Analysis of Window-Delay Score for Data Augmentation Methods in Brain-Computer Interfaces - Maurício, João Stephan S.; Amorim, Marcelo M.; Borges, Alex; Bernardino, Heder; de Souza, Gabriel (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2024)
Post-stroke motor rehabilitation is a challenging problem in the medical field. Considering this, Brain-Computer Interfaces (BCI) have proven to obtain positive results, especially for chronic stroke. However, as electroencephalogram data collection for BCI can be challenging, Data Augmentation (DA) methods can reduce data collection and simplify training. This study proposes analyzing the temporal behavior of the accuracy instead of analyzing it in fixed intervals, as it is commonly done. Six DA methods and five classification models were evaluated for different scenarios. Results show Filter Bank Common Spatial Pattern is consistent while EEGNet peaks at 2.5 seconds. Sliding Window DA improves response time by 16% and enhances model robustness.