  Near-infrared spectroscopy (NIRS) can measure neural activity through blood
oxygenation changes in the brain in a wearable form factor, enabling unique
applications for research in and outside the lab. NIRS has proven capable of
measuring cognitive states such as mental workload, often using machine
learning (ML) based brain-computer interfaces (BCIs). To date, NIRS research
has largely relied on probes with under ten to several hundred channels,
although recently a new class of wearable NIRS devices with thousands of
channels has emerged. This poses unique challenges for ML classification, as
NIRS is typically limited by few training trials which results in severely
under-determined estimation problems. So far, it is not well understood how
such high-resolution data is best leveraged in practical BCIs and whether
state-of-the-art (SotA) or better performance can be achieved. To address these
questions, we propose an ML strategy to classify working-memory load that
relies on spatio-temporal regularization and transfer learning from other
subjects in a combination that has not been used in previous NIRS BCIs. The
approach can be interpreted as an end-to-end generalized linear model and
allows for a high degree of interpretability using channel-level or cortical
imaging approaches. We show that using the proposed methodology, it is possible
to achieve SotA decoding performance with high-resolution NIRS data. We also
replicated several SotA approaches on our dataset of 43 participants wearing a
3198 dual-channel NIRS device while performing the n-Back task and show that
these existing methods struggle in the high-channel regime and are largely
outperformed by the proposed method. Our approach helps establish high-channel
NIRS devices as a viable platform for SotA BCI and opens new applications using
this class of headset while also enabling high-resolution model imaging and
interpretation.
