Review of key technology and application of wearable electroencephalogram device

doi:10.11772/j.issn.1001-9081.2021071277

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1029-1035.DOI: 10.11772/j.issn.1001-9081.2021071277

Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)

• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles

Review of key technology and application of wearable electroencephalogram device

Jing QIN¹, Fali SUN², Fang HUI³, Zumin WANG²(), Bing GAO², Changqing JI²^,⁴

^1.College of Software Engineering，Dalian University，Dalian Liaoning 116622，China
^2.College of Information Engineering，Dalian University，Dalian Liaoning 116622，China
^3.School of Computer Science，Loughborough University，Loughborough LE113TU，United Kingdom
^4.College of Physical Science and Technology，Dalian University，Dalian Liaoning 116622，China

Received:2021-07-16 Revised:2021-08-11 Accepted:2021-08-27 Online:2022-04-15 Published:2022-04-10
Contact: Zumin WANG
About author:QIN Jing， born in 1981， Ph. D.， associate professor. Her research interests include signal processing， big data analysis.
SUN Fali， born in 1995， M. S. candidate. Her research interests include smart healthcare.
HUI Fang， born in 1976， Ph. D.， assistant professor. His research interests include computer vision.
GAO Bing， born in 1976， Ph. D.， senior engineer. His research interests include data mining， big data.
JI Changqing， born in 1980， Ph. D.， associate professor，. His research interests include big data， artificial intelligence， spatial database.
Supported by:
National Natural Science Foundation of China(62002038)

可穿戴脑电图设备关键技术及其应用综述

秦静¹, 孙法莉², HUI Fang³, 汪祖民²(), 高兵², 季长清²^,⁴

^1.大连大学软件工程学院，辽宁大连 116622
^2.大连大学信息工程学院，辽宁大连 116622
^3.拉夫堡大学计算机科学学院，英国 LE113 TU
^4.大连大学物理科学与技术学院，辽宁大连 116622

通讯作者: 汪祖民
作者简介:秦静（1981—），女，甘肃张掖人，副教授，博士，CCF会员，主要研究方向：信号处理、大数据分析
孙法莉（1995—），女，山东枣庄人，硕士研究生，CCF会员，主要研究方向：智慧医疗； HUI Fang（1976—），男，英国人，助理教授，博士，主要研究方向：计算机视觉
高兵（1976—），男，黑龙江哈尔滨人，高级工程师，博士，CCF会员，主要研究方向：数据挖掘、大数据
季长清（1980—），男，辽宁大连人，副教授，博士，CCF会员，主要研究方向：大数据、人工智能、空间数据库。
基金资助:
国家自然科学基金资助项目(62002038)

Abstract

Abstract:

Wearable ElectroEncephaloGram （EEG） device is a wireless EEG system to daily real-time monitoring. It is developed rapidly and widely applied because of its portability， real-time performance， non-invasiveness， and low-cost advantages. This system is mainly composed of hardware parts such as signal acquisition module， signal processing module， micro-control module， communication module and power supply module， and software parts such as mobile terminal module and cloud storage module. The key technologies of wearable EEG devices were discussed. First， the improvement of EEG signal acquisition module was explained. In addition， the comparisons of wearable EEG device signal preprocessing module， signal noise reduction， artifact processing and feature extraction technology were performed. Then， the advantages and disadvantages of machine learning and deep learning classification algorithms were analyzed， and the application fields of wearable EEG device were summarized. Finally， future development trends of the key technologies of wearable EEG device were proposed.

Key words: wearable ElectroEncephaloGram (EEG) device, real-time monitoring, EEG signal acquisition, EEG signal processing, EEG signal classification

摘要：

可穿戴脑电图（EEG）设备是一种用于日常实时监测的无线EGG系统，因其便携性、实时性、无创性及低成本等优势迅速发展并得到广泛应用。该系统主要由信号采集模块、信号处理模块、微控制模块、通信模块及电源模块等硬件部分以及移动终端模块和云存储模块等软件部分组成。就可穿戴EEG设备关键技术进行论述。首先，阐述了对EGG信号采集模块的改进，另外对可穿戴EEG设备信号预处理模块、信号的降噪、伪影处理及特征提取技术进行比较；然后，对机器学习、深度学习分类算法的优缺点进行分析，并对穿戴式EEG设备的应用领域进行总结；最后，提出可穿戴EEG设备的关键技术未来的发展趋势。

关键词: 可穿戴脑电图设备, 实时监测, 脑电信号采集, 脑电信号处理, 脑电信号分类

CLC Number:

TP391.7

Jing QIN, Fali SUN, Fang HUI, Zumin WANG, Bing GAO, Changqing JI. Review of key technology and application of wearable electroencephalogram device[J]. Journal of Computer Applications, 2022, 42(4): 1029-1035.

