Brain computer interface (BCI) technology has been used to help disabled patients for their daily life by controlling external devices by brain activities. Noninvasive BCI systems typically rely on the scalp-recorded electroencephalogram (EEG). Many BCIs require the user to perform specific voluntary tasks to produce distinct EEG patterns, such as paying attention to stimulus or doing some other mental tasks (e.g., motor imagery) , which were related to human cognitive control. It has been applied to controlling intelligent peripherals in brain-robot interaction (BRI), but a number of the issues, such individual differences among subjects, accuracies, and information transfer rate, need to be addressed. In addition, developing shared control approaches by combining human intelligence and machine intelligence is important to improve the performance of BRI systems.

The aim of this special issue is to call for high-quality research articles as well as review articles with a focus on human cognitive mechanism to improve recognition performance of evoked EEG potentials and on smart brain-robot interaction.

Potential topics include but are not limited to the following:

• Acquisition methods of brain signals
• System designs for brain signal acquisition
• Brain computer interface paradigm based on human cognitive processes
• Closed loop control for subject and decoder learning in BCI system
• New decoding strategies of EEG-based paradigms (SSVEP, P300, N200, motor imagery, hybrid, etc.)
• Improvement of brain signal information transfer rate
• Processing methods for feature extraction and classification
• Interactive approaches between brain and robot
• Machine vision for BRI application
• Integration of human and machine intelligence
• Real-world applications