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

Saha, Anuradha and Konar, Amit and Nagar, Atulya K. (2017) EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers. IEEE Transactions on Emerging Topics in Computational Intelligence. ISSN 2471-285X (Accepted for Publication)

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Abstract

The paper aims at detecting on-line cognitive failures in driving by decoding the 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 pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed 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 co-pilot 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 EMG 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/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters.

Item Type: Article
Additional Information and Comments: (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords: EEG, Visual alertness, Motor-planning, Motor-execution and Type-2 fuzzy classifiers
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 11 Sep 2017 10:18
Last Modified: 11 Sep 2017 10:18
URI: http://hira.hope.ac.uk/id/eprint/2135

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