Transfer Learning assisted PodNet for Stimulation Frequency Detection in Steady state visually evoked potential-based BCI Spellers

References

• Jalilpour S, Hajipour Sardouie S, Mijani A. A novel hybrid BCI speller based on RSVP and SSVEP paradigm. Comput Methods Programs Biomed. 2020;187:105326.
   PubMed Web of Science ®Google Scholar
• Vega-Escobar L, Castro-Ospina AE, Duque-Muñoz L. Feature extraction schemes for BCI systems 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA) 02-04 September 2015 Bogota, Colombia. 2015 (IEEE);1–6. doi:10.1109/STSIVA.2015.7330455
   Google Scholar
• Elham S, Zahra S, Ahmadreza K, et al. Classification of the EEG Evoked by Auditory Stimuli with a Periodic Carrier Frequency Coding in Order to Be Used in BCI Systems. Frontiers in Biomedical Technologies. 2016;3:548.
   Google Scholar
• Chen X, Chen Z, Gao S, et al. A high-ITR SSVEP-based BCI speller. Brain-Comput Interfaces. 2014;1(3–4):181–191.
   Google Scholar
• Mannan MMN, Kamran MA, Kang S, et al. Design Using Eye Tracking and SSVEP Brain–Computer Interface. Sensors. 2020;20(3):891.
   PubMed Web of Science ®Google Scholar
• Ajami S, Mahnam A, Abootalebi V. Development of a practical high frequency brain–computer interface based on steady-state visual evoked potentials using a single channel of EEG. Biocybernetics Biomed Eng. 2018;38(1):106–114.
   Web of Science ®Google Scholar
• Floriano A, Delisle-Rodriguez D, Diez PF, et al. Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field. Comput Methods Programs Biomed. 2020;184:105271.
   PubMed Web of Science ®Google Scholar
• Zerafa R, Camilleri T, Falzon O, et al. A comparison of a broad range of EEG acquisition devices – is there any difference for SSVEP BCIs? Brain-Comput Interfaces. 2018;5(4):121–131.
   Web of Science ®Google Scholar
• Rostami E, Ghassemi F, Tabanfar Z. Canonical Correlation Analysis of Task Related Components as a noise-resistant method in Brain-Computer Interface Speller Systems based on Steady-State Visual Evoked Potential. Biomed Signal Process Control. 2022;73:103449.
   Web of Science ®Google Scholar
• Tabanfar Z, Ghassemi F, Moradi MH. Estimating brain periodic sources activities in steady-state visual evoked potential using local Fourier independent component analysis. Biomed Signal Process Control. 2022;71:103162.
   Web of Science ®Google Scholar
• Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng. 2007;54(6):1172–1176.
   PubMed Web of Science ®Google Scholar
• Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface. J Neural Eng. 2015;12(4):046008.
   PubMed Web of Science ®Google Scholar
• Parini S, Maggi L, Turconi AC, et al. and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication. Comput Intell Neurosci. 2009;(2009):864564.
   PubMedGoogle Scholar
• Nakanishi M, Wang Y, Chen X, et al. Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis. IEEE Trans Biomed Eng. 2018;65(1):104–112.
   PubMed Web of Science ®Google Scholar
• Neghabi M, Marateb H, Mahnam A. Novel frequency-based approach for detection of steady-state visual evoked potentials for realization of practical brain computer interfaces. Brain-Comput Interfaces. 2022; 9 ;1–14.
   Web of Science ®Google Scholar
• Sun Q, Chen M, Zhang L, et al. Similarity-constrained task-related component analysis for enhancing SSVEP detection. J Neural Eng. 2021;18(4):046080.
   Web of Science ®Google Scholar
• Aznan N, Bonner S, Connolly J, et al. On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. IEEE International Conference on Systems, Man, and Cybernetics (SMC2018) 07-10 October 2018 Miyazaki, Japan, 2018. IEEE. doi:10.1109/SMC.2018.00631
   Google Scholar
• Bengio Y, Lee H. Editorial introduction to the Neural Networks special issue on Deep Learning of Representations. Neural Networks. 2015;64:1–3.
