EEG data based human attention recognition using various machine learning techniques: a review

Attention is a cognitive process that is essential for human performance. Human development frequently depends on attention; however, this topic still requires additional research. Recently, EEG brain waves have been utilised to identify a person’s attention states. This work aims to review numerous machine learning algorithms to analyse the Electroencephalographic (EEG) data to recognise human attention. Various machine learning approaches for the analysis of emotional states with EEG data were reviewed. Moreover, the analysis includes the performance achieved in various works; their benefits and disadvantages are reviewed. An EEG data processing pipeline and a review of human attention recognition are developed to evaluate the performance of various machine learning techniques. According to the analysis, the neural network framework achieves 99.81% accuracy. The results serve as a design framework for future systems using EEG data on brain activity to monitor people’s health.