurointerfaces (CNN), 2021 Third International Conference. :74-75 Sep, 2021
- Subject
- Computing and Processing
Signal Processing and Analysis
Olfactory
Machine learning
Electroencephalography
Brain-computer interfaces
Classification algorithms
Decoding
Neurotechnology
neurofeedback
BCI
olfaction
EEG
machine learning
- Language
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.