Wearable electroencephalography (EEG) enables brain monitoring in real-world environments beyond clinical settings; however, the relaxed constraints of the acquisition setup often compromise signal quality. This review examines methods for artifact detection and for the identification of artifact categories (e.g., ocular) and specific sources (e.g., eye blink) in wearable EEG. A systematic search was conducted across six databases using the query: (“electroencephalographic” OR “electroencephalography” OR “EEG”) AND (“Artifact detection” OR “Artifact identification” OR “Artifact removal” OR “Artifact rejection”) AND “wearable”. Following PRISMA guidelines, 58 studies were included. Artifacts in wearable EEG exhibit specific features due to dry electrodes, reduced scalp coverage, and subject mobility, yet only a few studies explicitly address these peculiarities. Most pipelines integrate detection and removal phases but rarely separate their impact on performance metrics, mainly accuracy (71%) when the clean signal is the reference and selectivity (63%), assessed with respect to physiological signal. Wavelet transforms and ICA, often using thresholding as a decision rule, are among the most frequently used techniques for managing ocular and muscular artifacts. ASR-based pipelines are widely applied for ocular, movement, and instrumental artifacts. Deep learning approaches are emerging, especially for muscular and motion artifacts, with promising applications in real-time settings. Auxiliary sensors (e.g., IMUs) are still underutilized despite their potential in enhancing artifact detection under ecological conditions. Only two studies addressed artifact category identification. A mapping of validated pipelines per artifact type and a survey of public datasets are provided to support benchmarking and reproducibility.

Background: Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Methods: Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. Results: A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. Conclusions: This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence–arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann–Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute $\beta$- and $\gamma$-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations—such as long-term comfort, stability, and wearability of ear-EEG devices—remain important challenges and motivate future research on sustained use in naturalistic environments.

Event-related potentials (ERPs) are electroencephalographic (EEG) waveforms occurring milliseconds after eliciting environmental stimuli. Measurement of ERP amplitude and latency is widely used in clinical applications concerning neurodegenerative disorders. The amplitude of ERPs is comparable to background noise, thus, the typical procedure for acquiring ERPs consists of averaging over many EEG trials (assuming background noise with zero mean). Therefore, managing the delay uncertainty between stimulus delivery and marker apposition on the EEG trial (trigger delay uncertainty) becomes crucial, in general, and very challenging in the case of wireless transducers. However, also in the last IEC 80601-19 standard for electroencephalographs, trigger delay uncertainty is not mentioned among the listed requirements. In this study, a method is presented to: 1) characterize the trigger delay uncertainty and 2) analyze the propagation of this uncertainty on the ERP amplitude. The method was applied to a wireless device certified for clinical application. Twenty thousand measurements were carried out to characterize the uncertainty of the delay between stimulus delivery and marker apposition on the EEG trial. The delay distribution exhibited a mean value of 447 ms and a standard deviation of 33 ms, not compatible with clinical applications based on ERP latency measurements. In the second step, based on a Monte Carlo simulation, results revealed a propagated uncertainty concerning ERP amplitude compatible with clinical application in Alzheimer’s and Parkinson’s diagnosis (relative percentage uncertainty less than 3% with 95% confidence). The results confirmed the opportunity (especially in the case of wireless electroencephalographs) to update the IEC 80601-19 standard concerning the issue of trigger delay uncertainty management.