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Azadi Moghadam, Maedeh, Maleki, Ali. (1403). Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number. سامانه مدیریت نشریات علمی, 14(4), 365-378. doi: 10.31661/jbpe.v0i0.2401-1703
Maedeh Azadi Moghadam; Ali Maleki. "Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number". سامانه مدیریت نشریات علمی, 14, 4, 1403, 365-378. doi: 10.31661/jbpe.v0i0.2401-1703
Azadi Moghadam, Maedeh, Maleki, Ali. (1403). 'Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number', سامانه مدیریت نشریات علمی, 14(4), pp. 365-378. doi: 10.31661/jbpe.v0i0.2401-1703
Azadi Moghadam, Maedeh, Maleki, Ali. Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number. سامانه مدیریت نشریات علمی, 1403; 14(4): 365-378. doi: 10.31661/jbpe.v0i0.2401-1703

Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number

Journal of Biomedical Physics and Engineering
دوره 14، شماره 4، آبان 2024، صفحه 365-378    اصل مقاله (1.27 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2401-1703
نویسندگان
Maedeh Azadi Moghadam1؛ Ali Maleki* 2
1Department of Biotechnology, Faculty of New Science and Technologies, Semnan University, Semnan, Iran
2Department of Biomedical Engineering, Semnan University, Semnan, Iran
چکیده
Background: A key challenge in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems.
Objective: The current study aimed to examine the effect of data characteristics on frequency recognition accuracy.
Material and Methods: In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics.
Results: The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method. 
Conclusion: Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy.

تازه های تحقیق

Ali Maleki (Google Scholar)