秦静, 孙法莉, HUI Fang, 汪祖民, 高兵, 季长清. 可穿戴脑电图设备关键技术及其应用综述[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1029-1035.

Figures/Tables 5

References 61

1	RAMAKURI S K， GHOSH S， GUPTA B. Behaviour state analysis through brain computer interface using wearable EEG devices： a review［J］. Electronic Government， an International Journal， 2017， 13（4）： 377-390. 10.1504/EG.2017.087994
2	孔朝红，刘煜敏，陶枫，等. 脑电图在偏头痛患者中的诊断价值［J］. 临床与病理杂志， 2017， 37（6）：1216-1219. 10.3978/j.issn.2095-6959.2017.06.024
	KONG Z H， LIU Y M， TAO F， et al. Value of electroencephalogram in the diagnosis of migraine patients［J］. Journal of Clinical and Pathological Research， 2017， 37（6）： 1216-1219. 10.3978/j.issn.2095-6959.2017.06.024
3	CARVALHO D， MENDES T， DIAS A I， et al. Interictal spike quantification in Continuous Spike-Wave of Sleep （CSWS）： clinical usefulness of a wearable EEG device［J］. Epilepsy and Behavior， 2020， 104 （Pt A）： No.106902. 10.1016/j.yebeh.2020.106902
4	马思远，樊星，乔慧. 额颞叶脑胶质瘤病人术前脑电图特征的量化分析研究［J］. 中国微侵袭神经外科杂志， 2020， 25（9）：397-400.
	MA S Y， FAN X， QIAO H. Quantitative analysis of electroencephalogram characteristics in patients with frontotemporal gliomas［J］. Chinese Journal of Minimally Invasive Neurosurgery， 2020， 25（9）：397-400.
5	NASEER N， AYAZ H， DEHAIS F. Portable and wearable brain technologies for neuroenhancement and neurorehabilitation［J］. BioMed Research International， 2018， 2018： No.1806374. 10.1155/2018/1806374
6	KANNAN R， ALI S S A， FARAH A， et al. Smart wearable EEG sensor［J］. Procedia Computer Science， 2017， 105： 138-143. 10.1016/j.procs.2017.01.193
7	WAI A A P， HE D J， HUAT N S. IoT-enabled multimodal sensing headwear system［C］// Proceedings of the IEEE 4th World Forum on Internet of Things. Piscataway： IEEE， 2018：286-290. 10.1109/wf-iot.2018.8355103
8	CASSON A J， SMITH S， DUNCAN J S， et al. Wearable EEG： what is it， why is it needed and what does it entail？［C］// Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway： IEEE， 2008： 5867-5870. 10.1109/iembs.2008.4650549
9	LÜHMANN A VON， SOEKADAR S， MÜLLER K R， et al. Headgear for mobile neurotechnology： looking into alter EEG and NIRS probes［C/OL］// Proceedings of the 7th International Conference on Graz Brain-Computer Interface Conference. ［2021-05-23］..
10	XING X， PEI W H， WANG Y J， et al. Assessing a novel micro-seepage electrode with flexible and elastic tips for wearable EEG acquisition［J］. Sensors and Actuators A： Physical， 2017， 270：262-270. 10.1016/j.sna.2017.12.048
11	SRINIVASA M G， PANDIAN P S. Dry electrodes for bio-potential measurement in wearable systems［C］// Proceedings of the 2nd IEEE International Conference on Recent Trends in Electronics， Information and Communication Technology. Piscataway： IEEE， 2017： 270-276. 10.1109/rteict.2017.8256600
12	HSU T R， HSU T Y， HUANG W H， et al. Wearable electroencephalogram signal detection with dry metal electrodes［C］// Proceedings of the 12th International Microsystems， Packaging， Assembly and Circuits Technology Conference. Piscataway： IEEE， 2017： 311-314. 10.1109/impact.2017.8255960
13	FLEURY A， ALIZADEH M， STEFAN G， et al. Toward fabric-based EEG access technologies： seamless knit electrodes for a portable brain-computer interface［C］// Proceedings of the 2017 IEEE Life Sciences Conference. Piscataway： IEEE， 2017： 35-38. 10.1109/lsc.2017.8268137
14	GAO K P， YANG H J， WANG X L， et al. Soft pin-shaped dry electrode with bristles for EEG signal measurements［J］. Sensors and Actuators A： Physical， 2018， 283： 348-361. 10.1016/j.sna.2018.09.045
15	殷灿. 基于PDA/Pt-TiO₂纳米管阵列的EEG干电极及其SSVEP-BCI应用［D］. 天津：天津理工大学， 2021：17-46. 10.5954/icarob.2021.os13-10
