Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database
Article
Barua, Prabal Datta, Keles, Tugce, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Demir, Caner Feyzi, Fujita, Hamido, Tan, Ru-San, Ooi, Chui Ping and Acharya, U. Rajendra. 2023. "Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database." Biomedical Signal Processing and Control. 79 (Part 1). https://doi.org/10.1016/j.bspc.2022.104055
| Article Title | Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database |
|---|---|
| ERA Journal ID | 3391 |
| Article Category | Article |
| Authors | Barua, Prabal Datta, Keles, Tugce, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Demir, Caner Feyzi, Fujita, Hamido, Tan, Ru-San, Ooi, Chui Ping and Acharya, U. Rajendra |
| Journal Title | Biomedical Signal Processing and Control |
| Journal Citation | 79 (Part 1) |
| Article Number | 104055 |
| Number of Pages | 13 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1746-8094 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bspc.2022.104055 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1746809422005274 |
| Abstract | Electroencephalography (EEG) signal is an important physiological signal commonly used in machine learning to decode brain activities, including imagined words and sentences. We aimed to develop an automated lightweight EEG signal-based sentence classification model using a novel dynamic-sized binary pattern (DSBP) textural feature extractor and iterative multi-classifiers based majority voting (IMCMV) algorithm for iterative voting of results calculated using different classifiers for multi-channel EEG signal inputs. A new Turkish sentence EEG(TSEEG) was prospectively acquired. It comprised of 15-second 14-channel EEG signals recorded when 40 volunteers (for each dataset, we collected EEG signals from 20 participants) were either shown or read corresponding to demonstration or listening modes, respectively. Hence, 20 standardized commonly used sentences were obtained in their native Turkish language. The developed sentence classification model extracted 5,400 multilevel deep features from each channel EEG signal segment using the novel DSBP, statistical features, and multilevel discrete wavelet transform (MDWT). 512 features were then chosen using the neighborhood component analysis selection function. k-nearest neighbor and support vector machine classifiers were used to calculate two prediction vectors from the selected features using tenfold cross-validation, i.e., 28 vectors were generated for each 14-channel EEG recording. Finally, the best general voted results were determined for increasing numbers of iteratively calculated prediction vectors using the novel IMCMV algorithm. Channel-wise and voted results were found to be excellent for sentence classification for the TSEEG dataset in both demonstration and listening modes. The DSBP-IMCMV-based model attained the best general classification rates of 98.81% and 98.19% in the demonstration and listening modes, respectively. |
| Keywords | Dynamic sized binary pattern; EEG sentence classification; Iterative multi-classifiers based majority voting; Machine learning; Neighborhood component analysis |
| Contains Sensitive Content | Does not contain sensitive content |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Funder | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu |
| Byline Affiliations | School of Business |
| University of Technology Sydney | |
| Firat University, Turkey | |
| Ardahan University, Turkiye | |
| HUTECH University of Technology, Vietnam | |
| University of Granada, Spain | |
| Iwate Prefectural University, Japan | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| School of Mathematics, Physics and Computing | |
| Institute for Life Sciences and the Environment | |
| Centre for Health Research |
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Macin, Gulay, Tasci, Burak, Tasci, Irem, Faust, Oliver, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra. 2022. "An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ." Applied Sciences. 12 (10). https://doi.org/10.3390/app12104920A new lateral geniculate nucleus pattern-based environmental sound classification using a new large sound dataset
Tasci, Burak, Acharya, Madhav R., Barua, Prabal Datta, Yildiz, Arif Metehan, Gun, Mehmet Veysel, Keles, Tugce, Dogan, Sengul and Tuncer, Turker. 2022. "A new lateral geniculate nucleus pattern-based environmental sound classification using a new large sound dataset." Applied Acoustics. 196. https://doi.org/10.1016/j.apacoust.2022.108897TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
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Koh, Joel En Wei, de Michele, Simona, Sudarshan, Vidya K., Jahmunah, V., Ciaccio, Edward J., Ooi, Chui Ping, Gururajan, Raj, Gururajan, Rashmi, Oh, Shu Lih, Lewis, Suzanne K., Green, Peter H., Bhagat, Govind and Acharya, U. Rajendra. 2021. "Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach." Computer Methods and Programs in Biomedicine. 203. https://doi.org/10.1016/j.cmpb.2021.106010Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science
Jahmunah, Vicnesh, Sudarshan, Vidya K., Oh, Shu Lih, Gururajan, Raj, Gururajan, Rashmi, Zhou, Xujuan, Tao, Xiaohui, Faust, Oliver, Ciaccio, Edward J., Ng, Kwan Hoong and Acharya, U. Rajendra. 2021. "Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science." International Journal of Imaging Systems and Technology. 31 (2), pp. 455-471. https://doi.org/10.1002/ima.22552A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection
Zhou, Xujuan, Li, Yuefeng, Gururajan, Raj, Bargshady, Ghazal, Tao, Xiaohui, Venkataraman, Revathi, Barua, Prabal D. and Kondalsamy-Chennakesavan, Srinivas. 2020. "A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection." 7th International Conference on Behavioural and Social Computing (BESC 2020). Bournemouth, United Kingdom 05 - 07 Nov 2020 United Kingdom. https://doi.org/10.1109/BESC51023.2020.9348322A survey on text classification and its applications
Zhou, Xujuan, Gururajan, Raj, Li, Yuefeng, Venkataraman, Revathi, Tao, Xiaohui, Bargshady, Ghazala, Barua, Prabal D. and Kondalsamy-Chennakesavan, Srinivas. 2020. "A survey on text classification and its applications." Web Intelligence. 18 (2), pp. 205-216. https://doi.org/10.3233/WEB-200442Automated prediction of sepsis using temporal convolutional network
Kok, Christopher, Jahmunah, V., Oh, Shu Lih, Zhou, Xujuan, Gururajan, Raj, Tao, Xiaohui, Cheong, Kang Hao, Gururajan, Rashmi, Molinari, Filippo and Acharya, U. Rajendra. 2020. "Automated prediction of sepsis using temporal convolutional network." Computers in Biology and Medicine. 127, pp. 1-10. https://doi.org/10.1016/j.compbiomed.2020.103957Determination of factors influencing student engagement using a learning management system in a tertiary setting
Barua, Prabal Datta. 2020. Determination of factors influencing student engagement using a learning management system in a tertiary setting. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/Z9AS-4P42Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach
Nasarian, Elham, Abdar, Moloud, Fahami, Mohammad Amin, Alizadehsani, Roohallah, Hussain, Sadiq, Basiri, Mohammad Ehsan, Zomorodi-Moghadam, Mariam, Zhou, Xujuan, Plawiak, Pawel, Acharya, U. Rajendra, Tan, Ru-San and Sarrafzadegan, Nizal. 2020. "Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach." Pattern Recognition Letters. 133, pp. 33-40. https://doi.org/10.1016/j.patrec.2020.02.010Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease
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