EBAPy: A Python framework for analyzing the factors that have an influence in the performance of EEG-based applications[Formula presented]

Dustin Carrión-Ojeda, Paola Martínez-Arias, Rigoberto Fonseca-Delgado, Israel Pineda

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number100062
JournalSoftware Impacts
Volume8
DOIs
StatePublished - May 2021
Externally publishedYes

Keywords

  • Discrete wavelet transform
  • EEG-based applications
  • Recording time

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