Taming fNIRS-based BCI Input for Better Calibration and Broader Use

Video

DOI

Brain-computer interfaces (BCI) are an emerging technology with many potential applications. Functional near-infrared spectroscopy (fNIRS) can provide a convenient and unobtrusive real-time input for BCI. fNIRS is especially promising as a signal that could be used to automatically classify a user’s current cognitive workload. However, the data needed to train such a classifier is currently not widely available, difficult to collect, and difficult to interpret due to noise and cross-subject variation. A further challenge is the need for significant user-specific calibration. To address these issues, we introduce a new dataset gathered from 15 subjects and a new multi-stage supervised machine learning pipeline. Our approach learns from both observed data and augmented data derived from multiple subjects in its early stages, and then fine-tunes predictions to an individual subject in its last stage. We show promising gains in accuracy in a standard “n-back” cognitive workload classification task compared to baselines that use only subject-specific data or only group-level data, even when our approach is given much less subject-specific data. Even though these experiments analyzed the data retrospectively, we carefully removed anything from our process that could not have been done in real-time, because our process is targeted at future real-time operations. This paper contributes a new dataset, a new multi-stage training pipeline, results showing significant improvement compared to alternative pipelines, and a discussion of the implications for user interface design. Our complete dataset and software are publicly available at https://tufts-hci-lab.github.io/code_and_datasets/. We hope these results make fNIRS-based interactive brain input easier for a wide range of future researchers and designers to explore.