Single-Channel Selection for Detecting Steady-State Visual Evoked Potentials in a Brain-Computer Interface Speller
Abstract
Purpose: Brain-Computer Interface (BCI) provides a secondary communication pathway for patients with neuromuscular diseases such as amyotrophic lateral sclerosis (ALS) or brainstem stroke in which they are almost incapacitated to move or talk. BCI enacts neural oscillations to generate a command signal for machines to operate desired tasks instead of patients. Steady-State Visual Evoked Potential (SSVEP) is the brain response to a visual stimulus, with the same frequency as its eliciting signal (or its harmonics), that has been widely used in BCI environments. In order to provide a more convenient situation for BCI users, we aim to find the best single-channel EEG, which results in the highest accuracy for detecting SSVEP.
Materials and Methods: We developed a Deep Convolutional Neural Network with single-channel EEG as input to classify a 40-class SSVEP; each class represents a stimulus, which has been acquired from 35 subjects. We used 3.5 s windows of the data (Trials of 3.5 seconds length for each class) to train our model and leave-one-subject-out cross-validation for the testing.
Results: The proposed method resulted in the average classification accuracy of 74.30%±20.85 and Information Transfer Rate (ITR) of 57.51 bpm which outperforms the previous single-channel SSVEP BCIs in terms of ITR. Also, the O1 channel achieved the best performance criteria among the channels in the occipital and parietal lobes, which seems reasonable according to previous researches for finding the location of neurons, responsible for visual tasks in the brain.
Conclusion: In this study, we dedicated our efforts to reduce the number of EEG channels to a single channel while proposing a deep learning strategy for an SSVEP-based BCI speller to make it more feasible for patients whose lives are dependent on such systems. The overall results, although not ideal, open a new promising window toward a feasible BCI system.
1-Wolpaw, Jonathan R., Dennis J. McFarland, and Theresa M. Vaughan, "Brain-computer interface research at the Wadsworth Center," IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 222-226, 2000.
2-N. Birbaumer, "Breaking the silence: brain–computer interfaces (BCI) for communication and motor control," Psychophysiology, vol. 43, no. 6, pp. 517-532, 206.
3-Nijboer, Femke, E. W. Sellers, Jürgen Mellinger, Mary Ann Jordan, Tamara Matuz, Adrian Furdea, Sebastian Halder et al, "A P300-based brain–computer interface for people with
4. Conclusion
In the presented research, a CNN was developed with the aim of recognizing and classifying SSVEP in a BCI environment, with the limitation of using just a single EEG channel. For our model, we adopted time samples of EEG as inputs and got rid of transforming the data to the frequency domain and its computational cost, as opposed to several other related works [11, 18]. Moreover, the reduction of EEG channels in our study provides a more convenient and practical setting for BCI users, indeed. Lastly, the presented CNN model outperforms other classical methods for classifying SSVEP, such as Canonical Correlation Analysis and PSDA or state-of-the-art deep learning models [23, 26]. Despite this significant progress, the performance of an algorithm is highly dependent on the subjects, and its achievements extremely vary across the subjects. Hopefully, there could be other machine learning methods, such as transfer learning that may address this issue and add more generalization to these systems. Developing the proposed method using transfer learning can be the topic of future research.
amyotrophic lateral sclerosis," Clinical neurophysiology, vol. 119, no. 8, pp. 1909-1916, 2008.
4-Wolpaw, Jonathan R., Herbert Ramoser, Dennis J. McFarland, and Gert Pfurtschelle, "EEG-based communication: improved accuracy by response verificatio," IEEE transactions on Rehabilitation Engineering, vol. 6, no. 3, pp. 326-333, 1998.
5-Wang, Yijun, Xiaorong Gao, Bo Hong, Chuan Jia, and Shangkai Gao, "Brain-computer interfaces based on visual evoked potentials," IEEE Engineering in medicine and biology magazine, vol. 27, no. 5, pp. 64-71, 2008.
