A Wearable Low-Power Collaborative Sensing System for High-Quality SSVEP-BCI Signal Acquisition

The brain-computer interface (BCI) technology improves the communication efficiency between people and Internet of Things (IoT) devices. BCI based on the steady-state visual evoked potential (SSVEP-BCI) is the preferred scheme for controlling devices because of its convenient operation, low training...

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Bibliographic Details
Published in:IEEE internet of things journal 2022-05, Vol.9 (10), p.7273-7285
Main Authors: Na, Rui, Zheng, Dezhi, Sun, Ying, Han, Mingzhe, Wang, Shuai, Zhang, Shuailei, Hui, Qianxin, Chen, Xinlei, Zhang, Jun, Hu, Chun
Format: Article
Language:English
Subjects:
Circuit design
Collaboration
Correlation analysis
eHealth and mHealth
Electrodes
Electroencephalography
embedded devices
Human-computer interface
Internet of Things
low-cost sensors and devices
low-power devices and circuits
Medical research
Power consumption
Power demand
Power management
sensor signal processing
Sensors
Signal processing
Signal quality
Steady state
Visual signals
Visualization
Wearable technology
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Summary:The brain-computer interface (BCI) technology improves the communication efficiency between people and Internet of Things (IoT) devices. BCI based on the steady-state visual evoked potential (SSVEP-BCI) is the preferred scheme for controlling devices because of its convenient operation, low training requirement, and high information transmission rate (ITR). Most signal acquisition devices for BCIs are used for medical diagnosis and scientific research and utilize multiple channels and wet electrodes to obtain high-quality signals. However, the practicability, wearability, and cost of the signal acquisition devices for real-life applications need to be considered, resulting in new requirements for the acquisition mode, the number of electrodes, power consumption, and signal processing methods. This article presents a wearable low-power collaborative sensing system based on a time mask window canonical correlation analysis method (TMW-CCA). An 8-array spring dry electrode signal acquisition device based on a flexible circuit board is designed to address the shortcomings of traditional wet electrode acquisition devices, such as high-power consumption, discomfort, and being unsuitable for long-time use. The proposed TMW-CCA method, which uses a dry electrode sensor to evaluate the time domain's signal quality dynamically, exhibits 12.5% higher steady-state visual evoked potential recognition accuracy and 40% lower average power consumption (only 740 mW) than the benchmark.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3113910