EEG analysis for cognitive failure detection in driving using neuro-evolutionary synergism

The paper proposes a solution to reduce accidents in driving by alarming specific cognitive failures to the drivers on occurrence of the failures. Three different types of cognitive failures that might occur due to lapse of i) visual alertness, ii) cognitive planning and iii) motor execution are studied, and suitable classifiers have been employed to classify these failures. Force exerted by the driver during turning or sudden tracking is also measured to detect his level of cognitive load during significant changes in the driving environment. Recurrent neural networks are introduced here as classifiers to decode cognitive tasks performed by the driver from his acquired EEG. For each recurrent neural net, we use a Lyapunov energy surface the minima of which denote the cognitive tasks during one of three cognitive activities mentioned above. Given the features of the measured EEG for a cognitive tasks, the recurrent net converges to one of several optima, describing a specific cognitive failure. Experimental results obtained by employing a driving simulator and an EEG system are encouraging.