The lack of standardized evaluation metrics and benchmark datasets makes it difficult to compare different approaches objectively

The gap

Evidence profile

Sourced from the future-work section and stated research gap and inline gaps of the source papers, classified as general, spanning 2 journals. Those papers have been cited 2 times in total.

Supporting evidence — 3 representative gaps

• From Acquisition to Validation: Methodological Dependencies and Reproducibility in EEG-Based Alzheimer’s Disease Detection (2026) · Technologies · cited 1× · doi

  Development of community-agreed QC benchmarks for EEG-based AD studies, including minimum QC criteria and quantitative signal quality indices - Public release of fully specified, code-level preprocessing pipelines alongside published studies - Development of explainability methods tailored to EEG-based AD models, enabling clinicians to understand which features contribute to model decisions - Longitudinal designs examining EEG trajectories to track individuals from MCI through conversion to AD - Multimodal integration and inclusion of clinical biomarkers, such as vascular biomarkers, demographics, lifestyle variables, comorbidities, genetic markers, and cognitive measures

  generalfuture-work section
  Keywords: development community-agreed benchmarks eeg-based studies including minimum criteria
• Deep Neural Network-Based Segmentation of Epileptiform Activity Patterns in EEG Approaches Inter-Expert Agreement for a Pediatric Test Cohort (2026) · Technologies · cited 1× · doi

  The lack of standardized evaluation metrics and benchmark datasets makes it difficult to compare different approaches objectively. Most existing studies have focused on relatively small, homogeneous datasets, limiting the generalizability of developed models to diverse patient populations and recording conditions. There remains a critical need for large, comprehensively annotated EEG databases that capture the full spectrum of epileptiform activity across different epilepsy syndromes, age groups, and recording conditions.

  generalstated research gap
  Keywords: lack standardized evaluation metrics benchmark datasets makes difficult
• ScaleSpecter: a frequency-aware multi-scale patch framework for robust physiological classification under non-stationarity (2026) · Frontiers in Human Neuroscience · doi

  the performance gains The current study also focuses mainly on disease classification, while more fine-grained clinical questions remain unexplored. Therefore, the generalization ability of the proposed framework needs to be further examined on larger multicenter cohorts and independent clinical datasets. Although the results on APAVA, ADFTD, and TDBrain suggest that ScaleSpecter can provide effective representations for different neurodegenerative disease- related EEG classification tasks, the current evidence is still limited by the scale and heterogeneity of the available data.

  generalinline gapsevidence 5/5
  Keywords: current disease classi cation clinical performance gains focuses mainly grained questions remain unexplored generalization ability

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