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

TL;DR

This study benchmarks various machine learning models, including deep learning, for classifying brain states using portable fNIRS signals during interactive gaming, demonstrating high accuracy and real-world applicability.

Contribution

It introduces a comprehensive benchmarking of classical and deep learning models on fNIRS data in a realistic gaming scenario, highlighting the effectiveness of CNNs and novel data augmentation techniques.

Findings

Ensemble models achieved over 97% accuracy.

ResNet CNN reached 95% accuracy with 99.2% AUC.

Deep learning models outperform traditional classifiers.

Abstract

Brain-Computer Interfaces enable direct communication between the brain and external systems, with functional Near-Infrared Spectroscopy emerging as a portable and non-invasive method for capturing cerebral hemodynamics. This study investigates the classification of rest and task states during a realistic, interactive tennis simulation using fNIRS signals and a range of machine learning approaches. We benchmarked traditional classifiers based on engineered features, Long Short-Term Memory networks on raw time-series data, and Convolutional Neural Networks applied to Gramian Angular Field-transformed images. Ensemble models like Extra Trees and Gradient Boosting achieved accuracies above 97 percent, while the ResNet-based CNN reached 95.0 percent accuracy with a near-perfect AUC of 99.2 percent, outperforming both LSTM and EfficientNet architectures. A novel data augmentation strategy was employed to equalize trial durations while preserving physiological integrity. Feature importance analyses revealed that both oxygenated and deoxygenated hemoglobin signals, particularly slope and RMS metrics, were key contributors to classification performance. These findings demonstrate the strong potential of fNIRS-based BCIs for deployment in dynamic, real-world environments and underscore the advantages of deep learning models in decoding complex neural signals.