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Brain-Computer Interfaces enable direct communication between the brain andexternal systems, with functional Near-Infrared Spectroscopy emerging as aportable and non-invasive method for capturing cerebral hemodynamics. Thisstudy investigates the classification of rest and task states during arealistic, interactive tennis simulation using fNIRS signals and a range ofmachine learning approaches. We benchmarked traditional classifiers based onengineered features, Long Short-Term Memory networks on raw time-series data,and Convolutional Neural Networks applied to Gramian Angular Field-transformedimages. Ensemble models like Extra Trees and Gradient Boosting achievedaccuracies above 97 percent, while the ResNet-based CNN reached 95.0 percentaccuracy with a near-perfect AUC of 99.2 percent, outperforming both LSTM andEfficientNet architectures. A novel data augmentation strategy was employed toequalize trial durations while preserving physiological integrity. Featureimportance analyses revealed that both oxygenated and deoxygenated hemoglobinsignals, particularly slope and RMS metrics, were key contributors toclassification performance. These findings demonstrate the strong potential offNIRS-based BCIs for deployment in dynamic, real-world environments andunderscore the advantages of deep learning models in decoding complex neuralsignals.

Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming