Brain-computer interface technology and its applications in neuroscience

doi:10.3969/j.issn.1672-6731.2021.01.002

Abstract

Abstract:

Brain-computer interface (BCI) is a cutting-edge technology of interdisciplinary integration, which has been widely used in many fields. This article introduces the four key parts of BCI technology, including signal acquisition, feature extraction, feature classification and external devices manipulation. This review introduces the application status of BCI in the medical field, we focus on the application of network science and deep learning in BCI, including extracting deeper electroencephalography (EEG) signal representations and achieving high-precision classification. Lastly, we explore the application prospects and challenges of BCI in the medical field.

Key words: Neurosciences, Electroencephalography, Signal processing, computer-assisted, Review

摘要：

脑机接口是多学科交叉融合的前沿技术，目前已广泛应用于各个领域。本文从脑机接口信号采集、特征提取、特征分类和外部控制设备 4 个关键技术引入，介绍深度学习和复杂网络两项前沿技术在脑机接口系统中的作用与应用，重点归纳脑机接口技术在神经科学领域的应用现状，并探索该项技术在医疗领域中的应用前景与挑战。

关键词: 神经科学, 脑电描记术, 信号处理, 计算机辅助, 综述

WU Jia-ling, GAO Zhong-ke. Brain-computer interface technology and its applications in neuroscience[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2021, 21(1): 3-8.

巫嘉陵, 高忠科. 脑机接口技术及其在神经科学中的应用[J]. 中国现代神经疾病杂志, 2021, 21(1): 3-8.

