Extracting effective features from high density nirs-based BCI for assessing numerical cognition

Near-infrared spectroscopy (NIRS)-based Brain-Computer Interface (BCI) was recently proposed to assess level of numerical cognition in subjects. However, existing feature extraction method was only proposed for low density 16 channels NIRS-based BCI. This study investigates the performance of a high density 348 channels NIRS-based BCI on 8 healthy subjects while they solve mental arithmetic problems with two difficulty levels and the rest condition. A novel method of extracting effective features from high density single-trial NIRS data is proposed using common average reference spatial filtering and single-trial baseline reference. The performance of the proposed feature extraction method is presented using 5×5-fold cross-validations on the single-trial NIRS data collected using mutual information-based feature selection and support vector machine classifier. The results yielded an overall average accuracy of 73% and 92% in classifying hard versus easy tasks and hard versus rest tasks respectively using the proposed method, compared to 46% and 62% respectively using existing method. The results demonstrated the effectiveness of using the proposed method in high density NIRS-based BCI for assessing numerical cognition.