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Review Article

A review of giant magneto-impedance-based MEG detection technique

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Article: 2322930 | Received 12 Dec 2023, Accepted 20 Feb 2024, Published online: 06 Mar 2024

References

  • He B, Yuan H, Meng J, et al. Brain-computer interfaces. Neural Eng. 2020:131–183. doi: 10.1007/978-3-030-43395-6_4.
  • Peksa J, Mamchur D. State-of-the-art on brain-computer interface technology. Sensors. 2023;23(13):6001. doi: 10.3390/s23136001.
  • Värbu K, Muhammad N, Muhammad Y. Past, present, and future of EEG-based BCI applications. Sensors. 2022;22(9):3331. doi: 10.3390/s22093331.
  • Zabcikova M, Koudelkova Z, Jasek R, et al. Recent advances and current trends in brain‐computer interface research and their applications. Int J Dev Neurosci. 2022;82(2):107–123. doi: 10.1002/jdn.10166.
  • Anbarasan R, Gomez Carmona D, Mahendran R. Human taste-perception: brain computer interface (BCI) and its application as an engineering tool for taste-driven sensory studies. Food Eng Rev. 2022;14(3):408–434. doi: 10.1007/s12393-022-09308-0.
  • Zhang N, Zhou Z, Liu Y, et al. A novel single-character visual BCI paradigm with multiple active cognitive tasks. IEEE Trans Biomed Eng. 2019;66(11):3119–3128. doi: 10.1109/TBME.2019.2900555.
  • Dai W, Liu Y, Lu H, et al. Shared control based on a brain-computer interface for human-multirobot cooperation. IEEE Robot Autom Lett. 2021;6(3):6123–6130. doi: 10.1109/LRA.2021.3091170.
  • Dai W, Liu Y, Lu H, et al. A shared control framework for human-multirobot foraging with brain-computer interface. IEEE Robot Autom Lett. 2021;6(4):6305–6312. doi: 10.1109/LRA.2021.3092290.
  • Li X, Chen J, Shi N, et al. A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG. Expert Syst. Appl. 2023;223:119736. doi: 10.1016/j.eswa.2023.119736.
  • Islam MK, Rastegarnia A. Editorial: recent advances in EEG (non-invasive) based BCI applications. Front Comput Neurosci. 2023;17:1151852. doi: 10.3389/fncom.2023.1151852.
  • Levett JJ, Elkaim LM, Niazi F, et al. Invasive brain computer interface for motor restoration in spinal cord injury: a systematic review. Neuromodulation. 2023:1–7. doi: 10.1016/j.neurom.2023.10.006.
  • Sorger B, Goebel R. Real-time fMRI for brain-computer interfacing. Handb Clin Neurol. 2020;168:289–302. doi: 10.1016/B978-0-444-63934-9.00021-4.
  • Susan Philip B, Prasad G, Hemanth DJ. A systematic review on artifact removal and classification techniques for enhanced MEG-based BCI systems. Brain-Comput Interfaces. 2023;10(2-4):99–113. doi: 10.1080/2326263X.2023.2233368.
  • Janapati R, Dalal V, Sengupta R. Advances in modern EEG-BCI signal processing: a review. Mat Today Proc. 2023;80:2563–2566. doi: 10.1016/j.matpr.2021.06.409.
  • Chugh N, Aggarwal S. Hybrid brain-computer interface spellers: a walkthrough recent advances in signal processing methods and challenges. Int J Hum Comput Interact. 2023;39(15):3096–3113. doi: 10.1080/10447318.2022.2093445.
  • Mohri K, Kohsawa T, Kawashima K, et al. Magneto-inductive effect (MI effect) in amorphous wires. IEEE Trans Magn. 1992;28(5):3150–3152. doi: 10.1109/20.179741.
  • Panina LV, Mohri K. Magneto‐impedance effect in amorphous wires. Appl Phys Lett. 1994;65(9):1189–1191. doi: 10.1063/1.112104.
  • Uchiyama T, Mohri K, Honkura Y, et al. Recent advances of pico-Tesla resolution magneto-impedance sensor based on amorphous wire CMOS IC MI sensor. IEEE Trans Magn. 2012;48(11):3833–3839. doi: 10.1109/TMAG.2012.2198627.
  • Cohen D. Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents. Science. 1968;161(3843):784–786. doi: 10.1126/science.161.3843.784.
  • Caliskan A, Yuksel ME, Badem H, et al. A deep neural network classifier for decoding human brain activity based on magnetoencephalography. Elektron Elektrotech. 2017;23(2):63–67.
