Systematic review of data augmentation methods for EEG-based emotion recognition and cognitive workload decoding.

This systematic review examined the literature regarding data augmentation (DA) methods applied to electroencephalography (EEG) for emotion recognition and cognitive workload decoding. The study population and specific sample size were not reported in the available data. The primary focus was on identifying technical challenges and potential future research directions rather than establishing clinical efficacy or safety profiles.

The review did not report specific numerical results, adverse events, or tolerability data. Consequently, no conclusions regarding patient outcomes or safety can be drawn from this evidence. The investigation serves primarily to map the current state of the field and suggest areas for further technical development.

Key limitations include the lack of reported population details, sample sizes, and specific outcome metrics. The study does not provide evidence to support the routine use of DA methods in clinical settings. Practice relevance is currently restricted to researchers and engineers developing EEG algorithms rather than clinicians managing patient care.

Causality cannot be established as this is a systematic review of methodological approaches rather than a clinical trial. Clinicians should interpret these findings as informational regarding technological development rather than evidence for therapeutic intervention. Further research with defined populations and clinical endpoints is required before any clinical recommendations can be made.