EEG Signal-Processing Framework to Obtain High-Quality Brain Waves from an Off-the-Shelf Wearable EEG Device

摘要

Investigating brain waves collected by an electroencephalogram (EEG) can be useful in understanding human psychosocial conditions such as stress, emotional exhaustion, burnout, and mental fatigue. Recently, an off-the-shelf wearable EEG device, which is wireless, lightweight, and affordable, has become available so that field construction workers' psychosocial status can be explored without interfering with their ongoing work. However, capturing high-quality EEG signals from such a device can be very challenging at real construction sites because of the signal artifacts generated by body movement caused by physically demanding work. To address this issue, the authors propose an EEG signal-processing framework that can acquire high-quality EEG signals at real construction sites using a wearable EEG device. Specifically, the signal-processing framework reduces noises and is thus able to extract quality EEG signals. This framework is validated by examining whether brain activation (particularly by body movements) can be identified using the processed EEG signal applied to eight field construction workers under working (i.e., active) and not working (i.e., inactive) conditions. Specifically, mean power spectral density (PSD) of the EEG beta frequency range is calculated from electrodes near the motor cortex, the part of the brain that controls voluntary movements. A significant difference in mean PSD in the beta frequency range between active and inactive conditions demonstrates that the processed EEG signal, based on the proposed framework, captures brain activation. The results show the potential of the proposed signal-processing framework to monitor workers' brain wave patterns in the field with a wearable EEG device, opening up an opportunity to assess workers' psychosocial status in construction so that any psychosocial problems of workers can be investigated. (c) 2017 American Society of Civil Engineers.