P300 Detection Using Ensemble of SVM for Brain-Computer Interface Application
Abstract
Ahstract-This study provides a novel brain-computer interface (BCI) approach for character recognition. The character recognition task is a two class classification problem. The key objective of the character recognition is to detect the P300 signal from the set of electroencephalogram (EEG) signals. The character is predicted from the detected P300 signal and row/column information of the oddball paradigm. The signal-to-noise ratio (SNR) of the electroencephalogram (EEG) signal is low. Ensemble of classifier is used to reduce the classifier variabilIty and enhance the SNR of the acquired signal. Here ensemble of SVMs (ESVMs) is used as classifier. The distribution of dataset is imbalanced due to its paradigm. A novel approach is applied in this work to balance the dataset. The proposed algorithm is evaluated on dataset IIb of BCI Competition II and dataset II of the BCI Competition III. The experimental results show that the proposed method outperforms the state-of-the-art character recognition performance.
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A traditional metric of evaluating the accuracy of symbol recognition directly compares correctly recognized symbols at repetitions 5, 10, and 15, as used in [43] and [44].
“A traditional metric of evaluating the accuracy of symbol recognition is to directly compare the correctly recognized symbols at repetitions 5, 10 and 15, respectively as used in [43] and [44].”
From ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement (2023) · cites this paper as [43]
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
The Nature of Statistical Learning Theory
Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 4,097 · see citing works
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228 · see citing works
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143 · see citing works
The BCI competition III: validating alternative approaches to actual BCI problems
Blankertz, B., Muller, K.-R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., Pfurtscheller, G., Millan, J. R., Schroder, M., & Birbaumer, N. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875642 · Cited by 927 · see citing works
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
Cecotti, H., & Graser, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.125 · Cited by 778 · see citing works
Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., et al. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826692 · Cited by 670 · see citing works
BCI competition III: Dataset II—Ensemble of SVMs for BCI P300 speller
Rakotomamonjy, A., & Guigue, V. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.915728 · Cited by 512 · see citing works
BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm
Kaper, M., Meinicke, P., Großekathöfer, U., Lingner, T., & Ritter, H. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826698 · Cited by 496 · see citing works
Deep learning based on Batch Normalization for P300 signal detection
Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., & Li, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.039 · Cited by 230 · see citing works
A regularized discriminative framework for EEG analysis with application to brain–computer interface
Tomioka, R., & Müller, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.07.045 · Cited by 167 · see citing works
Grandvalet, Y. (2004) · Machine Learning · doi:10.1023/b:mach.0000027783.34431.42 · Cited by 133 · see citing works
HMMs and Coupled HMMs for multi-channel EEG classification
Zhong, S., & Ghosh, J. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2002.1007657 · Cited by 121 · see citing works
Noninvasive Brain–Computer Interfaces for Augmentative and Alternative Communication
Akçakaya, M., Peters, B., Moghadamfalahi, M., Mooney, A. R., Orhan, U., Oken, B., Deniz Erdogmus, B., & Fried-Oken, M. B. (2014) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2295097 · Cited by 102 · see citing works
Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set
Kee, C.-Y., Ponnambalam, S. G., & Loo, C. K. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.057 · Cited by 83 · see citing works
Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs
Yu, T., Yu, Z., Gu, Z., & Li, Y. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2413943 · Cited by 68 · see citing works
P300 Detection with Brain–Computer Interface Application Using PCA and Ensemble of Weighted SVMs
Kundu, S., & Ari, S. (2017) · IETE Journal of Research · doi:10.1080/03772063.2017.1355271 · Cited by 58 · see citing works
Self-citationAn Online Semi-supervised Brain–Computer Interface
Gu, Z., Yu, Z., Shen, Z., & Li, Y. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2261994 · Cited by 53 · see citing works
Wavelets and ensemble of FLDs for P300 classification
Salvaris, M., & Sepulveda, F. (2009) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2009.5109302 · Cited by 44 · see citing works
An efficient P300 speller system for Brain-Computer Interface
Chaurasiya, R. K., Londhe, N. D., & Ghosh, S. (2015) · International Conference on Signal Processing, Computing and Control (ISPCC) · doi:10.1109/ispcc.2015.7374998 · Cited by 25 · see citing works
Averaging techniques for single-trial analysis of oddball event-related potentials
Liang, N., & Bougrain, L. (2008) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 23 · see citing works
Score normalization of ensemble SVMs for brain-computer interface P300 speller
Kundu, S., & Ari, S. (2017) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2017.8204119 · Cited by 11 · see citing works
Self-citationUmut, Ç., & Arıca, S. (2010) · IEEE Convention of Electrical and Electronics Engineers in Israel · doi:10.1109/eeei.2010.5661941 · Cited by 3 · see citing works
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Wang, Z., Chen, C., Li, J., Wan, F., Sun, Y., & Wang, H. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3237319 · Cited by 75 · see citing works
“A traditional metric of evaluating the accuracy of symbol recognition is to directly compare the correctly recognized symbols at repetitions 5, 10 and 15, respectively as used in [43] and [44].”
P300 based character recognition using sparse autoencoder with ensemble of SVMs
Kundu, S., & Ari, S. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.08.001 · Cited by 21 · see citing works
Self-citationA Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection
Wang, H., Wang, Z., Sun, Y., Yuan, Z., Xu, T., & Li, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3415474 · Cited by 20 · see citing works
“Traditional machine learning methods, such as support vector machines (SVM), discriminant analysis, and common spatial pattern algorithms, were first applied [42], [43], [46], [49], [50].”
MethodFusion of Convolutional Neural Networks for P300 Based Character Recognition
Kundu, S., & Ari, S. (2019) · doi:10.1109/icit48102.2019.00034 · Cited by 12 · see citing works
Self-citationKaur, M., Upadhyay, R., & Kumar, V. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22866 · Cited by 5 · see citing works
Shukla, P. K., Cecotti, H., & Meena, Y. K. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.11169725 · Cited by 1 · see citing works
Havaei, P., Mahmoudzadeh, E., & Zekri, M. (2021) · Iranian Conference on Electrical Engineering · doi:10.1109/icee52715.2021.9544164
A Robust Classification Approach for Character Detection Using P300-Based Brain-Computer Interface
Mehta, D. H., Sarin, M., Shukla, P. K., Verma, S., & Chaurasiya, R. K. (2022) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-6684-3947-0.ch010
- Authors
- Kundu, S., & Ari, S.
- Affiliations
- National Institute of Technology Rourkela (IN)
- Venue
- 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-5
- OpenAlex
- W2884960676
- Cited by
- 8
- References
- 25
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 1 · 2021: 1 · 2019: 2
- FWCI
- 1.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computing, Communication and Networking Technologies (ICCCNT) (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- P300 detection, brain-computer interface, character recognition, EEG signals, oddball paradigm, signal-to-noise ratio improvement, class imbalance, data balancing, BCI competition datasets
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