Extracting and selecting discriminative features from high density NIRS-based BCI for numerical cognition

Near-Infrared Spectroscopy (NIRS)-based Brain-Computer Interface (BCI) was recently studied for numerical cognition. This study presents a study using high density 348 channels NIRS-based BCI from 8 healthy subjects while solving mental arithmetic problems with two difficulty levels and the rest condition. The existing feature extraction and selection methods on the existing study were presented only for low density 16 channels NIRS-based BCI, and required the specification on the number of features to select to yield desirable performance. This paper presents a method of extracting discriminative features from high density single-trial NIRS data using common average reference spatial filtering and single-trial baseline reference, and a method of automatically selecting a set of discriminative and non-redundant features using the Mutual Information-based Rough Set Reduction (MIRSR) and Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithms. The performance of the proposed method is evaluated using 5x5-fold cross-validations on the single-trial NIRS data collected using the support vector machine classifier. The results yielded an overall average accuracy of 71.4% and 91.0% in classifying hard versus easy tasks and hard versus rest tasks respectively using the proposed method, compared to 46.1% and 62.2% respectively using existing methods. The results demonstrated the effectiveness of using the proposed feature extraction and selection method in high density NIRS-based BCI for assessing numerical cognition.