Recent publications in the field of passive brain-computer interfaces (pBCI) suggest that this technology may be ready to leave the research labs and enter the market as a new consumer product. This assumption is supported by recent gains made in graphical user interfaces, classification algorithms, and improvements in the technology's wearability and sensor quality. In recent years, many companies have moved towards the development of wearable and minimally invasive biosignal acquisition devices.

Specifically for EEG systems, there are currently efforts to develop dry sensors (which do not require conductive gel) or possibly to use a water-based technology instead of the classic gel technology, which allows for high signal quality and comfort. There is general agreement that gel-based electrodes should still be considered the gold standard, but the gap with dry electrodes is being more and more reduced.

In this respect, pBCI could be used to enhance human–surrounding interaction. Indeed, the system can derive results from involuntary brain activity that occurs without voluntary control (i.e., implicit information on the user states), e.g., workload, attention, emotions, and in general task-induced states, which can only be captured with low reliability by conventional methods such as subjective (e.g., questionnaires) and/or behavioral (e.g., reaction time) measurements. Over the last decade, it has been repeatedly demonstrated how techniques in the field of artificial intelligence (e.g., pattern recognition, machine learning, deep learning) can be efficiently applied to biosignals (EEG, ECG, etc.) in order to develop so-called called “Neurometrics”, able to track, even online, specific human behaviors.

This research topic aims to outline the state-of-the-art applications of neuroscience-based techniques, specifically focusing on passive BCI systems in real-world settings. The goal is to address current issues and future trends in this field, highlighting how pBCIs can be employed outside of the lab, particularly through the use of wearable devices. By doing so, we aim to provide insights into the potential of pBCIs to enhance human-surroundings interaction and derive implicit information on user states, such as workload, attention, emotions, and task-induced states.

We invite authors to contribute research articles, experimental studies, empirical investigations, or theoretical frameworks related to passive BCI systems. Specific themes that we encourage authors to address include, but are not limited to:
• Advances in graphical user interfaces and classification algorithms for pBCIs
• Improvements in wearable and minimally invasive biosignal acquisition devices
• Comparison between gel-based and dry electrode technologies for pBCIs
• Integration of artificial intelligence techniques (e.g., pattern recognition, machine learning, deep learning) to analyze biosignals and develop neurometrics
• Exploration of challenges, limitations, and future directions for passive BCI systems
By presenting the latest advancements and addressing the practical applications of passive BCI technology, this research topic would thus contribute to the understanding of this field and promote its implementation beyond the confines of research labs.

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Frontiers in Computational Neuroscience

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