HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks

TL;DR

This paper introduces HemCNN, a convolutional neural network that effectively decodes left/right hand force from fNIRS signals in real-time, outperforming standard methods and enabling practical mobile brain interfacing applications.

Contribution

The paper presents HemCNN, a novel deep learning architecture that decodes hand force from fNIRS data without baseline correction, suitable for real-time applications.

Findings

HemCNN outperforms standard decoding methods.

Decodes hand force at ~1 Hz in real-time.

Does not require baseline correction.

Abstract

We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way the hand, left or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed of $~1\,$Hz, outperforming standard methods. Since HemCNN does not require baseline correction and the convolution operation is invariant to time translations, our method can help to unlock fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain machine interfacing can benefit from this to develop real-world neuroscience and practical human neural interfacing based on BOLD-like signals for the evaluation, assistance and rehabilitation of force generation, such as fusion of fNIRS with EEG signals.