Most long-term mobile EEG monitoring systems require professional application of the electrodes, which makes them inconvenient for everyday use. Additionally, many materials that facilitate EEG application, such as dry electrodes, may cause discomfort when worn for longer periods of time. To address these problems, we designed flex-printed EEG electrode grids (trEEGrid) and evaluated signal quality based on two pre-applied conductive materials. Self-applicable trEEGrid patches with a conductive solid hydrogel and a novel silicone-based dry material were used in a day-long (5–6 h) recording session, which included a 4 h continuous recording of impedance levels, as well as two auditory task recordings in the morning and afternoon. The signal-to-noise ratio (SNR) of the auditory evoked potentials (AEPs), AEP morphology, and impedance levels of the conductive materials were compared to evaluate overall signal quality, and further comparisons took place between the morning and afternoon sessions to evaluate signal deterioration over time. Comparable impedance values were observed for both silicone and hydrogel materials, but the silicone material exhibited a higher outlier rate, with impedance values over 200 kΩ. Over time, the impedance values increased for the silicone material and decreased for the hydrogel material. The morphology of the AEP was reproduced comparably well with both materials, with reasonable SNRs in both the morning and the afternoon. In conclusion, when combined with flex-printed electrode grids, silicone and hydrogel materials make it feasible to collect high-quality long-term EEG signals with high wearing comfort.

Electroencephalogram monitoring devices and online data repositories hold large amounts of data from individuals participating in research and medical studies without direct reference to personal identifiers. This paper explores what types of personal and health information have been detected and classified within task-free EEG data. Additionally, we investigate key characteristics of the collected resting-state and sleep data, in order to determine the privacy risks involved with openly available EEG data. We used Google Scholar, Web of Science and searched relevant journals to find studies which classified or detected the presence of various disorders and personal information in resting state and sleep EEG. Only English full-text peer-reviewed journal articles or conference papers about classifying the presence of medical disorders between individuals were included. A quality analysis carried out by 3 reviewers determined general paper quality based on specified evaluation criteria. In resting state EEG, various disorders including Autism Spectrum Disorder, Parkinson's disease, and alcohol use disorder have been classified with high classification accuracy, often requiring only 5 mins of data or less. Sleep EEG tends to hold classifiable information about sleep disorders such as sleep apnea, insomnia, and REM sleep disorder, but usually involve longer recordings or data from multiple sleep stages. Many classification methods are still developing but even today, access to a person's EEG can reveal sensitive personal health information. With an increasing ability of machine learning methods to re-identify individuals from their EEG data, this review demonstrates the importance of anonymization, and the development of improved tools for keeping study participants and medical EEG users' privacy safe.

Long-term EEG measurements (over eight hours) can allow a deeper understanding of everyday life brain functioning. However, most EEG systems can only be used for short periods in controlled settings, due to limitations in comfort, signal quality of EEG electrode sensors and amplifier size. In this study, we measured brain activity during day and night on ten lab member participants using our new flex-printed trEEGrid patch electrodes and a miniaturized and modularized amplifier prototype. The patch was left on participants up to 24 h while they went about their day. EEG was recorded during several exploratory tasks and overnight. Daytime scenarios included auditory and visual oddball tasks, as well as a resting-state measurement and sudoku cognitive load task. Impedances were recorded throughout all tasks. Ag/AgCl ‘ring’ electrodes were used as comparison. Daytime tasks were performed twice (i.e., once in the afternoon and again the next morning), to assess signal quality changes over time. P3 oddball, resting state and workload-related spectral effects were observed on both days. Auditory N1 amplitudes were similar between the patch and ring electrodes. The system shows promising data quality over the nearly 24 h wearing time and allows new possibilities for daytime and sleep EEG studies.