This repository contains the source code for our paper Data Generation for Brain-Computer Interfaces: A Survey.
In this paper, we provide a comprehensive overview of synthetic data in BCIs, covering its generation, evaluation, and applications, offering a holistic perspective on its utilization. Further details will be provided soon.
Data generation driven machine learning pipeline for BCIs, which includes brain signal acquisition, data preprocessing, data generation, feature engineering, and classification/regression. The latter two components can be unified into a single end-to-end neural network. Data generation approaches are categorized into four types: (a) knowledge-based generation, (b) feature-based generation, (c) model-based generation, and (d) translation-based generation.
Figure 1: Data generation driven machine learning pipeline for BCIs.