Brain-machine interfaces detect the signals produced by the human brain and translate them into commands. As a result, these systems can be used in many fields, including assistance in the weekdays of patients with various motor dysfunction. BMI devices can be used as an alternative communication channel, for controlling prostheses, wheelchairs or other vehicles that can help people with motor disabilities. Besides helping disadvantaged people, brain-machine interfaces can be used in clinical research, as well as rehabilitation after neurodegenerative diseases.
In order to increase the performance of the BMI devices, EEG measurement can be combined with various test methods that can help processing and displaying the data. A new approach is to combine electroencephalography and eye-tracking. Results of the eye-tracking can be useful for processing EEG data and removing artifacts from it. During the examination of active vision, the neural activity is monitored with free viewing eyes and the recorded EEG signals are processed with the combination of eye-tracking data.
The aim of this research is the investigation of fixation-related brain activity for development of brain-machine interfaces. Related to this, it is necessary to investigate the effect of eye dominance on eye movement parameters and fixation-related EEG activity in amblyopic subjects using a natural, active visual experimental paradigm. For this purpose, processing and evaluation of active visual and EEG data recorded during the experiment are required, and the results and the available literary data make the design of new, active vision-based BMI paradigms possible. This research includes validation of the selected paradigm using simultaneously measured eye movement and EEG data by examining the classification of defined classes.