一种用于被动脑机接口的新型围耳式脑电图仪的研制

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Minsu Kim;Eunkyu Oh;Yoosung Kim;Seonho Kim;Dasom Park;Jung-Hwan Kim;Suhye Kim;Hyunjin Ahn;Chang-Hwan Im
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引用次数: 0

摘要

随着人们对被动脑机接口(pBCI)技术在日常应用中的兴趣的增加,开发实用的可穿戴脑电图(EEG)记录设备变得越来越重要。在实现可穿戴脑电图系统的各种形式因素中,耳-脑电图因其在日常场景中的实用性而被频繁采用。本研究开发了一种适用于pBCI应用的新型可穿戴式耳戴式脑电图记录装置。通过两次pBCI实验验证了该装置的性能。在耳-脑电装置设计过程中,通过α衰减测试确定一对脑电电极的最佳位置。本研究测试的两个实际pBCI应用是预测用户对短视频剪辑的偏好和在线学习时的困倦检测。实验结果显示,偏好预测准确率为85.71%,困倦检测成功率为80%,有效证明了新开发的耳旁式EEG装置在日常生活场景下的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a New Around-the-Ear Electroencephalography Device for Passive Brain–Computer Interface Applications
As interest in passive brain–computer interface (pBCI) technology for everyday applications increases, the development of practical wearable electroencephalography (EEG) recording devices has become increasingly essential. Among the various form factors to implement wearable EEG systems, ear-EEG is frequently employed owning to its usefulness in everyday scenarios. In this study, a new wearable around-the-ear EEG recording device for pBCI applications was developed. The performance of the developed device was validated through two pBCI experiments. During the ear-EEG device design, an alpha attenuation test was conducted to determine the optimal location of a pair of EEG electrodes. The two practical pBCI applications tested in this study were the prediction of users’ preferences for short video clips and drowsiness detection during online learning. The experimental results showed an accuracy of 85.71% in terms of preference prediction and a success rate of 80% in terms of drowsiness detection, effectively demonstrating the practicality of the newly developed around-the-ear EEG device for daily life scenarios.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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