6-Nijholt, Anton, Desney Tan, Gert Pfurtscheller, Clemens Brunner, José del R. Millán, Brendan Allison, Bernhard Graimann, Florin Popescu, Benjamin Blankertz, and Klaus-R. Müller, "Brain-computer interfacing for intelligent systems," IEEE intelligent systems, vol. 23, no. 3, pp. 72-79, 2008.
7-Pfurtscheller, Gert, and FH Lopes Da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles," Clinical neurophysiology, vol. 110, no. 11, pp. 1842-1857, 1999.
8-C. S. Herrmann, "Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena," Experimental brain research, vol. 137, no. 3-4, pp. 346-353, 2001.
9-Wang, Yijun, Ruiping Wang, Xiaorong Gao, Bo Hong, and Shangkai Gao, "A practical VEP-based brain-computer interface," IEEE Transactions on neural systems and rehabilitation engineering, vol. 14, no. 2, pp. 234-240, 2006.
10-Müller-Putz, Gernot R., Reinhold Scherer, Christian Brauneis, and Gert Pfurtscheller., "Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components," Journal of neural engineering, vol. 2, no. 4, p. 123, 2005.
11-H. Cecotti, "A self-paced and calibration-less SSVEP-based brain–computer interface speller," EEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 2, pp. 127-133, 2010.
12-Waytowich, Nicholas, Vernon J. Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, and Jean M. Vettel, "Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials," Journal of neural engineering, vol. 15, no. 6, p. 066031, 2018.
13-Nakanishi, Masaki, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung, "A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials," PloS one, vol. 10, no. 10, 2015.
14-Wang, Yijun, Ruiping Wang, Xiaorong Gao, Bo Hong, and Shangkai Gao, "A practical VEP-based brain-computer interface," IEEE Transactions on neural systems and rehabilitation engineering, vol. 14, no. 2, pp. 234-240, 2006.
15-Cheng, Ming, Xiaorong Gao, Shangkai Gao, and Dingfeng Xu, "Design and implementation of a brain-computer interface with high transfer rates," IEEE transactions on biomedical engineering, vol. 49, no. 10, pp. 1181-1186, 2002.
16-Nakanishi, Masaki, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung, "A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials," PloS one, vol. 10, no. 10, 2015.
17-Lin, Zhonglin, Changshui Zhang, Wei Wu, and Xiaorong Gao, ""Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs," IEEE transactions on biomedical engineering, vol. 53, no. 12, pp. 2610-2614, 2006.
18-Nguyen, Trung-Hau; Wan-Young Chung, "A single-channel SSVEP-based BCI speller using deep learning," IEEE Access, vol. 7, pp. 1752-1763, 2018.
19-Lotte, Fabien, Marco Congedo, Anatole Lécuyer, Fabrice Lamarche, and Bruno Arnaldi, , "A review of classification algorithms for EEG-based brain–computer interfaces," Journal of neural engineering, vol. 4, no. 2, p. p. R1, 2007.
20-Faust, Oliver, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, and U. Rajendra Acharya, ""Deep learning for healthcare applications based on physiological signals: A review," Computer methods and programs in biomedicine, vol. 161, pp. 1-13, 2018.
21-Jamison, Keith W., Abhrajeet V. Roy, Sheng He, Stephen A. Engel, and Bin He,, "SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI," Journal of neuroscience methods, vol. 243, pp. 53-63, 2015.
22-Wolpaw, Jonathan R., Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan, "Brain–computer interfaces for communication and control," Clinical neurophysiology, vol. 113, no. 6, pp. 767-791, 2002.
23-A. T. SÖZER, "Enhanced Single Channel SSVEP Detection Method on Benchmark Dataset.," in In 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, 2018.
24-Chen, Xiaogang, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, and Shangkai Gao, "High-speed spelling with a noninvasive brain–computer interface," Proceedings of the national academy of sciences, vol. 112, no. 44, 2015.
25-Aznan, Nik Khadijah Nik, Stephen Bonner, Jason Connolly, Noura Al Moubayed, and Toby Breckon, "On the classification of SSVEP-based dry-EEG signals via convolutional neural networks," in In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2018.
