A convolutional deep learning framework for classification of EEG signals
محورهای موضوعی : Journal of Computer & RoboticsFarzaneh Latifi 1 , Rahil Hosseini 2 , Arash Sharifi 3 , Majid Sorouri 4
1 - a Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - b Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
3 - a Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - a Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Brain-Compute Interface (BCI), Electroencephalogram (EEG), Spelling, Classification, Convolutional Neural Network (CNN),
چکیده مقاله :
A brain-computer interface (BCI) is a form of assistive technology that facilitates communication between users and machines by interpreting brain signals. The P300 wave, an event-related potential (ERP) in oddball paradigms, is generated approximately 300 milliseconds after the presentation of the target stimulus selected by the user in the brain. Accurate recognition of these waves in a P300 spelling system enables the user to write letters. Classification of P300 waves in an EEG-based spelling system faces several challenges, including accurate detection of P300 waves and handling the high dimensionality of these signals. This study presents a Convolutional Deep Learning Framework (CDLF) for character recognition using EEG signals. The proposed model uses CNN with a one-dimensional kernel to extract features over time. The proposed model was applied to two public datasets: BCI Competition III dataset II and BCI Competition II dataset IIb. The proposed model showed an average character recognition rate of 95% at epoch 15 for BCI Competition III dataset II and 100% at epoch 15 for BCI Competition II dataset IIb, without using any feature and channel selection methods before classification. The proposed model is promising for brain-computer interface classification applications in the spelling domain.
A brain-computer interface (BCI) is a form of assistive technology that facilitates communication between users and machines by interpreting brain signals. The P300 wave, an event-related potential (ERP) in oddball paradigms, is generated approximately 300 milliseconds after the presentation of the target stimulus selected by the user in the brain. Accurate recognition of these waves in a P300 spelling system enables the user to write letters. Classification of P300 waves in an EEG-based spelling system faces several challenges, including accurate detection of P300 waves and handling the high dimensionality of these signals. This study presents a Convolutional Deep Learning Framework (CDLF) for character recognition using EEG signals. The proposed model uses CNN with a one-dimensional kernel to extract features over time. The proposed model was applied to two public datasets: BCI Competition III dataset II and BCI Competition II dataset IIb. The proposed model showed an average character recognition rate of 95% at epoch 15 for BCI Competition III dataset II and 100% at epoch 15 for BCI Competition II dataset IIb, without using any feature and channel selection methods before classification. The proposed model is promising for brain-computer interface classification applications in the spelling domain.
[1] G. Zhang, V. Davoodnia, A. Sepas-Moghaddam, Y. Zhang and A. Etemad, "Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network," in IEEE Sensors Journal, vol. 20, no. 6, pp. 3113-3122, 15 March15, 2020.
[2] V. Martinez-Cagigal, E. Santamaria-Vazquez, and R. Hornero, “Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms,” Applied Soft Computing, vol. 115, 2022.
[3] H. Cecotti and A. Graser, "Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 3, pp. 433-445, March, 2011.
[4] Y. Yu, Y. Liu, E. Yin, J. Jiang, Z. Zhou and D. Hu, “An Asynchronous Hybrid Spelling Approach Based on EEG–EOG Signals for Chinese Character Input,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1292-1302, June 2019.
[5] G. L. Du, Z. Wang, C. Li and P. X. Liu, "A TSK-type Convolutional Recurrent Fuzzy Network for Predicting Driving Fatigue," in IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2100-2111, Aug. 2021.
[6] B. Perseh, and A.R. Sharafat, “An efficient P300-based BCI using wavelet features and IBPSO-based channel selection,” J. Med. Signals Sens., vol. 2, no. 3, pp. 128–143, 2012.
[7] J. Jin, B.Z. Allison, C. Brunner, B. Wang, X. Wang, J. Zhang, C. Neuper, and G. Pfurtscheller, “P300 Chinese input system based on Bayesian LDA,” Biomed. Tech., vol. 55, no. 1, pp. 5–18, 2010.
[8] A. Gonzalez, I. Nambu, H. Hokari, M. Iwahashi, and Y. Wada, “Towards the classification of single-trial event-related potentials using adapted wavelets and particle swarm optimization,” in: Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC, 2013, pp. 3089–3094.
[9] M. Arican, and K. Polat, “Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems,” Journal of Artificial Intelligence and Systems, 2, pp. 27–37, 2020.
