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## The evolution of passive brain–computer interfaces: enhancing the human–machine interaction

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In the last decade, a real revolution in the field of brain -computer interfaces (BCI) led from the "overt" detection of human intention to the "covert" assessment of the actual human mental states. While the first aspect is the basis of the traditional BCI systems, the latter represents the outcome of the passive BCI applications. In fact, passive BCI derives its outputs from brain activity arising without the purpose of voluntary control, but implicitly related to the human mental state. The necessity of monitoring human mental states driven by safety -critical application has been just the boost to the passive BCIs developing: more in general, passive BCI represents the implicit channel of information that enhances the goal -oriented cooperation of humans and machines as a whole, the so-called human -machine interaction. So far, there have been countless passive BCI applications in a wide range of contexts such as driving, gaming, and surgery. If on the one hand, this has been possible thanks to the development of more and more discrete neurotechnological devices, on the other hand, we must not overlook the significant step forward in the employed algorithms, with the adoption in this field of machine learning and deep learning enhancements. This chapter will retrace not only the major achievements but also the future trends, in terms of technologies, methods, and applications of what concerns the field of passive BCIs. The final aim of the work is to draw a mark on where we are nowadays and the future challenges, in order to make passive BCIs closer to being integrated into day -life applications.

Chapter Contents:

• 6.1 Passive BCI as mind–computer interface
• 6.1.1 Passive BCI applications
• 6.1.1.1 User-state monitoring
• 6.1.1.2 Training, education, and cognitive improvement
• 6.1.1.3 Gaming and entertainment
• 6.1.1.4 Evaluation
• 6.1.1.5 Safety and security
• 6.2 Passive BCI system description
• 6.2.1 New technology for passive BCI
• 6.2.2 Signal processing
• 6.2.3 Features extraction
• 6.2.4 Classification techniques
• 6.3 Laboratory vs. realistic passive BCI example applications
• 6.3.1 Datasets
• 6.3.1.1 Laboratory dataset
• 6.3.1.2 Operational dataset
• 6.3.2 Methods
• 6.3.2.1 Signal processing
• 6.3.2.2 Features extraction
• 6.3.2.3 Classification
• 6.3.3 Results
• 6.3.4 Discussion
• 6.4 Limits, possible solutions, and future trends
• References

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