	YIN C. Preparation of polydopamine/Pt-TiO₂ nanotube array-based EEG dry electrode for SSVEP-BCI applications［D］. Tianjin： Tianjin University of Technology， 2021：17-46. 10.5954/icarob.2021.os13-10
16	ASOGWA C O， LIBESON V， LAI D T H. Conductive textile as wearable electrode in intrabody communications［J］. Medical Devices and Sensors， 2018， 1（3）： No.e10016.
17	SHU L， XU T Y， XU X M. Multilayer sweat-absorbable textile electrode for EEG measurement in forehead site［J］. IEEE Sensors Journal， 2019， 19（15）： 5995-6005. 10.1109/jsen.2019.2912667
18	祝伟仝，吕林洋，王鑫，等. 便携式柔性石墨烯电极脑电采集系统［J］. 现代电子技术， 2020， 43（12）：22-26.
	ZHU W T， LYU L Y， WANG X， et al. Portable EEG acquisition system with flexible graphene electrode［J］. Modern Electronics Technique 2020， 43（12）：22-26.
19	ZHAO Z C， IVANOV K， LUBICH L， et al. Signal quality and electrode-skin impedance evaluation in the context of wearable electroencephalographic systems［C］// Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway： IEEE， 2018： 4965-4968. 10.1109/embc.2018.8513457
20	BLEICHNER M G， Concealed DEBENER S.， ear-centered EEG acquisition unobtrusive ： cEEGrids for transparent EEG［J］. Frontiers in Human Neuroscience， 2017， 11： No.163. 10.3389/fnhum.2017.00163
21	JIANG Y B， LEE H， LI G， et al. High performance wearable two-channel hybrid BCI system with eye closure assist［C］// Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway： IEEE， 2016： 5869-5872. 10.1109/embc.2016.7592063
22	ZIBRANDTSEN I C， KIDMOSE P， CHRISTENSEN C B， et al. Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy-a comparison with scalp EEG monitoring［J］. Clinical Neurophysiology， 2017， 128（12）： 2454-2461. 10.1016/j.clinph.2017.09.115
23	VALENTIN O， DUCHARME M， CRÉTOT-RICHERT G， et al. Validation and benchmarking of a wearable EEG acquisition platform for real-world applications［J］. IEEE Transactions on Biomedical Circuits and Systems， 2019， 13（1）： 103-111.
24	MAHDID Y， LEE U， BLAIN-MORAES S. Assessing the quality of wearable EEG systems using functional connectivity［J］. IEEE Access， 2020， 8： 193214-193225. 10.1109/access.2020.3033472
25	JEBELLI H， HWANG S， LEE S. EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device［J］. Journal of Computing in Civil Engineering， 2018， 32（1）： No.04017070. 10.1061/(asce)cp.1943-5487.0000719
26	JEBELLI H， CHOI B， LEE S. Application of wearable biosensors to construction sites. I： assessing workers’ stress［J］. Journal of Construction Engineering and Management， 2019， 145（12）： No.04019079. 10.1061/(asce)co.1943-7862.0001729
27	曲洪权，单一平，刘欲哲，等. 脑电信号中独立分量特征提取与脑力负荷分类［J］. 科学技术与工程， 2020， 20（28）：11499-11504. 10.3969/j.issn.1671-1815.2020.28.014
	QU H Q， SHAN Y P， LIU Y Z， et al. Extraction of independent component features in electroencephalogram signals and classification of mental workload［J］. Science Technology and Engineering， 2020， 20（28）：11499-11504. 10.3969/j.issn.1671-1815.2020.28.014
28	MAJMUDAR C A， MORSHED B I. Autonomous OA removal in real-time from single channel EEG data on a wearable device using a hybrid algebraic-wavelet algorithm［J］. ACM Transactions on Embedded Computing Systems， 2017， 16（1）： No.20.
29	CHAUDHARY M， MUKHOPADHYAY S， LITOIU M， et al. Understanding brain dynamics for color perception using wearable EEG headband［C］// Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering. Armonk， NY： IBM Corporation， 2020：13-22.
30	MAHAJAN R， MORSHED B I. Sample Entropy enhanced wavelet-ICA denoising technique for eye blink artifact removal from scalp EEG dataset［C］// Proceedings of the 6th International IEEE/EMBS Conference on Neural Engineering. Piscataway： IEEE， 2013： 1394-1397. 10.1109/ner.2013.6696203