[10] J. F. Hwaidi and T. M. Chen, “Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach,” in IEEE Access, vol. 10, pp. 48071-48081, 2022.
[11] P. J. Kindermans, H. Verschore and B. Schrauwen, “A unified probabilistic approach to improve spelling in an event-related potential-based brain-computer interface,” IEEE Trans Biomed Eng. vol. 60, no. 10, pp. 2696-705, Oct 2013.
[12] M. Salvaris and F. Sepulveda, “Wavelets and ensemble of FLDs for P300 classification, in: 2009 4th International IEEE/EMBS Conference on Neural Engineering,” 2009, pp. 339–342.
[13] M. Liu, W. Wu, Z. Gu, Z. Yu, F. Qi, Y. Li, “Deep learning based on batch normalization for P300 signal detection,” Neurocomputing, vol. 275, pp. 288–297, 2018.
[14] N. Aghili and A. Erfanian, “A P300-based Speller Design Using a MINMAX Riemannian Geometry Scheme and Convolutional Neural Network,” IEEE Access, vol. 11, pp. 1-1. 2023.
[15] R. Afrah, Z. Amini and R. Kafieh, “An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems,” Journal of Biomedical Physics and Engineering, 2023.
[16] J. Sarraf and P. K. Pattnaik, “A study of classification techniques on P300 speller dataset,” Mater Today Proc., vol. 80, no. 3, pp. 2047-2050, 2023.
[17] H. Ghazikhani and M. Rouhani. “A deep neural network classifier for P300 BCI speller based on Cohen’s classtime-frequency distribution,” Turkish J Electr Eng Comput Sci., vol. 29, no. 2, pp. 1226-1240, 2021.
[18] D. Krusienski and G. Schalk, “Documentation of wadsworth BCI dataset (P300 evoked potentials) in BCI competition III challenge 2004,” in BCI Competition III Challenge. 2004. [Online]. Available: http://www.bbci.de/competition/iii/desc_II.pdf
[19] B. Blankertz, “Documentation second wadsworth BCI dataset (P300 evoked potentials) data acquired using BCI2000 P300 speller paradigm,” in BCI Classification Contest. Nov. 2002. [Online]. Available: http://www.bbci.de/competition/ii/albany_desc/albany_desc_ii.pdf
[20] B. Blankertz, K.-R. Muller, D. J. Krusienski, G. Schalk, J. R. Wolpaw, A. Schlogl, G. Pfurtscheller, J. R. Millan, M. Schroder, and N. Birbaumer, “The BCI competition III: Validating alternative approaches to actual BCI problems,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp. 153–159, Jun. 2006.
[21] L. A. Farwell and E. Donchin, “Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials,” Electroencephalogr. Clin. Neurophysiol., vol. 70, no. 6, pp. 510–523, Dec. 1988.
[22] R. Hosseini, S. D. Qanadli, S. Barman, M. Mazinani, T. Ellis and J. Dehmeshki, “An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System,” in IEEE Transactions on Fuzzy Systems, vol. 20, no. 2, pp. 224-234, April 2012.
[23] E. Khodadadi, R. Hosseini, and M. Mazinani, “Soft Computing Methods based on Fuzzy, Evolutionary and Swarm Intelligence for Analysis of Digital Mammography Images for Diagnosis of Breast Tumors,” Signal and Data Processing, vol. 16, no. 2, pp. 147-165, 2019.Z. Zhang, J. Sun, and T. Chen, “A new dynamically convergent differential neural network for brain signal recognition,” Biomedical Signal Processing and Control, vol. 71, 2022.
[24] Z. Zhang, J. Sun, and T. Chen, “A new dynamically convergent differential neural network for brain signal recognition,” Biomedical Signal Processing and Control, vol. 71, 2022.
[25] M. Kaper, P. Meinicke, U. Grossekathoefer, T. Lingner and H. Ritter, “BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm,” IEEE Transactions on Biomedical Engineering, vol. 51, pp. 1073–1076, 2004.
[26] A. Safari and R. Hosseini, “A Hybrid Type-2 Fuzzy-LSTM Model (HT2FLSTM) for Prediction of Environmental Temporal Patterns,” International Journal of Decision Intelligence, vol. 1, no.2, pp. 53–62, 2023.
[27] M. Benhari and R. Hosseini, “An improved ensemble deep belief model (EDBM) for pap-smear cell image classification,” Multimedia Tools and Applications, pp. 1–8, 2023.
