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.

This systematic review examines the combined use of electroencephalography (EEG) and transcranial electrical stimulation (tES) in both clinical and healthy populations. The review focuses on EEG’s role in guiding, monitoring, and evaluating tES interventions and assesses the generalizability of EEG responses to different tES protocols. A comprehensive search across Google Scholar, PubMed, Scopus, IEEE Xplore, ScienceDirect, and Web of Science identified 162 relevant studies using the query: “EEG AND (tDCS OR transcranial direct current stimulation OR tACS OR transcranial alternating current stimulation OR tRNS OR transcranial random noise stimulation OR tPCS OR transcranial pulsed current stimulation)”. Quality was assessed using the Quality Assessment Tool for Quantitative Studies (QATQS). Most studies used EEG post tES to assess neuromodulatory effects, with fewer studies using EEG for protocol design or incorporating real-time EEG for adaptive stimulation. Some studies integrated EEG both before and after stimulation, but considerable heterogeneity in tES parameters and EEG metrics limited reproducibility and comparability. Many studies reported non-significant EEG changes despite standardized approaches. Methodological quality was generally low, and the link between EEG changes and clinical outcomes remains unclear. The findings underscore the potential of EEG-informed, personalized tES protocols, though the use of real-time closed-loop systems remains a limited approach in current research.