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Passive Brain-Computer Interfaces (pBCIs) have made significant strides, indicating their potential for everyday use beyond research labs. Innovations in user interfaces, classification algorithms, and wearable technology have enhanced the feasibility of pBCIs as consumer products. Companies are now developing wearable biosignal acquisition devices, particularly EEG systems with improved comfort and signal quality.
pBCIs can significantly improve human-environment interactions by interpreting involuntary brain activity related to workload, attention, and emotions. Recent advancements in artificial intelligence, including machine learning and deep learning, have enabled the creation of neurometrics that track human behaviors in real-time. This research topic aims to explore the practical applications of pBCIs in daily life, addressing challenges and future trends.
• pBCIs are ready for practical applications beyond research laboratories.
• AI advancements enable real-time tracking of human behaviors through neurometrics.
pBCIs enhance interaction by interpreting involuntary brain activity, improving user experience.
Neurometrics utilize AI to track specific human behaviors in real-time, enhancing applications.
Machine learning techniques are applied in pBCIs to improve the accuracy of biosignal processing.
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