This paper review the classification method of EEG signal based on k-nearest neighbor
(kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input …

The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …

Feature extraction and classification play an important role in brain–computer interface (BCI)
systems. In traditional approaches, methods in pattern recognition field are adopted to solve …

Machine learning (ML)-based algorithms have shown promising results in
electroencephalogram (EEG)-based emotion recognition. This study compares five …

Background The processing of brain signals for Motor imagery (MI) classification to have
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …

This study focuses on investigating the performance of different machine learning algorithms
and corresponding comparative analysis in predicting cardiovascular disease. Globally this …

In recent times, the advancements in Brain-Computer Interface has not only been
instrumental in achieving its fundamental purpose of aiding disabled people, but also in …

Abstract Background The Brain Computer Interfaces (BCI) are devices allowing direct
communication between the brain of a user and a machine. This technology can be used by …

This paper represents an important perspective to analyze machine learning (ML)
algorithms, particularly linear and quadratic discriminant analysis algorithms, in order to …

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit
card fraud (CCF). Need for the study: With the advance of technology, the world is …