31	MAHAJAN R， MORSHED B I. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy， kurtosis， and wavelet-ICA［J］. IEEE Journal of Biomedical and Health Informatics， 2015， 19（1）： 158-165. 10.1109/jbhi.2014.2333010
32	张锐，刘家俊，陈明明，等. 基于小波变换—集合经验模态分解的单通道脑电信号眼电伪迹自动去除研究［J］. 生物医学工程学杂志， 2021， 38（3）：473-482. 10.7507/1001-5515.202012017
	ZHANG R， LIU J J， CHENG M M， et al. Research on automatic removal of ocular artifacts from single channel electroence-phalogram signals based on wavelet transform and ensemble empirical mode decomposition［J］. Journal of Biomedical Engineering， 2021， 38（3）： 473-482. 10.7507/1001-5515.202012017
33	姚粤汉. 脑电信号的伪迹去除与情绪识别研究［D］. 广州：华南理工大学， 2020：25-43.
	YAO Y H. Study on EEG artifacts removal and emotion recognition［J］. Guangzhou： South China University of Technology， 2020：25-43.
34	陈妮，覃玉荣，孙鹏飞. 基于脑电自回归预测的实时相位估计方法［J］. 电子测量与仪器学报， 2020， 34（6）：183-190.
	CHEN N， QIN Y R， SUN P F. Real time phase estimation method based on autoregressive prediction of EEG［J］. Journal of Electronic Measurement and Instrumentation， 2020， 34（6）：183-190.
35	ARPAIA P， MOCCALDI N， PREVETE R， et al. A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis［J］. IEEE Transactions on Instrumentation and Measurement， 2020， 69（10）： 8335-8343. 10.1109/tim.2020.2988744
36	LI M Y， CHEN W Z. FFT-based deep feature learning method for EEG classification［J］. Biomedical Signal Processing and Control， 2021， 66： No.102492. 10.1016/j.bspc.2021.102492
37	RAMOS-AGUILAR R， OLVERA-LÓPEZ J A， OLMOS-PINEDA I， et al. Feature extraction from EEG spectrograms for epileptic seizure detection［J］. Pattern Recognition Letters， 2020， 133： 202-209. 10.1016/j.patrec.2020.03.006
38	王玉潇，姜威，刘治，等. 基于共空间模式算法和支持向量机二重分类的癫痫发病预测［J］. 生物医学工程学杂志， 2021， 38（1）：39-46. 10.7507/1001-5515.201911042
	WANG Y X， JIANG W， LIU Z， et al. Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification［J］. Journal of Biomedical Engineering， 2021， 38（1）：39-46. 10.7507/1001-5515.201911042
39	ZHANG Q Z， DING J， KONG W Z， et al. Epilepsy prediction through optimized multidimensional sample entropy and Bi-LSTM［J］. Biomedical Signal Processing and Control， 2021， 64： No.102293. 10.1016/j.bspc.2020.102293
40	ARSALAN A， MAJID M. A study on multi-class anxiety detection using wearable EEG headband［J］. Journal of Ambient Intelligence and Humanized Computing， 2021， 6： 1-11. 10.1007/s12652-021-03249-y
41	CHAVARRIAGA R， FRIED-OKEN M， KLEIH S， et al. Heading for new shores！ overcoming pitfalls in BCI design［J］. Brain-Computer Interfaces， 2017， 4（1/2）： 60-73. 10.1080/2326263x.2016.1263916
42	ZHU M K， CHEN J F， LI H B， et al. Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network［J］. Neural Computing and Applications， 2021， 33（20）： 13965-13980. 10.1007/s00521-021-06038-y
43	ATTIA M， HETTIARACHCHI I， HOSSNY M， et al. A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks［C］// Proceedings of the IEEE 15th International Symposium on Biomedical Imaging. Piscataway： IEEE， 2018： 766-769. 10.1109/isbi.2018.8363685
44	HEFRON R G， BORGHETTI B J， CHRISTENSEN J C， et al. Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation［J］. Pattern Recognition Letters， 2017， 94： 96-104. 10.1016/j.patrec.2017.05.020
45	MARTINS I P， WESTERFIELD M， LOPES M， et al. Brain state monitoring for the future prediction of migraine attacks［J］. Cephalalgia， 2020， 40（3）： 255-265. 10.1177/0333102419877660
46	CAO Z H， LIN C T， LAI K L， et al. Extraction of SSVEPs-based inherent fuzzy entropy using a wearable headband EEG in migraine patients［J］. IEEE Transactions on Fuzzy Systems， 2020， 28（1）： 14-27. 10.1109/tfuzz.2019.2905823
