Abstract:
As a promising non-invasive technique, functional near-infrared spectroscopy(fNIRS) can easily detect the hemodynamic responses of cortical brain activities. This paper i...Show MoreMetadata
Abstract:
As a promising non-invasive technique, functional near-infrared spectroscopy(fNIRS) can easily detect the hemodynamic responses of cortical brain activities. This paper investigated the multiclass classification of motor imagery(MI)based on fNIRS. 10 healthy individuals were recruited to move an object using their imagination. A multi-channel continuous-wave fNIRS equipment was applied to obtain the signals from the prefrontal cortex(PFC). The combined Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis(ICA) method was used to solve the signal-noise frequency spectrum aliasing issues caused by Mayer wave(0.1Hz), then the signal means(SM) features were extracted as an input of Support Vector Machine(SVM) classifier. The average accuracies of 4 directions, up-down and left-right were 40.55%, 73.05%, 70.7% respectively using Hbo2(8-21s). This study demonstrated that Brodmann area 4 was activated, which is consistent with previous conclusions. Furthermore, we found that the orbitofrontal cortex is also involved in MI and O2sat can also serve as a classified index.
Date of Conference: 13-16 November 2017
Date Added to IEEE Xplore: 18 December 2017
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