Key result
EBAPy Python framework facilitates EEG application development and performance analysis by evaluating key algorithmic factors.
Why the study?
An easy-to-use framework to analyze factors influencing EEG-based application performance and computational cost was needed to reduce development time and evaluate new ideas.
EBAPy is a Python framework that facilitates the development and performance analysis of EEG-based applications.
EBAPy is an easy-to-use Python framework intended to help in the development of EEG-based applications. It allows performing an in-depth analysis of factors that influence the performance of the system and its computational cost. These factors include recording time, decomposition level of Discrete Wavelet Transform, and classification algorithm. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and evaluating new ideas in developing biometric systems using EEGs. Furthermore, different applications that classify EEG signals can use EBAPy because of the generality of its functions. These new applications will impact human–computer interaction in the near future.
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Carrión-Ojeda et al. (2021) studied EEG-based applications. EBAPy Python framework was evaluated. The EBAPy Python framework facilitates the development and performance analysis of EEG-based applications by evaluating factors like recording time, decomposition level, and classification algorithms.
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