Research Article
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Comparison of Deep Learning and Traditional Machine Learning Classification Performance in a SSVEP Based Brain Computer Interface

Year 2022, Volume: 10 Issue: 3, 347 - 355, 30.07.2022
https://doi.org/10.17694/bajece.1088353

Abstract

Brain-computer interfaces (BCIs) offer a very high potential to help those who cannot use their organs properly. In the literature, many electroencephalogram based BCIs exist. Steady state visual evoked potential (SSVEP) based BCIs provide relatively higher accuracy values which make them very popular in BCI research. Recently, deep learning (DL) based methods have been used in electroencephalogram classification problems and they had superior performance over traditional machine learning (ML) methods, which require feature extraction step. This study aimed at comparing the performance of DL and traditional ML based classification performance in terms of stimuli duration, number of channels, and number of trials in an SSVEP based BCI experiment. In the traditional approach canonical correlation analysis method was used for the feature extraction and then three well-known classifiers were used for classification. In DL-based classification, spatio-spectral decomposition (SSD) method was integrated as a preprocessing step to extract oscillatory signals in the frequency band of interest with a convolutional neural network structure. Obtained offline classification results show that proposed DL approach could generate better accuracy values than traditional ML-based methods for short time segments (< 1 s). Besides, use of SSD as a preprocessing step increased the accuracy of DL classification. Superior performance of proposed SSD based DL approach over the traditional ML methods in short trials shows the feasibility of this approach in future BCI designs. Similar approach can be used in other fields where there are oscillatory activity in the recorded signals.

References

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  • W. Nan et al., “A comparison of minimum energy combination and canonical correlation analysis for SSVEP detection,” 2011 5th International IEEE/EMBS Conference on Neural Engineering. pp. 469–472, 2011.
  • M. Simões et al., “BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces ,” Frontiers in Neuroscience , vol. 14. 2020.
  • A. Barachant and R. Cycon, Pushing the limits of BCI accuracy: Winning solution of the Grasp & Lift EEG challenge. 2016.
  • B. Zang, Y. Lin, Z. Liu, and X. Gao, “A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs.,” J. Neural Eng., vol. 18, no. 4, Aug. 2021.
  • O. B. Guney, M. Oblokulov, and H. Ozkan, “A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces,” IEEE Trans. Biomed. Eng., vol. 69, no. 2, pp. 932–944, 2022.
  • S. Tang, S. Yuan, and Y. Zhu, “Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery,” IEEE Access, vol. 8, pp. 149487–149496, 2020.
  • V. V Nikulin, G. Nolte, and G. Curio, “A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition.,” Neuroimage, vol. 55, no. 4, pp. 1528–1535, Apr. 2011.
  • T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad, “A review of channel selection algorithms for EEG signal processing,” EURASIP J. Adv. Signal Process., vol. 2015, no. 1, p. 66, Aug. 2015.
  • Y. Wang, S. Gao, and X. Gao, “Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface.,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 5, pp. 5392–5395, 2005.
  • M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, “EEG channel selection using decision tree in brain-computer interface,” in Proceedings of the Second APSIPA Annual Summit and Conference, Biopolis, Singapore, 14-17 December, 2010, pp. 225–230.