26-Podmore, Joshua J., Toby P. Breckon, Nik KN Aznan, and Jason D. Connolly, "On the relative contribution of deep convolutional neural networks for SSVEP-based bio-signal decoding in BCI speller applications," EEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 4, pp. 611-618, 2019.
27-C. S. Herrmann, "Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena," Experimental brain research, vol. 137, no. 3-4, pp. 346-353, 2001.
28-Jamison, Keith W., Abhrajeet V. Roy, Sheng He, Stephen A. Engel, and Bin He, "SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI," Journal of neuroscience methods, vol. 243, pp. 53-63, 2015.
29-Diez, Pablo F., Vicente Mut, Eric Laciar, and Enrique Avila, "A comparison of monopolar and bipolar EEG recordings for SSVEP detection," in In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010.
2-N. Birbaumer, "Breaking the silence: brain–computer interfaces (BCI) for communication and motor control," Psychophysiology, vol. 43, no. 6, pp. 517-532, 206.
3-Nijboer, Femke, E. W. Sellers, Jürgen Mellinger, Mary Ann Jordan, Tamara Matuz, Adrian Furdea, Sebastian Halder et al, "A P300-based brain–computer interface for people with
4. Conclusion
In the presented research, a CNN was developed with the aim of recognizing and classifying SSVEP in a BCI environment, with the limitation of using just a single EEG channel. For our model, we adopted time samples of EEG as inputs and got rid of transforming the data to the frequency domain and its computational cost, as opposed to several other related works [11, 18]. Moreover, the reduction of EEG channels in our study provides a more convenient and practical setting for BCI users, indeed. Lastly, the presented CNN model outperforms other classical methods for classifying SSVEP, such as Canonical Correlation Analysis and PSDA or state-of-the-art deep learning models [23, 26]. Despite this significant progress, the performance of an algorithm is highly dependent on the subjects, and its achievements extremely vary across the subjects. Hopefully, there could be other machine learning methods, such as transfer learning that may address this issue and add more generalization to these systems. Developing the proposed method using transfer learning can be the topic of future research.
amyotrophic lateral sclerosis," Clinical neurophysiology, vol. 119, no. 8, pp. 1909-1916, 2008.
4-Wolpaw, Jonathan R., Herbert Ramoser, Dennis J. McFarland, and Gert Pfurtschelle, "EEG-based communication: improved accuracy by response verificatio," IEEE transactions on Rehabilitation Engineering, vol. 6, no. 3, pp. 326-333, 1998.
5-Wang, Yijun, Xiaorong Gao, Bo Hong, Chuan Jia, and Shangkai Gao, "Brain-computer interfaces based on visual evoked potentials," IEEE Engineering in medicine and biology magazine, vol. 27, no. 5, pp. 64-71, 2008.
6-Nijholt, Anton, Desney Tan, Gert Pfurtscheller, Clemens Brunner, José del R. Millán, Brendan Allison, Bernhard Graimann, Florin Popescu, Benjamin Blankertz, and Klaus-R. Müller, "Brain-computer interfacing for intelligent systems," IEEE intelligent systems, vol. 23, no. 3, pp. 72-79, 2008.
7-Pfurtscheller, Gert, and FH Lopes Da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles," Clinical neurophysiology, vol. 110, no. 11, pp. 1842-1857, 1999.
8-C. S. Herrmann, "Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena," Experimental brain research, vol. 137, no. 3-4, pp. 346-353, 2001.
9-Wang, Yijun, Ruiping Wang, Xiaorong Gao, Bo Hong, and Shangkai Gao, "A practical VEP-based brain-computer interface," IEEE Transactions on neural systems and rehabilitation engineering, vol. 14, no. 2, pp. 234-240, 2006.
10-Müller-Putz, Gernot R., Reinhold Scherer, Christian Brauneis, and Gert Pfurtscheller., "Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components," Journal of neural engineering, vol. 2, no. 4, p. 123, 2005.
11-H. Cecotti, "A self-paced and calibration-less SSVEP-based brain–computer interface speller," EEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 2, pp. 127-133, 2010.
