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1Understanding brain function using electroencephalography (EEG) is an 2 important issue for cerebral nervous system diseases, especially for epilepsy and 3Alzheimer's disease. Many EEG measurement system systems are used reliably to 4 study these diseases, but their bulky size and the use of wet sensors make them 5 uncomfortable and inconvenient for users. To overcome the limitations of 6 conventional EEG measurement system systems, a wireless and wearable 7 multi-channel EEG measurement system is proposed in this study. This system 8 includes a wireless data acquisition device, dry spring-loaded sensors and a 9 size-adjustable soft cap. We compared the performance of the proposed system using 10 dry versus conventional wet sensors. A significant positive correlation between 11 readings from wet and dry sensors was achieved, thus demonstrating the performance 12 of the system. Moreover, four different features of EEG signals (i.e., normal, 13 eye-blinking, closed-eyes and teeth-clenching signals) were measured by 16 dry 14 sensors to ensure that they could be detected in real-life cognitive neuroscience 15 applications. Thus, we have shown that it is possible to reliably measure EEG signals 16 using the proposed system. This study presents novel insights into the field of 17 cognitive neuroscience, showing the possibility of studying brain function under 18 real-life conditions. 19 3 1

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90 % for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components.

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