ARTIFICIAL INTELLIGENCE IN MOTOR IMAGERY-BASED BCI SYSTEMS: A NARRATIVE
DOI:
https://doi.org/10.32896/ajmedtech.v2n2.55-64Keywords:
EEG signal, Motor imagery (MI), Fourier transform (FFT), Classification ModelsAbstract
Artificial intelligence concepts using machine learning models are implemented in medicines to examine medical data and gain insights to improve decision-making. This paper provides a narrative review of “Motor Imagery based brain-computer interface systems”. The essential techniques of machine learning and deep learning are reviewed and compared based on computation and test data accuracy. Various preprocessing and feature extraction techniques are highlighted in this paper, which include FFT-LDA, Wavelet Packet Decomposition (WPD), CSP Algorithm, Fisher ratio algorithm, Discrete Wavelet Transform, and Filter Bank Common Spatial Pattern (FBCSP). This method collects outcomes with multiple perspectives of the MI-BCI and optimizes it. Necessary details of Algorithms applied are also compared to give an insight into Ml techniques.
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