12-Waytowich, Nicholas, Vernon J. Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, and Jean M. Vettel, "Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials," Journal of neural engineering, vol. 15, no. 6, p. 066031, 2018.
13-Nakanishi, Masaki, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung, "A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials," PloS one, vol. 10, no. 10, 2015.
14-Wang, Yijun, Ruiping Wang, Xiaorong Gao, Bo Hong, and Shangkai Gao, "A practical VEP-based brain-computer interface," IEEE Transactions on neural systems and rehabilitation engineering, vol. 14, no. 2, pp. 234-240, 2006.
15-Cheng, Ming, Xiaorong Gao, Shangkai Gao, and Dingfeng Xu, "Design and implementation of a brain-computer interface with high transfer rates," IEEE transactions on biomedical engineering, vol. 49, no. 10, pp. 1181-1186, 2002.
16-Nakanishi, Masaki, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung, "A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials," PloS one, vol. 10, no. 10, 2015.
17-Lin, Zhonglin, Changshui Zhang, Wei Wu, and Xiaorong Gao, ""Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs," IEEE transactions on biomedical engineering, vol. 53, no. 12, pp. 2610-2614, 2006.
18-Nguyen, Trung-Hau; Wan-Young Chung, "A single-channel SSVEP-based BCI speller using deep learning," IEEE Access, vol. 7, pp. 1752-1763, 2018.
19-Lotte, Fabien, Marco Congedo, Anatole Lécuyer, Fabrice Lamarche, and Bruno Arnaldi, , "A review of classification algorithms for EEG-based brain–computer interfaces," Journal of neural engineering, vol. 4, no. 2, p. p. R1, 2007.
20-Faust, Oliver, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, and U. Rajendra Acharya, ""Deep learning for healthcare applications based on physiological signals: A review," Computer methods and programs in biomedicine, vol. 161, pp. 1-13, 2018.
21-Jamison, Keith W., Abhrajeet V. Roy, Sheng He, Stephen A. Engel, and Bin He,, "SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI," Journal of neuroscience methods, vol. 243, pp. 53-63, 2015.
22-Wolpaw, Jonathan R., Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan, "Brain–computer interfaces for communication and control," Clinical neurophysiology, vol. 113, no. 6, pp. 767-791, 2002.
23-A. T. SÖZER, "Enhanced Single Channel SSVEP Detection Method on Benchmark Dataset.," in In 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, 2018.
24-Chen, Xiaogang, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, and Shangkai Gao, "High-speed spelling with a noninvasive brain–computer interface," Proceedings of the national academy of sciences, vol. 112, no. 44, 2015.
25-Aznan, Nik Khadijah Nik, Stephen Bonner, Jason Connolly, Noura Al Moubayed, and Toby Breckon, "On the classification of SSVEP-based dry-EEG signals via convolutional neural networks," in In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2018.
26-Podmore, Joshua J., Toby P. Breckon, Nik KN Aznan, and Jason D. Connolly, "On the relative contribution of deep convolutional neural networks for SSVEP-based bio-signal decoding in BCI speller applications," EEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 4, pp. 611-618, 2019.
27-C. S. Herrmann, "Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena," Experimental brain research, vol. 137, no. 3-4, pp. 346-353, 2001.
28-Jamison, Keith W., Abhrajeet V. Roy, Sheng He, Stephen A. Engel, and Bin He, "SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI," Journal of neuroscience methods, vol. 243, pp. 53-63, 2015.
29-Diez, Pablo F., Vicente Mut, Eric Laciar, and Enrique Avila, "A comparison of monopolar and bipolar EEG recordings for SSVEP detection," in In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010.
| Files | ||
| Issue | Vol 8 No 3 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v8i3.7109 | |
| Keywords | ||
| Brain-Computer Interface Speller Steady-State Visual Evoked Potential Deep Learning Convolutional Neural Networks Single-Channel Electroencephalogram | ||
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How to Cite
1.
Saffari F, Khadem A. Single-Channel Selection for Detecting Steady-State Visual Evoked Potentials in a Brain-Computer Interface Speller. Frontiers Biomed Technol. 2021;8(3):155-160.
