An EEG-Based Brain-Computer Interface Using Spectral Correlation Function
Brain-Computer Interface (BCI) is a promising technique because of its wide variety of applications, from treating cognition in humans to person authentication. Brain signals can be transmitted straight to a prosthetic device from the BCI system, without the need for nerve or muscle activity. For ac...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10082936/ |
| _version_ | 1797849301620097024 |
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| author | Nazila Panahi Mehdi Chehel Amirani Morteza Valizadeh |
| author_facet | Nazila Panahi Mehdi Chehel Amirani Morteza Valizadeh |
| author_sort | Nazila Panahi |
| collection | DOAJ |
| description | Brain-Computer Interface (BCI) is a promising technique because of its wide variety of applications, from treating cognition in humans to person authentication. Brain signals can be transmitted straight to a prosthetic device from the BCI system, without the need for nerve or muscle activity. For accurately identifying the transmitted signals at the prosthetic device, considering the nature of the Electroencephalography (EEG) signal, and extracting the most informative features are effective keys. In this paper, we studied the cyclostationarity of the Slow Cortical Potential (SCP) EEG signals for BCI applications, following our previous studies. Cyclostationary analysis reveals the hidden periodicity in the signal and provides a second-order statistical description in the frequency domain. We used the FFT Accumulation Method (FAM), an effective computational algorithm, to extract the features of the Spectral Correlation Function (SCF). The features are classified using SVM RBF, SVM polynomial, and K-Nearest Neighbor classifiers, and they are considered with different pre-processing. Our research indicates that the SCP EEG signal has cyclostationary properties and this idea is applied to the BCIs as well. The classification accuracy on the BCI Competition 2003 dataset Ia’s increased considerably, by spotting the intrinsic correlation between just two EEG signals. |
| first_indexed | 2024-04-09T18:41:38Z |
| format | Article |
| id | doaj.art-56b80f4fa8e84f44ab6289639611add6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T18:41:38Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-56b80f4fa8e84f44ab6289639611add62023-04-10T23:01:46ZengIEEEIEEE Access2169-35362023-01-0111332363324710.1109/ACCESS.2023.326246510082936An EEG-Based Brain-Computer Interface Using Spectral Correlation FunctionNazila Panahi0https://orcid.org/0000-0003-2846-4184Mehdi Chehel Amirani1https://orcid.org/0000-0002-5179-9831Morteza Valizadeh2https://orcid.org/0000-0003-2204-3303Department of Electrical Engineering, Urmia University, Urmia, IranDepartment of Electrical Engineering, Urmia University, Urmia, IranDepartment of Electrical Engineering, Urmia University, Urmia, IranBrain-Computer Interface (BCI) is a promising technique because of its wide variety of applications, from treating cognition in humans to person authentication. Brain signals can be transmitted straight to a prosthetic device from the BCI system, without the need for nerve or muscle activity. For accurately identifying the transmitted signals at the prosthetic device, considering the nature of the Electroencephalography (EEG) signal, and extracting the most informative features are effective keys. In this paper, we studied the cyclostationarity of the Slow Cortical Potential (SCP) EEG signals for BCI applications, following our previous studies. Cyclostationary analysis reveals the hidden periodicity in the signal and provides a second-order statistical description in the frequency domain. We used the FFT Accumulation Method (FAM), an effective computational algorithm, to extract the features of the Spectral Correlation Function (SCF). The features are classified using SVM RBF, SVM polynomial, and K-Nearest Neighbor classifiers, and they are considered with different pre-processing. Our research indicates that the SCP EEG signal has cyclostationary properties and this idea is applied to the BCIs as well. The classification accuracy on the BCI Competition 2003 dataset Ia’s increased considerably, by spotting the intrinsic correlation between just two EEG signals.https://ieeexplore.ieee.org/document/10082936/Brain-computer interface (BCI)cyclostationary signalspectral correlation function (SCF)electroencephalography (EEG) classificationBCI competition 2003 dataset Ia |
| spellingShingle | Nazila Panahi Mehdi Chehel Amirani Morteza Valizadeh An EEG-Based Brain-Computer Interface Using Spectral Correlation Function IEEE Access Brain-computer interface (BCI) cyclostationary signal spectral correlation function (SCF) electroencephalography (EEG) classification BCI competition 2003 dataset Ia |
| title | An EEG-Based Brain-Computer Interface Using Spectral Correlation Function |
| title_full | An EEG-Based Brain-Computer Interface Using Spectral Correlation Function |
| title_fullStr | An EEG-Based Brain-Computer Interface Using Spectral Correlation Function |
| title_full_unstemmed | An EEG-Based Brain-Computer Interface Using Spectral Correlation Function |
| title_short | An EEG-Based Brain-Computer Interface Using Spectral Correlation Function |
| title_sort | eeg based brain computer interface using spectral correlation function |
| topic | Brain-computer interface (BCI) cyclostationary signal spectral correlation function (SCF) electroencephalography (EEG) classification BCI competition 2003 dataset Ia |
| url | https://ieeexplore.ieee.org/document/10082936/ |
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