Machine Learning-based Classification of Cognitive Workload via In-ear EEG

In-ear EEG has recently emerged as a promising avenue to assess cognitive workload using minimally obtrusive sensors, thus promoting continuous and ubiquitous health monitoring. However, concerns around the quality and representativeness of data collected with this new technology need further investigations. In this work, we utilize a dataset related to a participant engaged in various mathematical tasks while wearing an in-ear EEG device. We apply signal processing techniques and feature extraction methodologies to analyze the EEG data. Feature vectors were constructed from each data segment, and subsequently used to train various machine learning classifiers to discriminate between different levels of cognitive workload. Moreover, we investigate the effectiveness of feature selection methods, to reduce the dimensionality of the feature space and potentially improve classifier performance. The results indicate that in-ear EEG, together with proper processing in terms of feature selection and machine learning, can adequately differentiate cognitive workload levels. Our findings proved the convenience of carrying on the investigation of this new kind of technology to promote a healthcare service closer to patients.