کلیدواژه‌ها
Electroencephalogram؛ Brain-Computer Interfaces؛ Visual Evoked Potential؛ Photic Stimulation
سایر فایل های مرتبط با مقاله
مراجع
  1. Chadaga K, Prabhu S, Sampathila N, Nireshwalya S, Katta SS, Tan RS, Acharya UR. Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review. Diagnostics (Basel). 2023;13(5):824. doi: 10.3390/diagnostics13050824. PubMed PMID: 36899968. PubMed PMCID: PMC10000611.
  2. Azadimoghadam M, Maleki A. Fatigue Assessment using Frequency Features in SSVEP-based Brain-Computer Interfaces. Iran J Biomed Eng. 2022;16(3):251-60. doi: 10.22041/ijbme.2023.560724.1794.
  3. Pronina MV, Ponomarev VA, Poliakov YI, Martins-Mourao A, Plotnikova IV, Müller A, Kropotov YD. Event-related EEG synchronization and desynchronization in patients with obsessive-compulsive disorder. 2023;60(12):e14403. doi: 10.1111/psyp.14403. PubMed PMID: 37578353.
  4. Amini MM, Shalchyan V. Designing a motion-onset visual evoked potential-based brain-computer interface to control a computer game. IEEE TG. 2023:1-10. doi: 10.1109/TG.2023.3279289.
  5. Friðriksdóttir D, Andriyash Y. Exploring Attentional Neural Differences During an Oddball Paradigm: An SSVEP Study [dissertation]. University of Iceland; 2023.
  6. Sadeghi S, Maleki A. Recent Advances in Hybrid Brain-Computer Interface Systems: A Technological and Quantitative Review. Basic Clin Neurosci. 2018;9(5):373-88. doi: 10.32598/bcn.9.5.373. PubMed PMID: 30719252. PubMed PMCID: PMC6360492.
  7. Azadi Moghadam M, Maleki A. Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis. Front Hum Neurosci. 2023;17:1248474. doi: 10.3389/fnhum.2023.1248474. PubMed PMID: 38053651. PubMed PMCID: PMC10694510.
  8. Ziafati A, Maleki A. Genetic algorithm based ensemble system using MLR and MsetCCA methods for SSVEP frequency recognition. Med Eng Phys. 2023;111:103945. doi: 10.1016/j.medengphy.2022.103945. PubMed PMID: 36792239.
  9. Ojha MK, Tiwari P, Choubey DK, Gupta D. Detection of SSVEP Frequency component using Filter Bank Approach for EEG Based BCI System. Neuro Quantology. 2022;20(6):3533-40. doi: 10.14704/nq.2022.20.6.NQ22359.
  10. Vahid F, Behboodi M, Mahnam A. Bichromatic visual stimulus with subharmonic response to achieve a high-accuracy SSVEP BCI system with low eye irritation. Biomedical Signal Processing and Control. 2023;83:104629. doi: 10.1016/j.bspc.2023.104629.
  11. Tong C, Wang H, Wang Y. Relation of canonical correlation analysis and multivariate synchronization index in SSVEP detection. Biomedical Signal Processing and Control. 2022;73:103345. doi: 10.1016/j.bspc.2021.103345.
  12. Li H, Xu G, Li Z, Zhang K, Zheng X, Du C, Han C, Kuang J, Du Y, Zhang S. A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension. IEEE Trans Neural Syst Rehabil Eng. 2023;31:2486-96. doi: 10.1109/TNSRE.2023.3274121. PubMed PMID: 37155399.
  13. Nakanishi M, Wang Y, Chen X, Wang YT, Gao X, Jung TP. Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis. IEEE Trans Biomed Eng. 2018;65(1):104-12. doi: 10.1109/TBME.2017.2694818. PubMed PMID: 28436836. PubMed PMCID: PMC5783827.
  14. Jiao Y, Zhang Y, Jin J, Wang X. Multilayer correlation maximization for frequency recognition in SSVEP brain-computer interface. 6th International Conference on Information Science and Technology (ICIST); Dalian, China: IEEE; 2016. p. 31-5.
  15. Zhang Y, Zhou G, Jin J, Wang X, Cichocki A. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. Int J Neural Syst. 2014;24(4):1450013. doi: 10.1142/S0129065714500130. PubMed PMID: 24694168.
  16. Ziafati A, Maleki A. Fuzzy ensemble system for SSVEP stimulation frequency detection using the MLR and MsetCCA. J Neurosci Methods. 2020;338:108686. doi: 10.1016/j.jneumeth.2020.108686. PubMed PMID: 32173401.
  17. Neghabi M, Marateb HR, Mahnam A. Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels. Basic Clin Neurosci. 2019;10(3):245-6. doi: 10.32598/bcn.9.10.200. PubMed PMID: 31462979. PubMed PMCID: PMC6712635.
  18. Zhang R, Xu Z, Zhang L, Cao L, Hu Y, Lu B, Shi L, Yao D, Zhao X. The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality. J Neural Eng. 2022;19(3):36010. doi: 10.1088/1741-2552/ac6ae5. PubMed PMID: 35477130.
  19. Wang H, Zhang Y, Waytowich NR, Krusienski DJ, Zhou G, Jin J, Wang X, Cichocki A. Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI. IEEE Trans Neural Syst Rehabil Eng. 2016;24(5):532-41. doi: 10.1109/TNSRE.2016.2519350. PubMed PMID: 26812728.
  20. Sadeghi S, Maleki A. Character encoding based on occurrence probability enhances the performance of SSVEP-based BCI spellers. Biomedical Signal Processing and Control. 2020;58:101888. doi: 10.1016/j.bspc.2020.101888.
  21. Sadeghi S, Maleki A. Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs. Turkish J Electr Eng Comput Sci. 2019;27(5):3729-40. doi: 10.3906/elk-1805-32.
  22. Zhang Y, Zhou G, Jin J, Wang M, Wang X, Cichocki A. L1-regularized Multiway canonical correlation analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng. 2013;21(6):887-96. doi: 10.1109/TNSRE.2013.2279680. PubMed PMID: 24122565.
  23. Da Cruz JN, Wan F, Wong CM, Cao T. Adaptive time-window length based on online performance measurement in SSVEP-based BCIs. 2015;149:93-9. doi: 10.1016/j.neucom.2014.01.062.
  24. Chen X, Wang Y, Zhang S, Xu S, Gao X. Effects of stimulation frequency and stimulation waveform on steady-state visual evoked potentials using a computer monitor. J Neural Eng. 2019;16(6):066007. doi: 10.1088/1741-2552/ab2b7d. PubMed PMID: 31220820.
  25. Floriano A, F Diez P, Freire Bastos-Filho T. Evaluating the Influence of Chromatic and Luminance Stimuli on SSVEPs from Behind-the-Ears and Occipital Areas. Sensors (Basel). 2018;18(2):615. doi: 10.3390/s18020615. PubMed PMID: 29462975. PubMed PMCID: PMC5855130.
  26. Luo TJ. A comparative survey of SSVEP recognition algorithms based on template matching of training trials. International Journal of Intelligent Computing and Cybernetics. 2023;16(1):46-67. doi: 10.1108/IJICC-01-2022-0002.
  27. Wong CM, Wang B, Wang Z, Lao KF, Rosa A, Wan F. Spatial Filtering in SSVEP-Based BCIs: Unified Framework and New Improvements. IEEE Trans Biomed Eng. 2020;67(11):3057-72. doi: 10.1109/TBME.2020.2975552. PubMed PMID: 32091986.
  28. Zhang X, Qiu S, Zhang Y, Wang K, Wang Y, He H. Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs. J Neural Eng. 2022;19(4):46027. doi: 10.1088/1741-2552/ac823e. PubMed PMID: 35853437.
  29. Chen X, Wang Y, Gao S, Jung TP, Gao X. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng. 2015;12(4):046008. doi: 10.1088/1741-2560/12/4/046008. PubMed PMID: 26035476.
  30. Huang J, Yang P, Xiong B, Wan B, Su K, Zhang ZQ. Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs. IEEE Trans Neural Syst Rehabil Eng. 2022;30:851-9. doi: 10.1109/TNSRE.2022.3162029. PubMed PMID: 35324445.
  31. Hong J, Qin X. Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments. J Intell Fuzzy Syst. 2021;40(6):10559-73. doi: 10.3233/JIFS-201280.
  32. Vialatte FB, Maurice M, Dauwels J, Cichocki A. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol. 2010;90(4):418-38. doi: 10.1016/j.pneurobio.2009.11.005. PubMed PMID: 19963032.
  33. Müller-Putz GR, Pfurtscheller G. Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng. 2008;55(1):361-4. doi: 10.1109/TBME.2007.897815. PubMed PMID: 18232384.
  34. Diez PF, Mut VA, Avila Perona EM, Laciar Leber E. Asynchronous BCI control using high-frequency SSVEP. J Neuroeng Rehabil. 2011;8:39. doi: 10.1186/1743-0003-8-39. PubMed PMID: 21756342. PubMed PMCID: PMC3152890.
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