Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data

TL;DR

This paper introduces a new method for predicting cognitive workload from fNIRS data that works well across different subjects and sessions.

Contribution

The novel block-wise domain adaptation method improves generalization by treating intra-session blocks as different domains.

Findings

The proposed BWise-DA method outperforms three baseline models on three public workload datasets.

The method improves performance when used with baseline models through contrastive learning.

Visualizations confirm the model focuses on brain regions relevant to the tasks.

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-intrusive way to measure cortical hemodynamic activity. Predicting cognitive workload from fNIRS data has taken on a diffuse set of methods. To be applicable in real-world settings, models are needed, which can perform well across different sessions as well as different subjects. However, most existing works assume that training and testing data come from the same subjects and/or cannot generalize well across never-before-seen subjects. Additional challenges imposed by fNIRS data include not only the high variations in inter-subject fNIRS data but also the variations in intra-subject data collected across different blocks of sessions. To address these challenges, we propose an effective method, referred to as the block-wise domain adaptation (BWise-DA), which explicitly minimizes intra-session variance as well by viewing different blocks from the same subject and same session as different domains. We minimize the intra-class domain discrepancy and maximize the inter-class domain discrepancy accordingly. In addition, we propose an MLPMixer-based model for workload prediction. Experimental results demonstrate that the proposed model provides better performance compared to three different baseline models on three publicly-available workload datasets. Two of the datasets are collected from n-back tasks and one of them is from finger-tapping. Moreover, the experimental results show that our proposed contrastive learning method can also be leveraged to improve the performance of the baseline models. We also present a visualization study showing that the models are paying attention to the right regions in the brain, which are known to be involved in the respective tasks.

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