Abstract: An adaptive learning interface system for end-users for controlling one or more machines or robots to perform a given task, combining identification of gaze patterns, EEG channel's signal patterns, voice commands and/or touch commands. The output streams of these sensors are analysed by the processing unit in order to detect one or more patterns that are translated into one or more commands to the robot, to the processing unit or to other devices. A pattern learning mechanism is implemented by keeping immediate history of outputs collected from those sensors, analysing their individual behaviour and analysing time correlation between patterns recognized from each of the sensors. Prediction of patterns or combination of patterns is enabled by analysing partial history of sensors' outputs.

Abstract: An adaptive learning interface system for end-users for controlling one or more machines or robots to perform a given task, combining identification of gaze patterns, EEG channel's signal patterns, voice commands and/or touch commands. The output streams of these sensors are analyzed by the processing unit in order to detect one or more patterns that are translated into one or more commands to the robot, to the processing unit or to other devices. A pattern learning mechanism is implemented by keeping immediate history of outputs collected from those sensors, analyzing their individual behavior and analyzing time correlation between patterns recognized from each of the sensors. Prediction of patterns or combination of patterns is enabled by analyzing partial history of sensors' outputs.

Abstract: An adaptive learning interface system for end-users for controlling one or more machines or robots to perform a given task, combining identification of gaze patterns, EEG channel's signal patterns, voice commands and/or touch commands. The output streams of these sensors are analysed by the processing unit in order to detect one or more patterns that are translated into one or more commands to the robot, to the processing unit or to other devices. A pattern learning mechanism is implemented by keeping immediate history of outputs collected from those sensors, analysing their individual behaviour and analysing time correlation between patterns recognized from each of the sensors. Prediction of patterns or combination of patterns is enabled by analysing partial history of sensors' outputs.