Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces. (October 2020) - Aydin, Eda Akman
Highlights: Subject-specific feature selection significantly improves the classification accuracy. Stepwise regression based sequential feature selection provides extremely high feature reduction rates besides the high classification accuracies. Because it determines the subsets considering the compatibility and consistency of the features with each other, stepwise regression based sequential feature selection provided a superior performance in terms of classification accuracy and feature reduction rate. Abstract: Background and Objective: Brain-computer interfaces (BCIs) enable people to control an external device by analyzing the brain's neural activity. Functional near-infrared spectroscopy (fNIRS), which is an emerging optical imaging technique, is frequently used in non-invasive BCIs. Determining the subject-specific features is an important concern in enhancing the classification accuracy as well as reducing the complexity of fNIRS based BCI systems. In this study, the effectiveness of subject-specific feature selection on classification accuracy of fNIRS signals is examined. Methods: In order to determine the subject-specific optimal feature subsets, stepwise regression analysis based on sequential feature selection (SWR-SFS) and ReliefF methods were employed. Feature selection is applied on time-domain features of fNIRS signals such as mean, slope, peak, skewness and kurtosis values of signals. Linear discriminant analysis, k nearest neighborhood and support vector machines are employed to evaluate the performance of the selected feature subsets. The proposed techniques are validated on benchmark motor imagery (MI) and mental arithmetic (MA) based fNIRS datasets collected from 29 healthy subjects. Results: Both SWR-SFS and reliefF feature selection methods have significantly improved the classification accuracy. However, the best results (88.67% (HbR) and 86.43% (HbO) for MA dataset and 77.01% (HbR) and 71.32% (HbO) for MI dataset) were achieved using SWR-SFS while feature selection provided extremely high feature reduction rates (89.50% (HbR) and 93.99% (HbO) for MA dataset and 94.04% (HbR) and 97.73% (HbO) for MI dataset). Conclusions: The results of the study indicate that employing feature selection improves both MA and MI-based fNIRS signals classification performance significantly.