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References

[1] Chen SG, Jia J. Application of brain-computer interface in rehabilitation of hand function after stroke (review)[J].Zhongguo Kang Fu Li Lun Yu Shi Jian, 2017, 23:23-26.[陈树耿,贾杰.脑机接口在脑卒中手功能康复中的应用进展[J].中国康复理论与实践, 2017, 23:23-26.]
[2] Gao ZK, Dang WD, Wang XM, Hong XL, Hou LH, Ma K, Matja? P. Complex networks and deep learning for EEG signal analysis[J]. Cogn Neurodyn, 2020. [Epub ahead of print]
[3] Gharehbaghi A, Linden M. A deep machine learning method for classifying cyclic time series of biological signals using time-growing neural network[J]. IEEE Trans Neural Netw Learn Syst, 2018, 29:4102-4115.
[4] Siniscalchi SM, Ge FP, Huang Z, Lee CH, Wu B, Li KH, Yang ML. An end-to-end deep learning approach to simultaneous speech dereverberation and acoustic modeling for robust speech recognition[J]. IEEE J Selected Topics Signal Processing, 2017, 11:1289-1300.
[5] Wang DY, Su JL, Yu HB. Feature extraction and analysis of natural language processing for deep learning English Language[J]. IEEE Access, 2020, 8:46335-46345.
[6] Cecotti H, Gräser A. Convolutional neural networks for P300 detection with application to brain-computer interfaces[J]. IEEE Trans Pattern Anal Mach Intell, 2011, 33:433-445.
[7] Gao Z, Wang X, Yang Y, Mu C, Cai Q, Dang W, Zuo S. EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation[J]. IEEE Trans Neural Netw Learn Syst, 2019, 30:2755-2763.
[8] Gao ZK, Wang XM, Yang YX, Li YL, Ma K, Chen GR. A channel-fused dense convolutional network for EEG-based emotion recognition[J]. IEEE Trans Cogn Dev Syst, 2020.[Epub ahead of print]
[9] Gao ZK, Li Y, Yang YX, Dong NL, Yang X, Grebogi C. A coincidence-filtering-based approach for CNNs in EEG-based recognition[J]. IEEE Trans Ind Inform, 2020, 16:7159-7167.
[10] Gao J, Barzel B, Barabási AL. Universal resilience patterns in complex networks[J]. Nature, 2016, 530:307-312.
[11] Gao ZK, Small M, Kurths J, Kurths J, Kurths J. Complex network analysis of time series[J]. New J Phys, 2017, 116:50001.
[12] Gao ZK, Dang WD, Liu MX, Guo W, Ma K, Chen GR. Classification of EEG signals on VEP-based BCI systems with broad learning[J]. IEEE Trans Syst, Man, Cybernetics:Syst, 2020. [Epub ahead of print]
[13] Frisoli A, Loconsole C, Leonardis D, Banno F, Barsotti M, Chisari C, Bergamasco M. A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks[J]. IEEE Trans Systems, Man, Cybernetics:Syst, 2012, 42:1169-1179.
[14] Donati AR, Shokur S, Morya E, Campos DS, Moioli RC, Gitti CM, Augusto PB, Tripodi S, Pires CG, Pereira GA, Brasil FL, Gallo S, Lin AA, Takigami AK, Aratanha MA, Joshi S, Bleuler H, Cheng G, Rudolph A, Nicolelis MA. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients[J]. Sci Rep, 2016, 6:30383.
[15] Cheng N, Phua KS, Lai HS, Tam PK, Tang KY, Cheng KK, Yeow RC, Ang KK, Guan C, Lim JH. Brain-computer interface-based soft robotic glove rehabilitation for stroke[J]. IEEE Trans Biomed Eng, 2020, 67:3339-3351.
[16] Dang WD, Gao ZK, Sun XL, Li R, Cai Q, Grebogi C. Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder[J]. Nonlinear Dyn, 2020, 102:667-677.
[17] Kang Y, Escudero J, Shin D, Ifeachor E, Marmarelis V. Principal dynamic mode analysis of EEG data for assisting the diagnosis of Alzheimer's disease[J]. IEEE J Transl Eng Health Med, 2015, 3:1800110.
[18] Kortelainen J, Vayrynen E, Seppanen T. Isomap approach to EEG-based assessment of neurophysiological changes during anesthesia[J]. IEEE Trans Neural Syst Rehabil Eng, 2011, 19:113-120.
[19] Memar P, Faradji F. A novel multi-class EEG-based sleep stage classification system[J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26:84-95.
[20] Gao ZK, Wang Z, Ma C, Dang WD, Zhang K. A wavelet time-frequency representation based complex network method for characterizing brain activities underlying motor imagery signals[J]. IEEE Access, 2018, 6:65796-65802.
[21] Lu N, Li T, Ren X, Miao H. A deep learning scheme for motor imagery classification based on restricted boltzmann machines[J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25:566-576.
[22] Wang X, Wong WW, Sun R, Chu WC, Tong KY. Differentiated effects of robot hand training with and without neural guidance on neuroplasticity patterns in chronic stroke[J]. Front Neurol, 2018, 9:810.
[23] Wu Q, Yue Z, Ge YX, Ma D, Yin H, Zhao HL, Liu G, Wang J, Dou WB, Pan Y. Brain functional networks study of subacute stroke patients with upper limb dysfunction after comprehensive rehabilitation including BCI training[J]. Front Neurol, 2020, 10:1419.
[24] Mattia D, Pichiorri F, Colamarino E, Masciullo M, Morone G, Toppi J, Pisotta I, Tamburella F, Lorusso M, Paolucci S, Puopolo M, Cincotti F, Molinari M. The promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke:a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response[J]. BMC Neurol, 2020, 20:254.
[25] Khan MA, Das R, Iversen HK, Puthusserypady S. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation:from designing to application[J]. Comput Biol Med, 2020, 123:103843.
[26] Carino-Escobar RI, Carrillo-Mora P, Valdés-Cristerna R, Rodriguez-Barragan MA, Hernandez-Arenas C, Quinzaños-Fresnedo J, Galicia-Alvarado MA, Cantillo-Negrete J. Longitudinal analysis of stroke patients'brain rhythms during an intervention with a brain computer interface[J]. Neural Plast, 2019:ID7084618.
[27] Osuagwu BC, Wallace L, Fraser M, Vuckovic A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation:a randomised pilot study[J]. J Neural Eng, 2016, 13:065002.
[28] Khan A, Chen C, Yuan K, Wang X, Mehra P, Liu Y, Tong KY. Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke[J]. Top Stroke Rehabil, 2020. [Epub ahead of print]
[29] Lu RR, Zheng MX, Li J, Gao TH, Hua XY, Liu G, Huang SH, Xu JG, Wu Y. Motor imagery based brain computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients[J]. Neurosci Lett, 2020, 718:134727.
[30] Vourvopoulos A, Jorge C, Abreu R, Figueiredo P, Fernandes JC, Bermúdez I Badia S. Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation:a clinical case report[J]. Front Hum Neurosci, 2019, 13:244.
[31] Gao ZK, Cai Q, Yang YX, Dong N, Zhang SS. Visibility graph from adaptive optimal kernel time frequency representation for classification of epileptiform EEG[J]. Int J Neural Syst, 2017, 27:1750005.
[32] Tsiouris KM, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI. A long short term memory deep learning network for the prediction of epileptic seizures using EEG signals[J]. Comput Biol Med, 2018, 99:24-37.
[33] Usman SM, Khalid S, Aslam M. Epileptic seizures prediction using deep learning techniques[J]. IEEE Access, 2020, 8:39998-40007.
[34] Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet:a model for automatic sleep stage scoring based on raw singlechannel EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25:1998-2008.
[35] Cai Q, Gao ZK, An JP, Gao S, Grebogi C. A graph temporal fused dual input convolutional neural network for detecting sleep stages from EEG signals[J]. IEEE Trans Circuits Syst Iiexpress Briefs, 2020. [Epub ahead of print]
[36] Jeong J. EEG dynamics in patients with Alzheimer's disease[J]. Clin Neurophysiol, 2004, 115:1490-1505.
[37] Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer's disease from EEG signals:where are we standing[J]? Curr Alzheimer Res, 2010, 7:487-505.
[38] Morabito FC, Campolo M, Labate D, Morabito G, Bonanno L, Bramanti A, de Salvo S, Marra A, Bramanti P. A longitudinal EEG study of Alzheimer's disease progression based on a complex network approach[J]. Int J Neural Syst, 2015, 25:1550005.
[39] Ismail M, Hofmann K, Abd EI Ghany MA. Early diagnoses of Alzheimer using EEG data and deep neural networks classification[C]. 2019 IEEE Global Conference on Internet of Things (GCIoT), Dubai:United Arab Emirates, 2019:1-5.
[40] Sun ST, Li XW, Zhu J, Wang Y, La R, Zhang XM, Wei LQ, Hu B. Graph theory analysis of functional connectivity in major depression disorder with high density resting state EEG data[J]. IEEE Trans Neural Syst Rehabil Eng, 2019, 27:429-439.
[41] Sorbello R, Tramonte S, Giardina ME, La Bella V, Spataro R, Allison B, Guger C, Chella A. A human humanoid interaction through the use of BCI for lockedin ALS patients using neurobiological feedback fusion[J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26:487-497.

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