Academic Journal
Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble
| العنوان: | Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble |
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| المؤلفون: | Habib Ullah, Muhammad Uzair, Arif Mahmood, Mohib Ullah, Sultan Daud Khan, Faouzi Alaya Cheikh |
| المصدر: | IEEE Access, Vol 7, Pp 40144-40153 (2019) |
| بيانات النشر: | IEEE, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: | Multiple channel EEG, emotion recognition, linear discriminant analysis, sparse PCA, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: | Among various physiological signal acquisition methods for the study of the human brain, EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive, and accurate way of capturing brain signals in multiple channels at fine temporal resolution. We propose an ensemble learning algorithm for automatically computing the most discriminative subset of EEG channels for internal emotion recognition. Our method describes an EEG channel using kernel-based representations computed from the training EEG recordings. For ensemble learning, we formulate a graph embedding linear discriminant objective function using the kernel representations. The objective function is efficiently solved via sparse non-negative principal component analysis and the final classifier is learned using the sparse projection coefficients. Our algorithm is useful in reducing the amount of data while improving computational efficiency and classification accuracy at the same time. The experiments on publicly available EEG dataset demonstrate the superiority of the proposed algorithm over the compared methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2169-3536 49513575 |
| Relation: | https://ieeexplore.ieee.org/document/8665857/; https://doaj.org/toc/2169-3536 |
| DOI: | 10.1109/ACCESS.2019.2904400 |
| URL الوصول: | https://doaj.org/article/4951357523ef43bdb87cb64494dd5342 |
| رقم الانضمام: | edsdoj.4951357523ef43bdb87cb64494dd5342 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21693536 49513575 |
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| DOI: | 10.1109/ACCESS.2019.2904400 |