Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data

TL;DR

This study introduces a novel machine learning approach for decoding working-memory load from high-channel NIRS data, achieving state-of-the-art performance and demonstrating the potential of high-resolution wearable NIRS devices for advanced brain-computer interfaces.

Contribution

The paper presents a new ML strategy combining spatio-temporal regularization and transfer learning, optimized for high-channel NIRS data, outperforming existing methods in decoding working-memory load.

Findings

Achieved state-of-the-art decoding accuracy with high-channel NIRS data.

Existing methods underperform in high-channel regimes compared to the proposed approach.

High-channel NIRS devices are viable for advanced brain-computer interface applications.

Abstract

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.