In this paper, we propose a novel approach to select a subset of relevant frequency bands using sequential forward feature selection method from a composite ...

The use of time- and frequency-based features has proven effective in the process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still, most ...

Abstract— In order to provide basic communication abilities to people with motor disability, motor imagery brain computer interface is one of most widely used ...

Aug 1, 2016 · Section 3 gives the procedures of the proposed classification scheme for motor imagery classification which include wavelet packet decomposition ...

May 3, 2024 · This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems.

The appropriate frequency bands that present these features are Beta band and Mu band in the EEG systems.

This paper presents a channel selection method based on convolutional neural networks to obviate these challenges.

Nov 24, 2022 · We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various ...

Oct 21, 2022 · The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods.

Sep 29, 2018 · This paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs.