Chinese Journal of Magnetic Resonance >

2024 , Vol. 41 >Issue 4: 405 - 417

DOI: https://doi.org/10.11938/cjmr20243096

Articles

Study of Visual Hybrid Brain-Computer Interface Based on Wearable Magnetoencephalogram

  • WANG Chenxu ,
  • GUO Xu ,
  • WANG Hui ,
  • ZHANG Xin ,
  • CHANG Yan ,
  • GUO Qingqian ,
  • HU Tao ,
  • FENG Xiaoyu ,
  • YANG Xiaodong
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  • 1. School of Medical Imaging, Xuzhou Medical University, Xuzhou 221004, China
    2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
    3. Jihua Laboratory, Foshan 528000, China

Received date: 2024-02-02

  Online published: 2024-03-11

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Abstract

The emerging wearable magnetoencephalography technology lays the foundation for brain-computer interface to provide high-quality data. To explore the feasibility of applying wearable magnetoencephalography in visual hybrid brain-computer interface, a SSVEF-Alpha hybrid brain-computer interface is designed based on steady-state visual evoked field and Alpha wave, and the performance is compared with different classification models. The results show that based on the user-dependent training method, the average classification accuracy of hybrid brain-computer interface is (93.29±1.69)%, the information transmission rate can reach 86.81 bits/min. And the user-independent training method with short data length shows superiority over the training-free method. This study verifies the effectiveness of visual hybrid brain-computer interface and provides a reference example for further development and design of brain-computer interface products of wearable magnetoencephalography.

Key words： brain-computer interface; wearable magnetoencephalogram; steady-state visual evoked field; Alpha wave; classification accuracy; information transmission rate

Cite this article

WANG Chenxu , GUO Xu , WANG Hui , ZHANG Xin , CHANG Yan , GUO Qingqian , HU Tao , FENG Xiaoyu , YANG Xiaodong . Study of Visual Hybrid Brain-Computer Interface Based on Wearable Magnetoencephalogram[J]. Chinese Journal of Magnetic Resonance, 2024 , 41(4) : 405 -417 . DOI: 10.11938/cjmr20243096

