Deep Learning for hybrid EEG-fNIRS Brain-Computer Interface: application to Motor Imagery Classification.

Deep Learning for hybrid EEG-fNIRS Brain-Computer Interface: application to Motor Imagery Classification.

J Neural Eng. 2018 Feb 15;:

Authors: Chiarelli AM, Croce P, Merla A, Zappasodi F

Abstract
OBJECTIVE: Brain-Computer Interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using Electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional Near Infrared Spectroscopy (fNIRS). A crucial step of BCI is brain state classication from recorded signal features. Deep Articial Neural Networks (DNNs) recently reached unprecedented complex classication outcomes. These performances were achieved through increased computational power, ecient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting signicant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art Deep Learning procedures. Approach. We performed a guided Left and Right Hand Motor Imagery task on 15 subjects with a xed classication response time of 1 second and overall experiment length of 10 minutes. Left vs. Right classication accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classiers.
MAIN RESULTS: At a group level we obtained signicant increase in performance when considering multi-modal recordings and DNN classier with synergistic effect. Signicance. BCI performances can be signicantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear Deep Learning classication procedures.

PMID: 29446352 [PubMed - as supplied by publisher]

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