Minimizing inter-subject variability in fNIRS based Brain Computer Interfaces via multiple-kernel support vector learning

TL;DR

This paper introduces a multiple-kernel support vector machine approach to reduce inter-subject and inter-session variability in fNIRS-based BCI systems, enabling calibration-free and more robust neural signal classification.

Contribution

It proposes a novel multiple-kernel SVM framework that incorporates subject- and session-specific features to improve BCI classification without extensive calibration.

Findings

Classifiers maintain high accuracy despite variability

Reduced calibration time for new subjects

Effective handling of neural signal variability

Abstract

Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions before the BCI system can be used. Furthermore, the calibration session has to be repeated for each subject independently and before use of the BCI due to the inter-session variability. In this study, we present an algorithm in order to minimize the above-mentioned variabilities and to overcome the time-consuming and usually error-prone calibration time. Our algorithm is based on linear programming support-vector machines and their extensions to a multiple kernel learning framework. We tackle the inter-subject or -session variability in the feature spaces of the classifiers. This is done by incorporating each subject- or session-specific feature spaces into much richer feature spaces with a set of optimal decision boundaries. Each decision boundary represents the subject- or a session specific spatio-temporal variabilities of neural signals. Consequently, a single classifier with multiple feature spaces will generalize well to new unseen test patterns even without the calibration steps. We demonstrate that classifiers maintain good performances even under the presence of a large degree of BCI variability. The present study analyzes BCI variability related to oxy-hemoglobin neural signals measured using a functional near-infrared spectroscopy.