Application of rough set-based neuro-fuzzy system in NIRS-based BCI for assessing numerical cognition in classroom

Near-infrared spectroscopy (NIRS) studies have revealed that performing mental arithmetic tasks have associated event-related hemodynamic responses that are detectable. Thus NIRS-based Brain Computer Interface (BCI) has the potential for investigating how to best teach mathematics in a classroom setting. This paper presents a novel computational intelligent method of applying rough set-based neuro-fuzzy system (RNFS) in NIRS-based BCI for assessing numerical cognition. A study is performed on 20 healthy subjects to measure 32 channels of hemoglobin responses in performing three difficulty levels of mental arithmetic. The accuracy is then presented using 5×5-fold cross-validations on the data collected. The results of applying RNFS and its Mutual Information-based Rough Set Reduction (MIRSR) for feature selection is then compared against the Naïve Bayesian Parzen Window classifier and other MI-based feature selection algorithms. The results of applying RNFS yielded significantly better accuracy of 75.7% compared to the other methods, thus demonstrating the potential of RNFS in NIRS-based BCI for assessing numerical cognition.