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; ten 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. A combination of ensemble empirical mode decomposition and independent component analysis method was used to solve the signal-noise frequency spectrum aliasing issues caused by the Mayer wave (0.1 Hz), then the signal means features were extracted as an input of linear discriminant analysis and support vector machine (SVM) classifier. The SVM classifier shows better classification results, and the average accuracies of four directions, up-down and left-right were 40.55%, 73.05%, and 70.7%, respectively, using oxygenated hemoglobin (8-21 s). This paper 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.

In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuouswave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC)and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (∆CBV, ∆COE, |L| and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.

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