Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 7; pp. 1431 - 1436
• Main Authors: Arico, Pietro, Borghini, Gianluca, Di Flumeri, Gianluca, Sciaraffa, Nicolina, Colosimo, Alfredo, Babiloni, Fabio
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive automation; Algorithms; Brain; Brain Mapping - trends; Brain-Computer Interfaces - trends; Cognitive ability; Computer applications; EEG; Electrocardiography; Electroencephalography; Electroencephalography - trends; Electrooculography; Equipment Design; Forecasting; Human-computer interface; human-machine interaction (HMI); Humans; Implants; Innovations; Interfaces; Learning algorithms; Machine learning; Machine learning algorithms; machine learning techniques; Man-Machine Systems; mental states; operational environments; passive brain-computer interface (pBCI); Pattern Recognition, Automated - trends; Real time; Real-time systems; Reviews; Safety critical; Safety systems; Software - trends; Technology Assessment, Biomedical
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2017.2694856

Goal: This minireview aims to highlight recent important aspects to consider and evaluate when passive brain-computer interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications. Methods: Electroencephalography (EEG) based pBCI has become an important tool for real-time analysis of brain activity since it could potentially provide covertly-without distracting the user from the main task-and objectively-not affected by the subjective judgment of an observer or the user itself-information about the operator cognitive state. Results: Different examples of pBCI applications in operational environments and new adaptive interface solutions have been presented and described. In addition, a general overview regarding the correct use of machine learning techniques (e.g., which algorithm to use, common pitfalls to avoid, etc.) in the pBCI field has been provided. Conclusion: Despite recent innovations on algorithms and neurotechnology, pBCI systems are not completely ready to enter the market yet, mainly due to limitations of the EEG electrodes technology, and algorithms reliability and capability in real settings. Significance: High complexity and safety critical systems (e.g., airplanes, ATM interfaces) should adapt their behaviors and functionality accordingly to the user' actual mental state. Thus, technologies (i.e., pBCIs) able to measure in real time the user's mental states would result very useful in such "high risk" environments to enhance human machine interaction, and so increase the overall safety.