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

Abstract

Workload classification—the determination of whether a human operator is in a high or low workload state to allow their working environment to be optimized—is an emerging application of passive Brain-Computer Interface (BCI) systems. Practical systems must not only accurately detect the current workload state, but also have good temporal performance: requiring little time to set up and train the classifier, and ensuring that the reported performance level is consistent and predictable over time. This paper investigates the temporal performance of an Artificial Neural Network based classification system. For networks trained on little EEG data good classification accuracies (86%) are achieved over very short time frames, but substantial decreases in accuracy are found as the time gap between the network training and the actual use is increased. Noise-enhanced processing, where artificially generated noise is deliberately added to the testing signals, is investigated as a potential technique to mitigate this degradation without requiring the network to be re-trained using more data. Small stochastic resonance effects are demonstrated whereby the classification process gets better in the presence of more noise. The effect is small and does not eliminate the need for re-training, but it is consistent, and this is the first demonstration of such effects for non-evoked/free-running EEG signals suitable for passive BCI.

3D Printed Dry EEG Electrodes

“…However, this does not capture the electrode properties (noise, impedance and similar) and can be highly inaccurate. The work in [44] showed that correlation coefficients over 0.9 can be achieved even if there is more than 15 $\mathrm{sans-serif\mu }$Vrms of differences due to noise present between the two electrodes. The use of a head phantom and measurement of the electrode properties is therefore the preferred testing methodology.…”

Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls

“…Algorithms that can adapt the classification model on the fly could prevent problems due to generalization across time (e.g., Millán, 2004 ; Kindermans et al, 2012 ). Casson ( 2014 ) shows that adding artificial noise to EEG data helps to make classification performance more robust across time. Reuderink ( 2011 ) discusses generalization issues with respect to variability within and between users, and potential ways to make classification algorithms more robust which may help to reduce other generalization problems as well.…”

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

“…Taking into account the nature of the features, there are countless different examples of configurations, in terms of number of channels and frequencies used in the literature. The number of electrodes can vary from 64 [24], [36] to 6 [37], and even the bands considered vary from 2 (Theta and Alpha, [9]), to 7 (0-4 Hz, 4-7 Hz, 7-12 Hz, 12-30 Hz, 30-42 Hz, 42-84 Hz, 84-128 Hz [38]), up to considering all the single frequency bins that define the spectrum [39]. Several studies have shown that it does not necessarily take more than 5-10 electrodes to classify the workload [24].…”