Enhancing Subject-Independent Accuracy in fNIRS-based Brain-Computer Interfaces with Optimized Channel Selection

TL;DR

This paper introduces a new feature extraction and channel selection method that significantly improves subject-independent accuracy in fNIRS-based BCIs, reducing hardware complexity and achieving up to 95.98% accuracy with only two channels.

Contribution

It presents a novel Pearson correlation-based channel selection algorithm combined with optimized feature extraction for enhanced accuracy in subject-independent fNIRS BCIs.

Findings

Improved average accuracy by 28.09% over existing methods.

Achieved 95.98% peak accuracy with only two channels.

Demonstrated effectiveness on open-access dataset.

Abstract

Achieving high subject-independent accuracy in functional near-infrared spectroscopy (fNIRS)-based brain-computer interfaces (BCIs) remains a challenge, particularly when minimizing the number of channels. This study proposes a novel feature extraction scheme and a Pearson correlation-based channel selection algorithm to enhance classification accuracy while reducing hardware complexity. Using an open-access fNIRS dataset, our method improved average accuracy by 28.09% compared to existing approaches, achieving a peak subject-independent accuracy of 95.98% with only two channels. These results demonstrate the potential of our optimized feature extraction and channel selection methods for developing efficient, subject-independent fNIRS-based BCI systems.