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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، مهر و 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
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