  • Zubarev I, Zetter R, Halme H-L, et al. Adaptive neural network classifier for decoding MEG signals. Neuroimage. 2019;197:425–434. doi: 10.1016/j.neuroimage.2019.04.068.
  • Chholak P, Kurkin SA, Hramov AE, et al. Event-related coherence in visual cortex and brain noise: an MEG study. Appl Sci. 2021;11(1):375. doi: 10.3390/app11010375.
  • Niso G, Tadel F, Bock E, et al. Brainstorm pipeline analysis of resting-state data from the open MEG archive. Front Neurosci. 2019;13:284. doi: 10.3389/fnins.2019.00284.
  • Ovchinnikova AO, Vasilyev AN, Zubarev IP, et al. MEG-based detection of voluntary eye fixations used to control a computer. Front Neurosci. 2021;15:619591. doi: 10.3389/fnins.2021.619591.
  • Hsieh Y-W, Lee M-T, Lin Y-H, et al. Motor cortical activity during observing a video of real hand movements versus computer graphic hand movements: an MEG study. Brain Sci. 2020;11(1):6. doi: 10.3390/brainsci11010006.
  • Kim J, Kim M-Y, Kwon H, et al. Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography. J Neurosci Methods. 2020;338:108688. doi: 10.1016/j.jneumeth.2020.108688.
  • Babajani-Feremi A, Noorizadeh N, Mudigoudar B, et al. Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology. Neuroimage Clin. 2018;19:990–999. doi: 10.1016/j.nicl.2018.06.017.
  • Foldes ST, Boninger ML, Weber DJ, et al. Effects of MEG-based neurofeedback for hand rehabilitation after tetraplegia: preliminary findings in cortical modulations and grip strength. J Neural Eng. 2020;17(2):026019. doi: 10.1088/1741-2552/ab7cfb.
  • Roth BJ. Biomagnetism: the first sixty years. Sensors. 2023;23(9):4218. doi: 10.3390/s23094218.
  • Pfeiffer C, Ruffieux S, Jonsson L, et al. A 7-channel high-Tc SQUID-based on-scalp MEG system. IEEE Trans Biomed Eng. 2020;67(5):1483–1489. doi: 10.1109/TBME.2019.2938688.
  • Andersen LM, Pfeiffer C, Ruffieux S, et al. On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG. Neuroimage. 2020;221:117157. doi: 10.1016/j.neuroimage.2020.117157.
  • Cao L, Tang J, Zhang Y, et al. Signal-enhanced spin-exchange relaxation-free atomic magnetometer. Sens Actuators A Phys. 2023;353:114247. doi: 10.1016/j.sna.2023.114247.
  • Liu X, Zhu J, Wang S, et al. Structure optimization of non-magnetic electric heating film for spin exchange relaxation free magnetometer. Sens Int. 2023;4:100233. doi: 10.1016/j.sintl.2023.100233.
  • Dang HB, Maloof AC, Romalis MV. Ultrahigh sensitivity magnetic field and magnetization measurements with an atomic magnetometer. Appl Phys Lett. 2010;97(15):151110.
  • Allred JC, Lyman RN, Kornack TW, et al. High-sensitivity atomic magnetometer unaffected by spin-exchange relaxation. Phys Rev Lett. 2002;89(13):130801. doi: 10.1103/PhysRevLett.89.130801.
  • Li J, Quan W, Zhou B, et al. SERF atomic magnetometer-recent advances and applications: a review. IEEE Sensors J. 2018;18(20):8198–8207. doi: 10.1109/JSEN.2018.2863707.
  • Zhang G, Huang S, Xu F, et al. Multi-channel spin exchange relaxation free magnetometer towards two-dimensional vector magnetoencephalography. Opt Express. 2019;27(2):597–607. doi: 10.1364/OE.27.000597.
  • Guo Q-Q, Hu T, Feng X-Y, et al. A compact and closed-loop spin-exchange relaxation-free atomic magnetometer for wearable magnetoencephalography. Chin Phys B. 2023;32(4):040702. doi: 10.1088/1674-1056/ac7e38.
  • Brookes MJ, Boto E, Rea M, et al. Theoretical advantages of a triaxial optically pumped magnetometer magnetoencephalography system. Neuroimage. 2021;236:118025. doi: 10.1016/j.neuroimage.2021.118025.
  • Brookes MJ, Leggett J, Rea M, et al. Magnetoencephalography with optically pumped magnetometers (OPM-MEG): the next generation of functional neuroimaging. Trends Neurosci. 2022;45(8):621–634. doi: 10.1016/j.tins.2022.05.008.
  • Boto E, Holmes N, Leggett J, et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature. 2018;555(7698):657–661. doi: 10.1038/nature26147.
  • Boto E, Seedat ZA, Holmes N, et al. Wearable neuroimaging: combining and contrasting magnetoencephalography and electroencephalography. Neuroimage. 2019;201:116099. doi: 10.1016/j.neuroimage.2019.116099.
  • Rui Z, Wei X, Yudong D, et al. Recording brain activities in unshielded earth’s field with optically pumped atomic magnetometers. Sci Adv. 2020;6(24):1–8.
  • Zhao B, Li L, Zhang Y, et al. Optically pumped magnetometers recent advances and applications in biomagnetism: a review. IEEE Sensors J. 2023;23(17):18949–18962. doi: 10.1109/JSEN.2023.3297109.
  • Mohri K, Uchiyama T, Yamada M, et al. Arousal effect of physiological magnetic stimulation on elder person’s spine for prevention of drowsiness during car driving. IEEE Trans Magn. 2011;47(10):3066–3069. doi: 10.1109/TMAG.2011.2157087.
  • Mohri K, Honkura Y, Panina LV, et al. Super MI sensor: recent advances of amorphous wire and CMOS-IC magneto-impedance sensor. J Nanosci Nanotechnol. 2012;12(9):7491–7495. doi: 10.1166/jnn.2012.6541.
  • Uchiyama T, Mohri K, Nakayama S. Measurement of spontaneous oscillatory magnetic field of Guinea-pig smooth muscle preparation using pico-Tesla resolution amorphous wire magneto-impedance sensor. IEEE Trans Magn. 2011;47(10):3070–3073. doi: 10.1109/TMAG.2011.2148165.
  • Uchiyama T, Nakayama S, Mohri K, et al. Biomagnetic field detection using very high sensitivity magnetoimpedance sensors for medical applications. Physica Status Solidi. 2009;206(4):639–643. doi: 10.1002/pssa.200881251.
  • Melo LGC, Ménard D, Yelon A, et al. Optimization of the magnetic noise and sensitivity of giant magnetoimpedance sensors. J Appl Phys. 2008;103(3):033903.
  • Tajima S, Uchiyama T, Okuda Y, et al. Brain activity measurement in the occipital region of the head using a magneto-impedance sensor. 2013 Seventh International Conference on Sensing Technology. p. 267–270.
  • Wang K, Tajima S, Song D, et al. Auditory evoked field measurement using magneto-impedance sensors. J Appl Phys. 2015;117(17):17B306.
  • Wang K, Cai C, Yamamoto M, et al. Real-time brain activity measurement and signal processing system using highly sensitive MI sensor. AIP Adv. 2017;7(5):056635.
  • Ma J, Uchiyama T. Alpha rhythm and visual event-related fields measurements at room temperature using magneto-impedance sensor system. IEEE Trans Magn. 2019;55(7):1–6. doi: 10.1109/TMAG.2018.2888875.
  • Uchiyama T, Ma J. Design and demonstration of novel magnetoencephalogram detectors. IEEE Trans Magn. 2019;55(7):1–8. doi: 10.1109/TMAG.2019.2895399.
  • Uchiyama T, Ma J. Development of pico tesla resolution amorphous wire magneto-impedance sensor for bio-magnetic field measurements. J Magn Magn Mater. 2020;514:167148. doi: 10.1016/j.jmmm.2020.167148.
  • Mohri K. Application of amorphous magnetic wires to computer peripherals. Mater Sci Eng. 1994;185(1-2):141–145. doi: 10.1016/0921-5093(94)90937-7.
  • Panina LV, Mohri K, Bushida K, et al. Giant magneto-impedance and magneto-inductive effects in amorphous alloys (invited). J Appl Phys. 1994;76(10):6198–6203. doi: 10.1063/1.358310.
  • Tannous C, Gieraltowski J. Giant magneto-impedance and its applications. J Mater Sci Mater Electron. 2004;15(3):125–133. doi: 10.1023/B:JMSE.0000011350.93694.91.
  • Mansourian S, Bakhshayeshi A, Taghavi Mendi R. Giant magneto-impedance variation in amorphous CoFeSiB ribbons as a function of tensile stress and frequency. Phys Lett A. 2020;384(26):126657. doi: 10.1016/j.physleta.2020.126657.
  • Chen Y, Zou J, Shu X, et al. Enhanced giant magneto-impedance effects in sandwich FINEMET/rGO/FeCo composite ribbons. Appl Surf Sci. 2021;545:149021. doi: 10.1016/j.apsusc.2021.149021.
  • Shuai S, Lu S, Xiang Z, et al. Stress-induced giant magneto-impedance effect of amorphous CoFeNiSiPB ribbon with magnetic field annealing. J Magn Magn Mater. 2022;551:169131. doi: 10.1016/j.jmmm.2022.169131.
  • Chen L, Shuai S, Lu S, et al. Impact of stress and magnetic field in annealing process on GMI and GSI effect of CoFeSiB amorphous material. J Magn Magn Mater. 2023;588:171466. doi: 10.1016/j.jmmm.2023.171466.
  • Liu M, Wang Z, Meng Z, et al. Giant magnetoimpedance effect of multilayered thin film meanders formed on flexible substrates. Micromachines. 2023;14(5):1002. doi: 10.3390/mi14051002.
  • Kraus L. Theory of giant magneto-impedance in the planar conductor with uniaxial magnetic anisotropy. J. Magn. Magn. Mater. 1999;195(3):764–778. doi: 10.1016/S0304-8853(99)00286-3.
  • Kraus L. GMI modeling and material optimization. Sens Actuators A Phys. 2003;106(1–3):187–194. doi: 10.1016/S0924-4247(03)00164-X.
  • Bushida K, Mohri K, Uchiyama T. Sensitive and quick response micro magnetic sensor using amorphous wire MI element colpitts oscillator. IEEE Trans Magn. 1995;31(6):3134–3136. doi: 10.1109/20.490305.
  • Kanno T, Mohri K, Yagi T, et al. Amorphous wire MI micro sensor using C-MOS IC multivibrator. IEEE Trans Magn. 1997;33(5):3358–3360. doi: 10.1109/20.617943.
  • Kawajiri N, Nakabayashi M, Cai CM, et al. Highly stable MI micro sensor using CMOS IC multivibrator with synchronous rectification. IEEE Trans Magn. 1999;35(5):3667–3669. doi: 10.1109/20.800625.
  • Mohri K, Nakamura Y, Uchiyama T, et al. Sensing of human micro-vibration transmitted along solid using pico-tesla magneto-impedance sensor (pT-MI sensor). Piers Online. 2010;6(2):161–164. doi: 10.2529/PIERS090831111225.
  • Nakayama S, Atsuta S, Shinmi T, et al. Pulse-driven magnetoimpedance sensor detection of biomagnetic fields in musculatures with spontaneous electric activity. Biosens Bioelectron. 2011;27(1):34–39. doi: 10.1016/j.bios.2011.05.041.
  • Nakayama S, Sawamura K, Mohri K, et al. Pulse-driven magnetoimpedance sensor detection of cardiac magnetic activity. PLOS One. 2011;6(10):e25834. doi: 10.1371/journal.pone.0025834.
  • Uchiyama T, Hamada N, Cai C. Highly sensitive CMOS magnetoimpedance sensor using miniature multi-core head based on amorphous wire. IEEE Trans Magn. 2014;50(11):1–4. doi: 10.1109/TMAG.2014.2326658.
  • Tajima S, Okuda Y, Watanabe T, et al. High-resolution magneto-impedance sensor with TAD for low noise signal processing. IEEE Trans Magn. 2014;50(11):1–4. doi: 10.1109/TMAG.2014.2332178.
  • Takiya T, Uchiyama T, Aoyama H. Development of first-order gradiometer-type MI sensor and its application for a metallic contaminant detection system. J Magn Soc Jpn. 2016;40(3):51–55. doi: 10.3379/msjmag.1605R001.
  • Takiya T, Uchiyama T. Common-mode magnetic field rejection-type magneto-impedance gradiometer. J Int Counc Electr Eng. 2016;7(1):1–6.
  • Takiya T, Uchiyama T. Development of active shielding-type MI gradiometer and application for magnetocardiography. IEEE Trans Magn. 2017;53(11):1–4. doi: 10.1109/TMAG.2017.2726111.
  • Uchiyama T, Takiya T. Development of precise off-diagonal magnetoimpedance gradiometer for magnetocardiography. AIP Adv. 2017;7(5):056644.
  • Ma J, Uchiyama T. High performance single element MI magnetometer with peak-to-peak voltage detector by synchronized switching. IEEE Trans Magn. 2017;53(11):1–4. doi: 10.1109/TMAG.2017.2712715.
  • Ma J, Uchiyama T. Development of peak-to-peak voltage detector-type MI gradiometer for magnetocardiography. IEEE Trans. Magn. 2018;54(11):1–5.
  • Shi K, Uchiyama T. Sensor circuit for a full-sample magneto-impedance gradiometer. IEEE Magn Lett. 2019;10:1–4. doi: 10.1109/LMAG.2019.2954808.
  • Yao R, Takemura Y, Uchiyama T. High precision MI sensor with low energy consumption driven by low-frequency Wiegand pulse. AIP Adv. 2023;13(2):025201.