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

• URL: http://arxiv.org/abs/2103.05338v1
• Date: Tue, 9 Mar 2021 10:32:53 GMT
• Title: HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
• Authors: Pablo Ortega and Aldo Faisal
• Abstract summary: We use a convolutional neural network architecture, the HemCNN, to solve the fNIRS left/right hand force decoding problem. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed, outperforming standard methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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