Singular spectrum analysis of near-infrared spectroscopy signal classification for mental arithmetic and rest state

The brain–computer interface (BCI) is the connection between the human brain and computers, creating a bridge that mimics the human brain. The premise behind near-infrared spectroscopy (NIRS) is that increased oxygen consumption in the brain leads to increased blood flow due to nerve connections. NIRS is a non-invasive procedure; changes in oxyhemoglobin (Oxy-Hb) and deoxyhemoglobin (Deoxy-Hb) parameters can be easily utilized to detect brain hemodynamics. This study is based on the Oxy-Hb parameter to classify mental arithmetic and rest states of the brain using singular spectrum analysis (SSA). SSA results in a better-denoised signal and decomposition into different principal components for analysis of these states. Oxy- and Deoxy-Hb patterns are temporary and unstable, so features such as power bandwidth, entropy, and complexity were extracted for classification. The reported accuracy in existing methods is 79.4% for the antagonistic single-trial classification and 86.9% for graph NIRS methods. The present study’s mean accuracy was 98.4% based on a set of selected features using filtering detrending (FD)-SSA, thus reducing the cost of poor sorting. Finally, classification models were evaluated based on scores such as Matthew’s correlation coefficient, precision, F1-score, and recall, resulting in 0.889, 0.968, 0.966, and 0.963, respectively.