47	FÜRBASS F， KAMPUSCH S， KANIUSAS E， et al. Automatic multimodal detection for long-term seizure documentation in epilepsy［J］. Clinical Neurophysiology， 2017， 128（8）： 1466-1472. 10.1016/j.clinph.2017.05.013
48	SOPIC D， AMINIFAR A， ATIENZA D. e-Glass： a wearable system for real-time detection of epileptic seizures［C］// Proceedings of the 2018 IEEE International Symposium on Circuits and Systems. Piscataway： IEEE， 2018： 1-5. 10.1109/iscas.2018.8351728
49	TAUFIQUE Z， ZHU B Z， COPPOLA G， et al. A low power multi-class migraine detection processor based on somatosensory evoked potentials［J］. IEEE Transactions on Circuits and Systems Ⅱ： Express Briefs， 2021， 68（5）： 1720-1724. 10.1109/TCSII.2021.3066389
50	BILLECI L， TONACCI A， TARTARISCO G， et al. An integrated approach for the monitoring of brain and autonomic response of children with autism spectrum disorders during treatment by wearable technologies［J］. Frontiers in Neuroscience， 2016， 10： No.276. 10.3389/fnins.2016.00276
51	ROHANI D A， FAURHOLT-JEPSEN M， KESSING L V， et al. Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders： systematic review［J］. JMIR mHealth and uHealth， 2018， 6（8）： No.e165. 10.2196/mhealth.9691
52	DEHZANGI O， FAROOQ M. Wearable Brain Computer Interface （BCI） to assist communication in the Intensive Care Unit （ICU）［C］// Proceedings of the 2018 IEEE International Conference on Consumer Electronics. Piscataway： IEEE， 2018： 1-4. 10.1109/icce.2018.8326188
53	ROSHDY A， KORK S AL， SAID S， et al. A wearable exoskeleton rehabilitation device for paralysis ― a comprehensive study［J］. Advances in Science， Technology and Engineering Systems Journal， 2019， 4（1）： 17-26. 10.25046/aj040103
54	BHAGAT N A， VENKATAKRISHNAN A， ABIBULLAEV B， et al. Design and optimization of an EEG-based Brain Machine Interface （BMI） to an upper-limb exoskeleton for stroke survivors［J］. Frontiers in Neuroscience， 2016， 10： No.122. 10.3389/fnins.2016.00122
55	JEBELLI H， KHALILI M M， HWANG S， et al. A supervised learning-based construction workers’ stress recognition using a wearable Electroencephalography （EEG） device［C］// Proceedings of the 2018 Construction Research Congress. Reston， VA： American Society of Civil Engineers， 2018： 40-50. 10.1061/9780784481288.005
56	YOKOTA Y， TANAKA S， MIYAMOTO A， et al. Estimation of human workload from the auditory steady-state response recorded via a wearable electroencephalography system during walking［J］. Frontiers in Human Neuroscience， 2017， 11： No.314. 10.3389/fnhum.2017.00314
57	HWANG S， JEBELLI H， CHOI B， et al. Measuring workers’ emotional state during construction tasks using wearable EEG［J］. Journal of Construction Engineering and Management， 2018， 144（7）： No.04018050. 10.1061/(asce)co.1943-7862.0001506
58	BETTI S， LOVA R M， ROVINI E， et al. Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers［J］. IEEE Transactions on Biomedical Engineering， 2018， 65（8）： 1748-1758. 10.1109/tbme.2017.2764507
59	HA U， YOO H J. An EEG-NIRS ear-module SoC for wearable drowsiness monitoring system［C］// Proceedings of the 2016 IEEE Asian Solid-State Circuits Conference. Piscataway： IEEE， 2016： 193-196. 10.1109/asscc.2016.7844168
60	ZHANG Z T， LUO D Y， RASIM Y， et al. A vehicle active safety model： Vehicle speed control based on driver vigilance detection using wearable EEG and sparse representation［J］. Sensors， 2016， 16（2）： No.242. 10.3390/s16020242
61	KARTSCH V， BENATTI S， ROSSI D， et al. A wearable EEG-based drowsiness detection system with blink duration and alpha waves analysis［C］// Proceedings of the 8th International IEEE/EMBS Conference on Neural Engineering. Piscataway： IEEE， 2017： 251-254. 10.1109/ner.2017.8008338

