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Summary

This summary is machine-generated.

A new transformer-based method improves cross-subject mental workload classification using functional near-infrared spectroscopy (fNIRS) with fewer channels. This approach enhances accuracy for real-world neurorehabilitation and skill training applications.

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Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Mental state monitoring is crucial for neurorehabilitation and skill training.
Functional near-infrared spectroscopy (fNIRS) is a promising tool for this purpose.
Real-world applications require methods with fewer detection channels and robust cross-subject performance.

Purpose of the Study:

To propose a transformer-based method for cross-subject mental workload classification.
To achieve this using limited functional near-infrared spectroscopy (fNIRS) channels.
To enhance the efficiency and accuracy of mental state monitoring.

Main Methods:

fNIRS signals were divided into temporal patches and transformed into embeddings.
A transformer encoder was employed to capture long-range dependencies.
A multilayer perceptron (MLP) head processed the output for classification.

Main Results:

The proposed transformer-based method demonstrated superior cross-subject classification accuracy compared to previous approaches.
The method achieved relatively efficient computation.
Validation was performed on the open-access fNIRS2MW dataset.

Conclusions:

The transformer-based approach is effective for cross-subject mental workload classification using fNIRS.
This method offers a promising solution for real-world mental state monitoring.
The technique shows potential for advancing neurorehabilitation and skill training.