   PubMed Web of Science ®Google Scholar
• Ali N, Kyanoosh A. Introducing a Convolutional Neural Network and Visualization of its Filters for Classification of EEG Signal for SSVEP Task. Frontiers in Biomedical Technologies. 2020;7;548.
   Google Scholar
• Waytowich N, Lawhern VJ, Garcia JO, et al. Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. J Neural Eng. 2018;15(6):066031.
   PubMed Web of Science ®Google Scholar
• Xing J, Qiu S, Ma X, et al. A CNN-based comparing network for the detection of steady-state visual evoked potential responses. Neurocomputing. 2020;403:452–461.
   Web of Science ®Google Scholar
• Podmore JJ, Breckon TP, Aznan NKN, et al. On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications. IEEE Trans Neural Syst Rehabil Eng. 2019;27:611–618.
   PubMed Web of Science ®Google Scholar
• Guney O, Oblokulov M, Ozkan H. A Deep Neural Network for SSVEP-based Brain-Computer Interfaces. IEEE Trans Biomed Eng. 2021;1:515.
   Google Scholar
• Bassi PRAS, Rampazzo W, Attux R. Transfer learning and SpecAugment applied to SSVEP based BCI classification. Biomed Signal Process Control. 2021;67:102542.
   Web of Science ®Google Scholar
• Pan SJ, Yang Q. A Survey on Transfer Learning. IEEE Trans Knowledge Data Eng. 2010;22(10):1345–1359.
   Web of Science ®Google Scholar
• Wan Z, Yang R, Huang M, et al. A review on transfer learning in EEG signal analysis. Neurocomputing. 2021;421:1–14.
   Web of Science ®Google Scholar
• Caruana DLSR, Baxter J, Mitchell TM, et al., Workshop on “Learning to learn: knowledge consolidation and transfer in inductive systems”. Workshop, held at NIPS-95, Vail, CO, (1995).
   Google Scholar
• Zhang K, Robinson N, Lee S-W, et al. Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network. Neural Networks. 2021;136:1–10.
   PubMed Web of Science ®Google Scholar
• Wong C, Wang Z, Rosa A, et al. Transferring Subject-Specific Knowledge Across Stimulus Frequencies in SSVEP-Based BCIs. IEEE Trans Autom Sci Eng. 2021;1;1–12.
   Google Scholar
• Liu B, Huang X, Wang Y, et al. Database Toward SSVEP-BCI Application. Front Neurosci. 2020;14:627.
   PubMed Web of Science ®Google Scholar
• Wang Y, Chen X, Gao X, et al. A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng. 2017;25:1746–1752.
   PubMed Web of Science ®Google Scholar
• Pan SJ, Tsang IW-H, Kwok JT, et al. Domain Adaptation via Transfer Component Analysis. IEEE Trans Neural Networks. 2011;22(2):199–210.
   PubMed Web of Science ®Google Scholar
• Nakanishi M, Wang Y, Wang Y-T, et al. Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials. PLOS ONE. 2015;10:e0140703.
   PubMed Web of Science ®Google Scholar
• Gao Q, Zhang Y, Wang Z, et al. Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control. Comput Intell Neurosci. 2019;(2019):2361282.
   PubMed Web of Science ®Google Scholar
• Rostami E, Ghassemi F, Tabanfar Z. Improving the Classification of Real-World SSVEP Data in Brain-Computer Interface Speller Systems Using Deep Convolutional Neural Networks. Frontiers in Biomedical Technologies. 9(4):2022.
   Google Scholar
• Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng. 2018;15(5):056013.
   PubMed Web of Science ®Google Scholar
• Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017;38:5391–5420.
   PubMed Web of Science ®Google Scholar
• Cao T, Wang X, Wang B, et al., A high rate online SSVEP based brain-computer interface speller. International IEEE/EMBS Conference on Neural Engineering. 2011;
   Google Scholar