FGPat18: Feynman graph pattern-based language detection model using EEG signals
Article
Kirik, Serkan, Dogan, Sengul, Baygin, Mehmet, Barua, Prabal Datta, Demir, Caner Feyzi, Keles, Tugce, Yildiz, Arif Metehan, Baygin, Nursena, Tuncer, Ilknur, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra. 2023. "FGPat18: Feynman graph pattern-based language detection model using EEG signals." Biomedical Signal Processing and Control. 85. https://doi.org/10.1016/j.bspc.2023.104927
| Article Title | FGPat18: Feynman graph pattern-based language detection model using EEG signals |
|---|---|
| ERA Journal ID | 3391 |
| Article Category | Article |
| Authors | Kirik, Serkan, Dogan, Sengul, Baygin, Mehmet, Barua, Prabal Datta, Demir, Caner Feyzi, Keles, Tugce, Yildiz, Arif Metehan, Baygin, Nursena, Tuncer, Ilknur, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
| Journal Title | Biomedical Signal Processing and Control |
| Journal Citation | 85 |
| Article Number | 104927 |
| Number of Pages | 12 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1746-8094 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bspc.2023.104927 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1746809423003609 |
| Abstract | We aimed to develop an efficient handcrafted feature engineering model based on four directed graphs modeled on Feynman graph patterns (FGPat) for electroencephalography (EEG)-based language identification. We prospectively acquired a 3252-EEG dataset from 20 native English-speaking Nigerian-born and 20 Turkish subjects who were shown 20 standardized sentences in the English and Turkish languages, respectively. 14-channel 15-second EEG signals (sampling frequency 128 Hz) were acquired using the EMOTIV EPOC+ mobile brain cap system. In our FGPat18 model, input EEG signals and their 17 tunable Q wavelet transform-decomposed wavelet bands were fed as input to four FGPat-based feature extraction functions and statistical feature generators to extract textural and statistical features, respectively. Then they were concatenated to obtain four final feature vectors of varying lengths. The latter was input to the neighborhood component analysis function to select the most discriminative/meaningful 256 vectors in each vector, which were then fed to the k-nearest neighbor (kNN) classifier for binary classification. Next, iterative majority voting (IMV) was applied to the four kNN-predicted vectors to generate two voted vectors; the most accurate among the six pooled vectors was then selected as the best channel-wise result. Finally, all 14 channel-wise best vectors were input to the IMV algorithm again to calculate another 12 voted vectors; the best overall result for the EEG study was chosen among the 26 vectors. FGPat18 attained 99.38% and 92.47% classification accuracy rates with 10-fold and leave-one-subject-out cross-validations, respectively. The model has linear complexity. |
| Keywords | Brain-computer interface; Language detection; Machine learning; Feynman diagrams; EEG signal classification |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkiye |
| Firat University, Turkey | |
| Ardahan University, Turkiye | |
| School of Business | |
| University of Technology Sydney | |
| Cogninet Australia, Australia | |
| Australian International Institute of Higher Education, Australia | |
| University of New England | |
| Taylor's University, Malaysia | |
| SRM Institute of Science and Technology, India | |
| Kumamoto University, Japan | |
| University of Sydney | |
| Erzurum Technical University, Turkey | |
| Interior Ministry, Turkiye | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| School of Mathematics, Physics and Computing |
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