An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 24; no. 7; pp. 806 - 813
• Main Authors: Yu, Yi-Hsin, Lu, Shao-Wei, Chuang, Chun-Hsiang, King, Jung-Tai, Chang, Che-Lun, Chen, Shi-An, Chen, Sheng-Fu, Lin, Chin-Teng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplifiers, Electronic; Analog-Digital Conversion; Brain-computer interface (BCI); Computer Communication Networks - instrumentation; Devices; Drying; Electric Power Supplies; electro encephalography (EEG); Electrodes; Electroencephalography; Electroencephalography - instrumentation; Equipment Design; Equipment Failure Analysis; Feasibility Studies; Hair; Human-computer interface; Humans; Inflatable; inflatable and wearable wireless system; Monitoring, Ambulatory - instrumentation; Reproducibility of Results; Scalp; Sensitivity and Specificity; Sensors; Signal Processing, Computer-Assisted - instrumentation; Visual; Wearable; Wireless communication; Wireless sensor networks; Wireless Technology - instrumentation
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2016.2516029

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.