Towards Noise-Enhanced Augmented Cognition

Abstract

Abstract. Workload classification Augmented Cognition systems aim to detect when an operator is in a high or low workload state, and then to modify their work flow and operating environment based upon this knowledge. This paper reviews state-of-the-art electroencephalography (EEG) recorders for use in such systems and investigates the impact of EEG noise on an example system performance. It is found that adding up to 15 µV RMS of artificially generated noise still leaves EEG signals that have correlations in-line with the correlations found between conventional wet EEG electrodes and new dry electrodes. The workload classification system is found to be robust in the presence of small amounts of noise, and there is initial evidence of small stochastic resonance effects whereby better performance can actually be obtained in the noisy case compared to the traditional noise-less case.

Combined assessment of attentional reserve and cognitive‐motor effort under various levels of challenge with a dry EEG system

“…Such an approach could be complemented by other metrics derived from eye and heart activity to assess attentional processes and more generally cognitive-motor functions under various conditions of performance (He, Wang, Gao, & Chen, 2012;Murray & Russoniello, 2012;Murray & Hunfalvay, 2017;Wilson, 2002;Wilson & Russell, 2003). Third, mental workload assessment with dry EEG systems is advantageous since the setup of participants is fast and no cleaning of participants' hair is needed after data recording (Baek et al, 2013;Casson, 2013;Chi et al, 2012;Guger et al, 2012;Hairston et al, 2014;Mayaud et al, 2013;Zander et al, 2011). Once set, the wearing of the dry EEG cap (compared to the gel-based cap) did not affect per se the individual's mental workload, which is consistent with the fact that no participant complained about any discomfort-at least with the particular dry electrodes and configurations employed here.…”

“…This follows because gel-based systems require substantial preparation to allow conductive gel to reduce the impedance between the electrodes and the scalp. Moreover, participants are burdened with having to wash their hair after the EEG recording (Baek, Kim, Heo, Lim, & Park, 2013;Casson, 2013;Chi et al, 2012;Guger, Krausz, Allison, & Edlinger, 2012;Hairston et al, 2014;Mayaud et al, 2013;Zander et al, 2011). Thus, overall these mental workload studies that employed gel-based and dry EEG systems typically did not examine the direct relationship between the modulations of attentional reserve and cognitive-motor effort, which can collectively assess mental workload and more generally further inform the neural mechanisms underlying cognitive-motor performance.…”

Artificial Neural Network classification of operator workload with an assessment of time variation and noise-enhancement to increase performance

“…Further, in general electronics there is often a trade-off between power consumption and the effective noise present (Harrison and Charles, 2003 ; Xu et al, 2011 ; Cuadras et al, 2012 ). A number of high quality, highly miniaturized wearable EEG units have become commercially in recent years, but with battery lives typically in the 8 h range (Casson, 2013 ). This power performance still falls far short of pick up and use devices and substantial improvements in system power consumptions are still required to realize units that can be trusted to be re-usable session after session.…”

“…A preliminary investigation of using noise-enhancement in workload monitoring was presented in Casson ( 2013 ). However, this was restricted to the investigation of two subjects and the use of a single Artificial Neural Network based algorithm.…”

Towards out-of-the-lab EEG in uncontrolled environments: Feasibility study of dry EEG recordings during exercise bike riding

“…These do not require conductive gel and can still collect high quality EEG signals; see [5] for a review of the electrode technologies avaiable. Such dry electrode EEG systems are now starting to be commercially available from companies such as g.tec, Mindo, Quasar and others [6].…”