With the vast expansion and increasing demand for the application domain of brain-computer interface (BCI) technology, stringent requirements have been imposed for the precision, stability, and convenience of the instruments and algorithms employed in implementing BCI technology. The adoption of BCI based on functional near-infrared spectroscopy (fNIRS) has successfully attained equilibrium among assorted factors such as acquisition modality, signal efficacy, deployment complexity, and resilience to interference, thereby making it a pivotal component of BCI research. In this investigation, portable fNIRS devices are used to achieve highly precise motion decision recognition within a single cycle, as indicated in the display of classification outcomes. The results of this study serve as a pivotal resource, offering invaluable tools and methodological references for the pragmatic application of BCI in motion decision recognition.

Neural activation data are acquired from a cohort of three individuals during motion execution, and an optimization strategy based on statistical parameter attributes is subsequently devised. This study aims to achieve instantaneous classification of distinct motion tasks encompassed within a single cycle. Primarily, the acquired measurement data are carefully refined using sophisticated algorithms such as cubic spline interpolation and fusion filtering techniques such as Savitzky Golay. These methodologies effectively identify and rectify any undesired effects caused by movement-related artifacts. Subsequently, the optimized statistical attributes pertaining to brain activation are input into a classification model, to categorize precisely, three distinct motion decisions: the grasping movement of the left and right hands, finger tapping of the left and right hands, and the resting state.

The accuracy (A), precision (P), recall (R), and F1-score (F₁) are employed as performance evaluation metrics for model training. In contrast to the direct T-test optimization, the model trained using the statistically optimized feature set displays enhanced R and elevated A. Notably, finger-tapping movements exhibit superior discernibility compared with grasping movements. Specifically, the research findings indicate that task groups situated farther from the baseline exhibit higher distinguishability, with tapping tasks demonstrating greater classification sensitivity towards right-handedness.

This work utilizes portable fNIRS devices to acquire neural activation data during various motor execution paradigms. By employing advanced statistical optimization algorithms, significant combinations of features were derived to effectively classify three distinct action modes: bilateral hand-grasping movements, bilateral hand-finger tapping, and the resting state. More specifically, this study develops an interactive fNIRS-based BCI analysis interface by employing the aforementioned analytical framework, enabling the real-time classification of online single-cycle tasks. By harnessing the widespread applicability of fNIRS technology in daily settings, this study offers a valuable methodological and practical toolkit for research and application of fNIRS-BCI in motion decision recognition.