A Straightforward Approach to fNIRS Channel Selection for Distinguishing Mental States from Resting States: Effective in both Subject-Dependent and Subject-Independent Classification Models

Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a relatively novel tool that measures local hemodynamic changes, including oxygenated hemoglobin [Oxy-Hb], deoxygenated hemoglobin [Deoxy-Hb], and total hemoglobin [Tot-Hb]. Its safety, portability, non-invasiveness, and cost-effectiveness make it a preferred technique for designing Brain-Computer Interfaces (BCIs). This study aims to develop an accurate fNIRS-based BCI module for classifying mental tasks and the resting state. Materials and Methods: Rather than relying on conventional statistical features, our approach utilizes nonlinear indices derived from a 2D Poincaré plot. These measures are computationally efficient and capable of revealing the underlying dynamics of the system. Our primary innovation lies in the development of a novel feature and selection method. We assessed mental task recognition in both subject-dependent and subject-independent classification modes. Results: Our findings demonstrated a maximum accuracy of 93.75% for subject-specific style and 91.67% for subject-independent style. Conclusion: In summary, the simplicity and high performance of the proposed framework suggest promising future directions for designing online fNIRS-based BCI systems.