EEG Analysis for Subjective Assessment of Motor Learning Skill in Driving Using Type‐2 Fuzzy Reasoning

This chapter attempts to measure the motor learning skill of subjects using 2 well‐known brain signals, called error‐related potential (ErrP) and N400. It aims to develop a hybrid brain–computer interfacing system with a motivation to detect motor execution failures. The proposed motor learning skill assessment includes three steps: feature extraction of the used electroencephalography (EEG) signals, detection of the EEG signals based on the selected features, and automated reasoning to measure the degree of learning skill of the driving learners in motor learning activity. The chapter provides a general overview of the problem along with the design of the fuzzy rules and the approach is undertaken. It concerns the inference generation procedures by the proposed type‐2 fuzzy reasoning to determine the degree of learning of the desired motor actuation. The chapter deals the experiments undertaken and results obtained thereof and provides performance analysis and statistical validation.