Singular Spectrum Analysis of Near-Infrared Spectroscopy Signal Classification for Mental Arithmetic and Rest State

Sachin Taran^*Rahul ThakurAnukul PandeyKanwardeep Singh Gahlot

• Delhi Technological University, Rohini, India

The Brain-Computer Interface (BCI) is the connection between the human brain and computers. BCI creates 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 Deoxy hemoglobin (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 resulted in a better-denoised signal and decomposition into different principal components for analysis of these states. Oxy and Deoxy patterns are temporary and unstable, so several features like power bandwidth, entropy, and complexity were extracted for classification. The reported accuracy in existing methods is 79.4% and 86.9% for the Antagonistic Single Trial Classification and Graph-NIRS methods, respectively. The present work's mean accuracy is 98.4% based on a set of selected features using Filtering Detrending (FD)-SSA, 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.