A Survey on Machine Learning Methods for Brain–Computer Interface Applications: Techniques, Challenges, and Future Directions

Abstract

A Brain Computer Interface (BCI) turns information from the brain into commands that a computer can understand, so a person can talk to an outside object directly through their brain activity. An EEG-based class of BCI systems is the subject of this paper's research. Specifically, ERP, DL, and ML for processing and classifying signals. Using EEG for signal extraction, preprocessing, feature extraction, classification, and user interface development in BCI pipeline because it is less invasive than other approaches. Reliable BCI signal classifier development requires EEG signals to be non-stationary, extremely noisy, and highly derivative. In order to improve non-invasive EEG classification, machine learning algorithms including linear discriminative analysis and support vector machine classifiers have been developed. Conversely, deep learning methods like transformers, long short-term memory autoencoders, and convolutional neural networks have enabled BCI systems for healthcare rehabilitation, human interface, brain monitoring, and assistive devices