HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
This work addresses the challenge of real-time fNIRS decoding for mobile brain imaging and brain-machine interfaces, with potential applications in neuroscience and rehabilitation, though it appears incremental as it builds on existing deep learning approaches for fNIRS.
The authors tackled the problem of decoding left/right hand force from fNIRS cortical signals during motor tasks by developing HemCNN, a convolutional neural network that achieved decoding at naturalistic speeds of ~1 Hz, outperforming standard methods.
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.