EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Abstract

This paper aims at detecting online cognitive failures in driving by decoding the electroencephalography (EEG) signals acquired during visual alertness, motor planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the preprocessed EEG signals obtained from his prefrontal and frontal lobes into two classes: alert and nonalert. Motor planning performed by the driver using the preprocessed parietal signals is classified into four classes: braking, acceleration, steering control, and no operation. Cognitive failures in motor planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the copilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time delay between the onset of motor imagination and the electromyogram response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration, and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/h, the lead time is more than 600 ms, which offer a safe distance of 10.66 m.