  • M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, “Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI,” IEEE Trans. Biomed. Eng., vol. 58, no. 6, pp. 1865–1873, 2011.
  • J. K. Feng et al., “An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System,” Comput. Intell. Neurosci., vol. 2019, p. 8068357, May 2019.
  • J. Zhang, C. Yan, L. Cao, and X. Gong, “Optimal channel set selection for SSVEP-based BCI using spatial temporal correlation,” in 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017, pp. 2038–2042.
  • L. Meng, J. Jin, and X. Wang, “A comparison of three electrode channels selection methods applied to SSVEP BCI,” in 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), 2011, vol. 1, pp. 584–587.
  • E. Webster, H. Habibzadeh, J. J. S. Norton, T. M. Vaughan, and T. Soyata, “An Unsupervised Channel-Selection Method for SSVEP-based BCI Systems,” in 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2018, pp. 626–632.
  • S. N. Carvalho et al., “Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs,” Biomed. Signal Process. Control, vol. 21, pp. 34–42, 2015.
  • Y. Zhang, D. Guo, P. Xu, Y. Zhang, and D. Yao, “Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index,” Cogn. Neurodyn., vol. 10, no. 6, pp. 505–511, Dec. 2016.
  • M. Schroder, M. Bogdan, T. Hinterberger, and N. Birbaumer, “Automated EEG feature selection for brain computer interfaces,” in First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings., 2003, pp. 626–629.
  • D. A. Peterson, J. N. Knight, M. J. Kirby, C. W. Anderson, and M. H. Thaut, “Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface,” EURASIP J. Adv. Signal Process., vol. 2005, no. 19, p. 218613, 2005.
  • L. Wang, D. Han, B. Qian, Z. Zhang, Z. Zhang, and Z. Liu, “The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli.,” Brain Sci., vol. 10, no. 9, Sep. 2020.
  • Z. Işcan and V. V. Nikulin, “Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations,” PLoS One, vol. 13, no. 1, 2018.
  • A. Schlögl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, and G. Pfurtscheller, “A fully automated correction method of {EOG} artifacts in {EEG} recordings,” Clin. Neurophysiol., vol. 118, no. 1, pp. 98–104, 2007.
  • D. Ibáñez-Soria, A. Soria-Frisch, J. Garcia-Ojalvo, and G. Ruffini, “Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks,” PLoS One, vol. 14, no. 7, p. e0218771, Jul. 2019.
  • W. M. Perlstein, M. A. Cole, M. Larson, K. Kelly, P. Seignourel, and A. Keil, “Steady-state visual evoked potentials reveal frontally-mediated working memory activity in humans.,” Neurosci. Lett., vol. 342, no. 3, pp. 191–195, May 2003.
  • G. Y. Bin, X. R. Gao, Z. Yan, B. Hong, and S. K. Gao, “An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method,” J Neural Eng., vol. 6, 2009.
  • Z. Işcan and Z. Dokur, “A novel steady-state visually evoked potential-based brain-computer interface design: Character Plotter,” Biomedical Signal Processing and Control, 2013.
  • T. Bender, T. W. Kjaer, C. E. Thomsen, H. B. D. Sorensen, and S. Puthusserypady, “Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training,” 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 4279–4282, 2013.
  • N.-S. Kwak, K.-R. Muller, and S.-W. Lee, “A lower limb exoskeleton control system based on steady state visual evoked potentials.,” J. Neural Eng., vol. 12, no. 5, p. 56009, Oct. 2015.
  • S. M. T. Muller, P. F. Diez, T. F. Bastos-Filho, M. Sarcinelli-Filho, V. Mut, and E. Laciar, “SSVEP-BCI implementation for 37-40 Hz frequency range.,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2011, pp. 6352–6355, 2011.
  • M. Nakanishi, Y. Wang, Y.-T. Wang, and T.-P. Jung, “A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol. 10, no. 10, p. e0140703, Oct. 2015.
Year 2022, Volume: 10 Issue: 3, 347 - 355, 30.07.2022
https://doi.org/10.17694/bajece.1088353

