Recognition of Frequencies of Short-Time SSVEP Signals Utilizing an SSCCA-Based Spatio-Spectral Feature Fusion Framework
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909585751670784 |
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| author | Bashar, Saif Nira, Samia Nasir Mahmood, Shabbir Kabir, Md. Humaun Roy, Sujit Farhana, Iffat |
| author_facet | Bashar, Saif Nira, Samia Nasir Mahmood, Shabbir Kabir, Md. Humaun Roy, Sujit Farhana, Iffat |
| contents | A brain-computer interface (BCI) facilitates direct communication between the brain and external equipment through EEG, which is preferred for its superior temporal resolution. Among EEG techniques, the steady-state visual evoked potential (SSVEP) is favored due to its robust signal-to-noise ratio, minimal training demands, and elevated information transmission rate. Frequency detection in SSVEP-based brain-computer interfaces commonly employs canonical correlation analysis (CCA). SSCCA (spatio-spectral canonical correlation analysis) augments CCA by refining spatial filtering. This paper presents a multistage feature fusion methodology for short-duration SSVEP frequency identification, employing SSCCA with template signals derived via leave-one-out cross-validation (LOOCV). A filterbank generates bandpass filters for stimulus frequencies and their harmonics, whereas SSCCA calculates correlation coefficients between subbands and templates. Two phases of non-linear weighting amalgamate these coefficients to discern the target stimulus. This multistage methodology surpasses traditional techniques, attaining a accuracy of 94.5%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_14269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recognition of Frequencies of Short-Time SSVEP Signals Utilizing an SSCCA-Based Spatio-Spectral Feature Fusion Framework Bashar, Saif Nira, Samia Nasir Mahmood, Shabbir Kabir, Md. Humaun Roy, Sujit Farhana, Iffat Human-Computer Interaction A brain-computer interface (BCI) facilitates direct communication between the brain and external equipment through EEG, which is preferred for its superior temporal resolution. Among EEG techniques, the steady-state visual evoked potential (SSVEP) is favored due to its robust signal-to-noise ratio, minimal training demands, and elevated information transmission rate. Frequency detection in SSVEP-based brain-computer interfaces commonly employs canonical correlation analysis (CCA). SSCCA (spatio-spectral canonical correlation analysis) augments CCA by refining spatial filtering. This paper presents a multistage feature fusion methodology for short-duration SSVEP frequency identification, employing SSCCA with template signals derived via leave-one-out cross-validation (LOOCV). A filterbank generates bandpass filters for stimulus frequencies and their harmonics, whereas SSCCA calculates correlation coefficients between subbands and templates. Two phases of non-linear weighting amalgamate these coefficients to discern the target stimulus. This multistage methodology surpasses traditional techniques, attaining a accuracy of 94.5%. |
| title | Recognition of Frequencies of Short-Time SSVEP Signals Utilizing an SSCCA-Based Spatio-Spectral Feature Fusion Framework |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.14269 |