Deep Learning of EEG Signals for Brain Function and Injury: Data of Reviewed Studies

The data were collected following the PRISMA 2020 protocol and PROSPERO registration (CRD42023489566), covering studies published between 2018 and 2023 in major scientific databases such as ACM Digital Library, Elsevier, Google Scholar, IEEE, PubMed, and Scopus.

Each record in the dataset represents a study that met strict eligibility criteria, including the use of human EEG data, DL techniques, and the provision of details on datasets, preprocessing methods, feature extraction, classifier architectures, and performance metrics. Studies combining EEG with other modalities (e.g., fMRI, ECG) or lacking methodological transparency were excluded. The dataset contains rich metadata organized into more than 80 fields covering: Context and task (e.g., classification, prediction, clinical context) Dataset characteristics (public availability, sample size, duration, arrangement, channels, frequency bands) Preprocessing techniques (noise removal, dimensionality reduction, normalization, data augmentation) Feature extraction methods and technical details Deep Learning architectures (CNN, LSTM, GAT, GAN, Transformers) and hyperparameters (e.g., layers, activation functions, optimizers, learning rates, batch sizes, epochs) Evaluation strategies (validation type, dataset division, cross-validation folds, parameter optimization) Performance metrics (accuracy, AUC, sensitivity, specificity, precision, F1-score, MAE, Wasserstein distance, reconstruction error, spectral entropy difference) Explainable AI (XAI) considerations and comparisons with other techniques or datasets.