… learning (ML) techniques and deep learning (DL) approaches to classify EEG-based …
Machine learning techniques enable the brain computer interface to learn from the subject's brain …

… discriminating standard pattern matching algorithms. In addition, … before regression by machine
learning algorithms. Also, two … The application of machine learning in the BCI system is of …

… deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning
… It also reviews the ML methods used for mental state detection, mental task categorization, …

… computer. The proposed system uses EEG signals as a communication link between brains
and computers. … The extracted features were inputted into machine learning algorithms to …

… This paper tries to demonstrate the performance of different machine learning algorithms …
before introducing them to machine learning algorithms. The algorithms applied are Bayesian …

… discusses machine learning methods and their application to Brain-Computer Interfacing. A
… We also point out common flaws when validating machine learning methods in the context …

… brain signal activities. This paper tries to demonstrate the performance of different machine
learning algorithms … features before introducing them to machine learning algorithms. The …

… algorithms to the newer methods of deep learning. We explore two different types of deep
learning methods, … The results prove the superiority of deep learning methods in comparison …

… This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by
using classifiers from machine learning technique. The BCI system consists of two main steps …

… There is a variety of other brain potentials, that are used for brain-computer interfacing, see
Chapter 2 in this book for an overview. Here, we only introduce those brain potentials, which …