The Tufts fNIRS to Mental Workload (fNIRS2MW) open-access dataset is a new dataset for building machine learning classifiers that can consume a short window (30 seconds) of multivariate fNIRS recordings and predict the mental workload intensity of the user during that window.

You can use this dataset for tasks like

• time series classification using sliding windows
• domain adaptation or domain generalization (how well does your classifier generalize to a new subject?)
• fairness of time series classifiers (does performance of your classifier vary by subject race or gender?)

Useful Links:

• Project Website (and data download links): https://tufts-hci-lab.github.io/code_and_datasets/fNIRS2MW.html
• Code for benchmarks: https://github.com/tufts-ml/fNIRS-mental-workload-classifiers
• DataSheet documentation: https://github.com/tufts-ml/fNIRS-mental-workload-classifiers/blob/main/Datasheet-Tufts-fNIRS2MW.pdf
• Academic Paper (published at NeurIPS Datasets & Benchmarks '21) https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/bd686fd640be98efaae0091fa301e613-Paper-round2.pdf

Motivation

We are interested in building brain computer interfaces (BCIs) that would help out everyday computer users working at a desktop or laptop. In our target future use case, a user would actively use a keyboard and mouse as usual, but also wear a non-intrusive headband sensor that would passively provide real-time measurements of brain activity to the computer. Based on moment-to-moment estimates of mental workload, the computer could adjust the interface to support the user.

Functional near-infrared spectroscopy (fNIRS) is a promising sensor technology for achieving this goal of "everyday BCI", compared to alternatives like EEG or fMRI. We have developed a prototype fNIRS probe mounted on a headband that we used to collect this dataset (see our paper for details).

Dataset Overview

For a complete dataset summary, see our public DataSheet PDF

For each participant (68 recommended; 87 total), the dataset contains the following records obtained during one 30-60 minute experimental session. Each subject contributes just over 21 minutes of fNIRS data from the desired n-back experimental conditions, with remaining time related to rest or instruction periods.

• fNIRS recordings

  • Multivariate (D=8) time-series representing brain activity throughout the session, recorded by a sensor probe placed on the forehead and secured via headband
  • All measurements are recorded at a regular sampling rate of 5.2 Hz.
  • At each timestep, we record 8 real-valued measurements, one for each combination of
    • 2 blood chemical concentration changes (oxygenated hemoglobin and deoxygenated hemoglobin)
    • 2 optical data types used for the measurement (intensity and phase)
    • 2 spatial locations on the forehead.
  • The units of each measurement are micro-moles of (oxy-/deoxy-)hemoglobin per liter of tissue.

• Activity labels

  • Annotations of the experimental task activity the subject performed throughout the session, including instruction, rest, and active experiment segments.
  • We label each segment of the active experiment as one of four possible n-back working memory intensity levels (0-back, 1-back, 2-back, or 3-back). Increased intensity levels are intended to induce an increased level of cognitive workload.
  • For all experiments reported in the paper, we focus on a binary task (0 vs 2 back)

• Demographics

  • The participant’s age, gender, race, handedness, and other attributes. This lets us measure and audit performance by subpopulation (e.g. how does the classifier perform on white subjects vs. black subjects).

Publications

The Tufts fNIRS Mental Workload Dataset & Benchmark for Brain-Computer Interfaces that Generalize
Zhe Huang, Liang Wang, Giles Blaney, Christopher Slaughter, Devon McKeon, Ziyu Zhou, Robert Jacob, and Michael C. Hughes
To appear in the Proceedings of Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks , 2021

Link to Paper PDF: https://openreview.net/pdf?id=QzNHE7QHhut