Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.

Electroencephalogram (EEG) registration as a direct measure of brain activity has unique potentials. It is one of the most reliable and predicative indicators when studying human cognition, evaluating a subject's health condition, or monitoring their mental state. Unfortunately, standard signal acquisition procedures limit the usability of EEG devices and narrow their application outside the lab. Emerging sensor technology allows gel-free EEG registration and wireless signal transmission. Thus, it enables quick and easy application of EEG devices by users themselves. Although a main requirement for the interpretation of an EEG is good signal quality, there is a lack of research on this topic in relation to new devices. In our work, we compared the signal quality of six very different EEG devices. On six consecutive days, 24 subjects wore each device for 60 min and completed tasks and games on the computer. The registered signals were evaluated in the time and frequency domains. In the time domain, we examined the percentage of artifact-contaminated EEG segments and the signal-to-noise ratios. In the frequency domain, we focused on the band power variation in relation to task demands. The results indicated that the signal quality of a mobile, gel-based EEG system could not be surpassed by that of a gel-free system. However, some of the mobile dry-electrode devices offered signals that were almost comparable and were very promising. This study provided a differentiated view of the signal quality of emerging mobile and gel-free EEG recording technology and allowed an assessment of the functionality of the new devices. Hence, it provided a crucial prerequisite for their general application, while simultaneously supporting their further development.

Background: Registration of brain activity has become increasingly popular and offers a way to identify the mental state of the user, prevent inappropriate workload, and control other devices by means of brain-computer interfaces. However, electroencephalography (EEG) is often related to user acceptance issues regarding the measuring technique. Meanwhile, emerging mobile EEG technology offers the possibility of gel-free signal acquisition and wireless signal transmission. Nonetheless, user experience research about the new devices is lacking. Objective: This study aimed to evaluate user experience aspects of emerging mobile EEG devices and, in particular, to investigate wearing comfort and issues related to emotional design. Methods: We considered 7 mobile EEG devices and compared them for their wearing comfort, type of electrodes, visual appearance, and subjects’ preference for daily use. A total of 24 subjects participated in our study and tested every device independently of the others. The devices were selected in a randomized order and worn on consecutive day sessions of 60-min duration. At the end of each session, subjects rated the devices by means of questionnaires. Results: Results indicated a highly significant change in maximal possible wearing duration among the EEG devices (χ26=40.2, n=24; P<.001). Regarding the visual perception of devices’ headset design, results indicated a significant change in the subjects’ ratings (χ26=78.7, n=24; P<.001). Results of the subjects’ ratings regarding the practicability of the devices indicated highly significant differences among the EEG devices (χ26=83.2, n=24; P<.001). Ranking order and posthoc tests offered more insight and indicated that pin electrodes had the lowest wearing comfort, in particular, when coupled with a rigid, heavy headset. Finally, multiple linear regression for each device separately revealed that users were not willing to accept less comfort for a more attractive headset design. Conclusions: The study offers a differentiated look at emerging mobile and gel-free EEG technology and the relation between user experience aspects and device preference. Our research could be seen as a precondition for the development of usable applications with wearables and contributes to consumer health informatics and health-enabling technologies. Furthermore, our results provided guidance for the technological development direction of new EEG devices related to the aspects of emotional design.

One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health.

In our modern society, planning and problem solving are crucial for handling a wide range of situations. Investigation of the experienced mental workload connected to planning, strategy learning, and working memory capacity is of particular interest for adjusting conditions according to the mental state of the individual. In our study, we examined 21 subjects during a planning and a working memory task. We applied the method of Dual Frequency Head Maps (DFHM) from the electroencephalogram for capturing mental workload objectively. We evaluated the DFHM-workload index and performance data during the learning and main phase of the planning task and linked the results to subjects’ working memory capacity. The DFHM-workload index indicated that subjects with higher working memory capacity experienced a gradual decrease in mental workload during strategy learning of the planning task. However, the effect of learning on mental workload disappeared during the main phase.