Investigating brain dynamics in industrial environment – integrating mobile EEG and kinect for cognitive state detection of a worker

Abstract

© Springer International Publishing AG 2017. In the present work we used wearable EEG sensor for recording brain activity during simulated assembly work, in replicated industrial environment. We investigated attention related modalities of P300 ERP component and engagement index (EI), which is extracted from signal power ratios of α, β and θ frequency bands. Simultaneously, we quantified the task unrelated movements, which are previously reported to be related to attention level, in an automated way employing kinect™ sensor. Reaction times were also recorded and investigated. We found that during the monotonous task, both the P300 amplitude and EI decreased as the time of the task progressed. On the other hand, the increase of the task unrelated movement quantity was observed, together with the increase in RTs. These findings lead to conclusion that the monotonous assembly work induces the decrease of attention and engagement of the workers as the task progresses, which is observable in both neural (EEG) and behavioral (RT and unrelated movements) signal modalities. Apart from observing how the attention-related modalities are changing over time, we investigated the functional relationship between the neural and behavioral modalities by using Pearson’s correlation. Since the Person’s correlation coefficients showed the functional relationship between the attention-related modalities, we proposed the creation of the multimodal implicit Human-Computer Interaction (HCI) system, which could acquire and process neural and behavioral data in real-time, with the aim of creating the system that could be aware of the operator’s mental states during the industrial work, consequently improving the operator’s well-being.