Multi-View Pattern Recognition Algorithm For Multi-Channel EEG Signals: A New Approach To Brain Signal Analysis

The multi-view pattern recognition algorithms have been introduced as a revolutionized method in the analysis of multichannel EEG signals which have enhanced the ability to assess complex activities in the brain.This review article introduces the fundamental concepts of multi-view learning, how it has been used in signal processing, and finally differences between EEG analysis.We explain the challenges that come with multi-channel EEG data including high dimensionality of the data, noise, and the challenges with most analysis methods.The article also discusses the theoretical aspects of multi-view pattern recognition and features of pattern recognition such as feature extraction, data preprocessing, and classification.Advancements are described by considering the application of higher-tier AI and ML for enhancing the algorithms' performance, implementation of multi-view recognition together with other signal types, and online analysis of brain signals.In this review, the progress of the multi-view pattern recognition for EEG analysis is presented, and new directions for future studies are suggested, which may be useful for researchers to have a clearer view of the present and the further direction of the multi-view pattern recognition.