Abstract

References

  • J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol., vol. 113, 2002.
  • M. Sengelmann, A. K. Engel, and A. Maye, “Maximizing Information Transfer in SSVEP-Based Brain–Computer Interfaces,” IEEE Trans. Biomed. Eng., vol. 64, no. 2, pp. 381–394, 2017.
  • X. Chen, Y. Wang, M. Nakanishi, X. Gao, T.-P. Jung, and S. Gao, “High-speed spelling with a noninvasive brain–computer interface,” Proc. Natl. Acad. Sci. , vol. 112, no. 44, pp. E6058–E6067, Nov. 2015.
  • Z. Yan, X. Gao, G. Bin, B. Hong, and S. Gao, “A half-field stimulation pattern for SSVEP-based brain-computer interface.,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2009, pp. 6461–6464, 2009.
  • W. Nan et al., “A comparison of minimum energy combination and canonical correlation analysis for SSVEP detection,” 2011 5th International IEEE/EMBS Conference on Neural Engineering. pp. 469–472, 2011.
  • M. Simões et al., “BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces ,” Frontiers in Neuroscience , vol. 14. 2020.
  • A. Barachant and R. Cycon, Pushing the limits of BCI accuracy: Winning solution of the Grasp & Lift EEG challenge. 2016.
  • B. Zang, Y. Lin, Z. Liu, and X. Gao, “A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs.,” J. Neural Eng., vol. 18, no. 4, Aug. 2021.
  • O. B. Guney, M. Oblokulov, and H. Ozkan, “A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces,” IEEE Trans. Biomed. Eng., vol. 69, no. 2, pp. 932–944, 2022.
  • S. Tang, S. Yuan, and Y. Zhu, “Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery,” IEEE Access, vol. 8, pp. 149487–149496, 2020.
  • V. V Nikulin, G. Nolte, and G. Curio, “A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition.,” Neuroimage, vol. 55, no. 4, pp. 1528–1535, Apr. 2011.
  • T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad, “A review of channel selection algorithms for EEG signal processing,” EURASIP J. Adv. Signal Process., vol. 2015, no. 1, p. 66, Aug. 2015.
  • Y. Wang, S. Gao, and X. Gao, “Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface.,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 5, pp. 5392–5395, 2005.
  • M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, “EEG channel selection using decision tree in brain-computer interface,” in Proceedings of the Second APSIPA Annual Summit and Conference, Biopolis, Singapore, 14-17 December, 2010, pp. 225–230.
  • M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, “Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI,” IEEE Trans. Biomed. Eng., vol. 58, no. 6, pp. 1865–1873, 2011.
  • J. K. Feng et al., “An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System,” Comput. Intell. Neurosci., vol. 2019, p. 8068357, May 2019.
  • J. Zhang, C. Yan, L. Cao, and X. Gong, “Optimal channel set selection for SSVEP-based BCI using spatial temporal correlation,” in 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017, pp. 2038–2042.
  • L. Meng, J. Jin, and X. Wang, “A comparison of three electrode channels selection methods applied to SSVEP BCI,” in 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), 2011, vol. 1, pp. 584–587.
  • E. Webster, H. Habibzadeh, J. J. S. Norton, T. M. Vaughan, and T. Soyata, “An Unsupervised Channel-Selection Method for SSVEP-based BCI Systems,” in 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2018, pp. 626–632.
  • S. N. Carvalho et al., “Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs,” Biomed. Signal Process. Control, vol. 21, pp. 34–42, 2015.
  • Y. Zhang, D. Guo, P. Xu, Y. Zhang, and D. Yao, “Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index,” Cogn. Neurodyn., vol. 10, no. 6, pp. 505–511, Dec. 2016.
  • M. Schroder, M. Bogdan, T. Hinterberger, and N. Birbaumer, “Automated EEG feature selection for brain computer interfaces,” in First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings., 2003, pp. 626–629.
  • D. A. Peterson, J. N. Knight, M. J. Kirby, C. W. Anderson, and M. H. Thaut, “Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface,” EURASIP J. Adv. Signal Process., vol. 2005, no. 19, p. 218613, 2005.
  • L. Wang, D. Han, B. Qian, Z. Zhang, Z. Zhang, and Z. Liu, “The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli.,” Brain Sci., vol. 10, no. 9, Sep. 2020.
  • Z. Işcan and V. V. Nikulin, “Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations,” PLoS One, vol. 13, no. 1, 2018.
  • A. Schlögl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, and G. Pfurtscheller, “A fully automated correction method of {EOG} artifacts in {EEG} recordings,” Clin. Neurophysiol., vol. 118, no. 1, pp. 98–104, 2007.
  • D. Ibáñez-Soria, A. Soria-Frisch, J. Garcia-Ojalvo, and G. Ruffini, “Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks,” PLoS One, vol. 14, no. 7, p. e0218771, Jul. 2019.
  • W. M. Perlstein, M. A. Cole, M. Larson, K. Kelly, P. Seignourel, and A. Keil, “Steady-state visual evoked potentials reveal frontally-mediated working memory activity in humans.,” Neurosci. Lett., vol. 342, no. 3, pp. 191–195, May 2003.
  • G. Y. Bin, X. R. Gao, Z. Yan, B. Hong, and S. K. Gao, “An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method,” J Neural Eng., vol. 6, 2009.
  • Z. Işcan and Z. Dokur, “A novel steady-state visually evoked potential-based brain-computer interface design: Character Plotter,” Biomedical Signal Processing and Control, 2013.
  • T. Bender, T. W. Kjaer, C. E. Thomsen, H. B. D. Sorensen, and S. Puthusserypady, “Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training,” 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 4279–4282, 2013.
  • N.-S. Kwak, K.-R. Muller, and S.-W. Lee, “A lower limb exoskeleton control system based on steady state visual evoked potentials.,” J. Neural Eng., vol. 12, no. 5, p. 56009, Oct. 2015.
  • S. M. T. Muller, P. F. Diez, T. F. Bastos-Filho, M. Sarcinelli-Filho, V. Mut, and E. Laciar, “SSVEP-BCI implementation for 37-40 Hz frequency range.,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2011, pp. 6352–6355, 2011.
  • M. Nakanishi, Y. Wang, Y.-T. Wang, and T.-P. Jung, “A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol. 10, no. 10, p. e0140703, Oct. 2015.
There are 34 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Araştırma Articlessi
Authors

Zafer İşcan 0000-0001-9832-6591

Publication Date July 30, 2022
Published in Issue Year 2022 Volume: 10 Issue: 3

Cite

APA İşcan, Z. (2022). Comparison of Deep Learning and Traditional Machine Learning Classification Performance in a SSVEP Based Brain Computer Interface. Balkan Journal of Electrical and Computer Engineering, 10(3), 347-355. https://doi.org/10.17694/bajece.1088353

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