Tens of millions of people suffer from depression worldwide. It is urgent to explore an effective method for diagnosing depression. This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS). Sixty volunteers, including thirty patients with depression and thirty healthy controls, participated in the study. The 22-channel fNIRS device recorded the participants’ brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration changes in the positive, neutral and negative affective words’ stimulation. K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to recognize depressed patients from normal people, and 10-fold cross-validation was used to verify the classification result. Under the three single-mode features, the accuracy rates were 85.69%, 88.32% and 86.77%, corresponding to the positive condition, neutral condition and negative condition. Then, we used concatenation and linear combination for feature fusion. For the concatenation fusion method, the principal component analysis (PCA) was used to reduce the dimension. The result showed that feature fusion can relatively improve the recognition rate of people with depression, compared with single-model features. The optimal feature fusion method is to concatenate the neutral features and negative features, and the best accuracy reaches 94.45%. The study may provide a more accurate and convenient method for depression detection.
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