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Open Access
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Manas P. Patil, V.G. Raut, Krushna S. Rajkule, Rutuja M. Dusane,
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- Student,, Assistant Professor,, Student,, Student, Sinhgad College of Engineering, Pune,, Sinhgad College of Engineering, Pune,, Sinhgad College of Engineering, Pune,, Sinhgad College of Engineering, Pune, Maharashtra,, Maharashtra,, Maharashtra,, Maharashtra, India, India, India, India
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Abstract
nA Brain-Computer Interface (BCI) is a system that translates brain activity patterns into computer commands, bypassing physical movement. Electroencephalography (EEG) is commonly used to acquire signals in BCI research. Visual evoked potentials (VEPs) are brain responses in the visual cortex to visual stimuli. Recent studies show that exposing individuals to flickering at a consistent frequency generates EEG signals synchronized with the stimulation. Efficient extraction of VEP signals begins with preprocessing raw EEG data. Various machine learning techniques—such as support vector machines (SVMs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs)—are evaluated for their ability to classify VEP components like the P100 wave. Feature engineering methods tailored to VEP characteristics are explored to enhance model performance. This study emphasizes integrating machine learning with preprocessed EEG features from Steady State Visually Evoked Potential (SSVEP) signals flickering at specific frequencies. The dataset includes EEG data from experiments using repetitive visual stimuli with two distinct flicker frequencies. The goal is to identify and implement a suitable machine learning approach that improves the extraction of valuable information from VEP data, facilitating further research into these systems.
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Keywords: Brain Computer Interface (BCI) Electroencephalography (EEG), Steady State Visually Evoked Potential (SSVEP), visual stimuli
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Microwave Engineering and Technologies(jomet)]
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References
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- Phanikrishna BV, Pławiak P, Prakash AJ. A brief review on EEG signal pre-processing techniques for real-time brain-computer interface applications. Authorea Preprints. 2023 Oct 30.
- Volosyak I, Cecotti H, Graser A. Optimal visual stimuli on LCD screens for SSVEP based brain-computer interfaces. In2009 4th International IEEE/EMBS Conference on Neural Engineering 2009 Apr 29 (pp. 447–450). IEEE.
- Altın C, Er O. Comparison of different time and frequency domain feature extraction methods on elbow gesture’s EMG. European journal of interdisciplinary studies. 2016 Aug 30;2(3):25–34.
- Acampora G, Trinchese P, Vitiello A. A dataset of EEG signals from a single-channel SSVEP-based brain computer interface. Data in Brief. 2021 Apr 1;35:106826.
- Wang S, Ji B, Shao D, Chen W, Gao K. A methodology for enhancing SSVEP features using adaptive filtering based on the spatial distribution of EEG signals. Micromachines. 2023 Apr 29;14(5):976.
- On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks Nik Khadijah Nik Aznan∗, Stephen Bonner∗, Jason D. Connolly Noura Al Moubayed∗ and Toby P. Breckon∗ Department of {∗Computer Science, Engineering, Psychology} Durham University, Durham, UK
- Albahri AS, Al-Qaysi ZT, Alzubaidi L, Alnoor A, Albahri OS, Alamoodi AH, Bakar AA. A Systematic Review of Using Deep Learning Technology in the Steady‐State Visually Evoked Potential‐Based Brain‐Computer Interface Applications: Current Trends and Future Trust Methodology. International Journal of Telemedicine and Applications. 2023;2023(1):7741735.
- Erkan E, Akbaba M. A study on performance increasing in SSVEP based BCI application. Engineering Science and Technology, an International Journal. 2018 Jun 1;21(3):421–7.
- Norcia AM, Appelbaum LG, Ales JM, Cottereau BR, Rossion B. The steady-state visual evoked potential in vision research: A review. Journal of vision. 2015 May 1;15(6):4–.
- Khan SM, Khan AA, Farooq O. Selection of features and classifiers for EMG-EEG-based upper limb assistive devices—A review. IEEE reviews in biomedical engineering. 2019 Nov 1;13:248-60.
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Journal of Microwave Engineering and Technologies
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 5, 2024 | |
| Accepted | July 15, 2024 | |
| Published | August 14, 2024 |
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