References

[1] BENABID A L, COSTECALDE T, ELISEYEV A, et al. An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration[J]. Lancet Neurol, 2019, 18(12): 1112-1122.
[2] ZHAI W W, YANG Y N, LU S Y, et al. Brain computer interface system research of upper limb rehabilitation training robot[J]. Journal of Biomedical Engineering Research, 2019, 38(3):269-274.
  翟文文, 杨玉娜, 鲁守银, 等. 上肢康复训练机器人的脑机接口系统研究[J]. 生物医学工程研究, 2019, 38(3): 269-274.
[3] ANUMANCHIPALLI G K, CHARTIER J, CHANG E F. Speech synthesis from neural decoding of spoken sentences[J]. Nature, 2019, 568(7753): 493-498.
[4] LEEB R, FRIEDMAN D, MüLLER-PUTZ G R, et al. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic[J]. Comput Intell Neurosci, 2007, 2007: 79642.
[5] ZRENNER C, DESIDERI D, BELARDINELLI P, et al. Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex[J]. Brain Stimul, 2018, 11(2): 374-389.
[6] LIU H, DU Y X, PENG J, et al. A review of brain-computer interface development[J]. 2011, 24(5): 116-119.
  刘辉, 杜玉晓, 彭杰, 等. 脑-机接口技术发展[J]. 电子科技, 2011, 24(5): 116-119.
[7] 拉杰什P.N.拉奥. 脑机接口导论[M]. 张莉, 陈民铀, 译. 脑机接口导论, 2016.
[8] VANSTEENSEL M J, PELS E G M, BLEICHNER M G, et al. Fully implanted brain-computer interface in a locked-in patient with ALS[J]. N Engl J Med, 2016, 375(21): 2060-2066.
[9] PANDARINATH C, NUYUJUKIAN P, BLABE C H, et al. High performance communication by people with paralysis using an intracortical brain-computer interface[J]. Elife, 2017, 6: e18554.
[10] KRUSIENSKI D J, SHIH J J. Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus[J]. J Neural Eng, 2011, 8: 025006.
[11] LOPES DA SILVA F. EEG and MEG: Relevance to neuroscience[J]. Neuron, 2013, 80(5): 1112-1128.
[12] HEDRICH T, PELLEGRINO G, KOBAYASHI E, et al. Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG[J]. NeuroImage, 2017, 157: 531-544.
[13] IIVANAINEN J, STENROOS M, PARKKONEN L. Measuring MEG closer to the brain: Performance of on-scalp sensor arrays[J]. NeuroImage, 2017, 147: 542-553.
[14] BOTO E, HOLMES N, LEGGETT J, et al. Moving magnetoencephalography towards real-world applications with a wearable system[J]. Nature, 2018, 555(7698): 657-661.
[15] ZHANG S L, CAO N. A synthetic optically pumped gradiometer for magnetocardiography measurements[J]. Chin Phys B, 2020, 29(4): 040702.
[16] TIERNEY T M, HOLMES N, MELLOR S, et al. Optically pumped magnetometers: From quantum origins to multi-channel magnetoencephalography[J]. NeuroImage, 2019, 199: 598-608.
[17] CHEN C Q, ZHANG X, GUO Q Q, et al. Moving wearable magnetoencephalography measurement study based on optically-pumped magnetometer[J]. Chinese J Magn Reson, 2022, 39(3): 337-344.
  陈春巧, 张欣, 郭清乾, 等. 基于原子磁力计的穿戴式脑磁图动态测量研究[J]. 波谱学杂志, 2022, 39(3): 337-344.
[18] SUN W, WANG H, ZHANG Y, et al. Optimal design for quantification of gas concentration based olfactory stimulator[J]. Chinese J Magn Reson, 2021, 38(1):12-21.
  孙韦, 王慧, 张寅, 等. 基于气体浓度定量的嗅觉刺激器优化设计[J]. 波谱学杂志, 2021, 38(1): 12-21.
[19] REGAN D. Steady-state evoked potentials[J]. J Opt Soc Am, 1977, 67(11): 1475-1489.
[20] ZHIGALOV A, HERRING J D, HERPERS J, et al. Probing cortical excitability using rapid frequency tagging[J]. NeuroImage, 2019, 195: 59-66.
[21] BRICKWEDDE M, SCHMIDT M D, KRüGER M C, et al. 20 Hz steady-state response in somatosensory cortex during induction of tactile perceptual learning through LTP-like sensory stimulation[J]. Front Hum Neurosci, 2020, 14: 257.
[22] DEWAN E M. Occipital Alpha rhythm eye position and lens accommodation[J]. Nature, 1967, 214(5092): 975-977.
[23] LI L, ZHANG J X. Study of the alpha wave differences between eyes-closed and eyes-open resting states[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(3): 450-453.
  李凌, 张金香. 闭眼与开眼静息状态下脑电α波的差异研究[J]. 电子科技大学学报, 2010, 39(3): 450-453.
[24] KIRKUP L, SEARLE A, CRAIG A, et al. EEG-based system for rapid on-off switching without prior learning[J]. Med Biol Eng Comput, 1997, 35(5): 504-509.
[25] WITTEVRONGEL B, HOLMES N, BOTO E, et al. Practical real-time MEG-based neural interfacing with optically pumped magnetometers[J]. BMC Biol, 2021, 19(1): 158.
[26] LI X, CHEN J, SHI N, et al. A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG[J]. Expert Syst Appl, 2023, 223: 119736.
[27] RAVI A, BENI N H, MANUEL J, et al. Comparing user-dependent and user-independent training of CNN for SSVEP BCI[J]. J Neural Eng, 2020, 17(2): 026028.
[28] ZHAO D, WANG T, TIAN Y, et al. Filter bank convolutional neural network for SSVEP classification[J]. IEEE Access, 2021, 9: 147129-147141.
[29] BRAINARD D H. The psychophysics toolbox[J]. Spat Vis, 1997, 10(4): 433-436.
[30] VAPNIK V. Statistical learning theory[M]. New York: Wiley, 1998.
[31] NAKANISHI M, WANG Y, WANG Y T, et al. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials[J]. PLoS One, 2015, 10(10): e0140703.
[32] TANAKA H, KATURA T, SATO H. Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data[J]. NeuroImage, 2013, 64: 308-327.
[33] LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces[J]. J Neural Eng, 2018, 15(5): 056013.
[34] WOLPAW J R, RAMOSER H, MCFARLAND D J, et al. EEG-based communication: improved accuracy by response verification[J]. IEEE Trans Rehabil Eng, 1998, 6(3): 326-333.
[35] ZHANG Y, VALSECCHI M, GEGENFURTNER K R, et al. The time course of chromatic adaptation in human early visual cortex revealed by SSVEPs[J]. J Vis, 2023, 23(5): 17.
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