产品名称	电极类型	通道数	数据传输类型	质量/g
MindWave	干	1	蓝牙	90
EPOC（+）	干	5~14	蓝牙	125
Jellyfish	干	4	WiFi	95
Muse	干	4~7	蓝牙	41
Smarting	湿	24	蓝牙	60
LiveAmp	干	8~64	蓝牙	30

产品名称	电极类型	通道数	数据传输类型	质量/g
MindWave	干	1	蓝牙	90
EPOC（+）	干	5~14	蓝牙	125
Jellyfish	干	4	WiFi	95
Muse	干	4~7	蓝牙	41
Smarting	湿	24	蓝牙	60
LiveAmp	干	8~64	蓝牙	30

方法	优点	缺点
自回归^［34］	高分辨率，频谱损失小	不适用于固定信号
主成分分析^［35］	正交变换，不丢失数据	无法处理复杂数据集
独立分量分析^［27］	线性变换，计算量小	提取的特征可能相关
快速傅里叶变换^［36］	通过时域转化到频域提取特征	损失时间域信息，对信号敏感
短时傅里叶变换^［37］	能提取时频域特征	窗口大小难确定
小波变换^［29］	时频特征，适用于非平稳信号	适用与少通道脑电信号
共空间模式法^［38］	能提取信号的空间相关性信息	需要多通道分析，易受噪声影响
样本熵法^［39］	运算量小，算法稳定	无法反映时频信息
功率谱分析^［40］	算法简单，易操作	无法描述信号的非线性信息

方法	优点	缺点
自回归^［34］	高分辨率，频谱损失小	不适用于固定信号
主成分分析^［35］	正交变换，不丢失数据	无法处理复杂数据集
独立分量分析^［27］	线性变换，计算量小	提取的特征可能相关
快速傅里叶变换^［36］	通过时域转化到频域提取特征	损失时间域信息，对信号敏感
短时傅里叶变换^［37］	能提取时频域特征	窗口大小难确定
小波变换^［29］	时频特征，适用于非平稳信号	适用与少通道脑电信号
共空间模式法^［38］	能提取信号的空间相关性信息	需要多通道分析，易受噪声影响
样本熵法^［39］	运算量小，算法稳定	无法反映时频信息
功率谱分析^［40］	算法简单，易操作	无法描述信号的非线性信息

种类	分类器	优点	缺点
机器学习	K近邻^［35］	易实现，新数据可随意加入	不适用大数据量样本；K值需预设定，无自适应
	支持向量机^［27］	适用小样本数据集；对数据不敏感，模型泛化能力强	不适用于大量数据和多分类问题；对参数和核函数选择敏感
	随机森林^［29］	准确性高；可处理高维数据、对数据集的适应能力强	树多时，训练时空间开销大；噪声大时，模型容易过拟合
	线性判别分析^［41］	计算要求低，适合于在线脑机接口系统；既可降维，又可分类	不适用于处理非线性数据；可能过度拟合数据
深度学习	多层感知机^［37］	网络结构相较简单	隐含节点个数难确定；容易陷入局部极值
	卷积神经网络^［42］	局部感知细化特征提取，权值共享减少网络参数；池化层降低数据维度	需要大样本量和反复调参
	循环神经网络^［43］	具有记忆功能；用于时序数据处理	无法长期依赖，甚至导致梯度消失或爆炸
	长短期记忆网络^［44］	解决长期依赖问题；可输入不定长时序数据	并行处理上存在劣势

Review of key technology and application of wearable electroencephalogram device

可穿戴脑电图设备关键技术及其应用综述

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