The exciting world of Human–Computer Interaction (HCI) involves the study of the interaction between the user and the computer. Here we see how computer science combines with behavioral sciences and results in interaction at the user interface which includes both software and hardware.
We propose novel methods and algorithms for time-series analysis that can be successfully applied for real-time brain states classification from Electroencephalogram EEG and can be integrated in real-time EEG-enabled systems. Current approach in brain state recognition algorithms development is to propose new features (including non-linear), feature extraction algorithms and/or learn features using deep learning techniques and study different neural network systems to improve accuracy of brain states recognition. The proposed algorithms can be used for optimization of human-computer interfaces, human-machine interfaces, in rehabilitation systems, entertainment, robotics, etc.