An Extended Computer Aided Diagnosis System for Robust BCI Applications

Yu, Xiaojun; Aziz, Muhammad Zulkifal; Hou, Yiyan; Li, Haopeng; Lv, Jialin; Jamil, Mudasir

doi:10.1109/icicn52636.2021.9673818

Cited by 4 publications

(2 citation statements)

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“…We notice that the study in [25] produced an average accuracy of 87.6%, with only 18 people scoring above 90%, 12 subjects scoring between 85 and 90%, and 19 participants scoring between 80 and 85%. The minimum results obtained by EFD-CNN for any subject in dataset 3 are 80.03%, compared to 78.54% [25], 73.06% [43], 72.15% [31], and 25.42% [44] reported by its counterparts. This section concludes that the EFD-CNN method is independent of inter-subject transfer and robust for a wide range of MI EEG datasets with different acquisition protocols.…”

Section: G Empirical Results For Datasetmentioning

confidence: 77%

“…To alleviate the high complexity, extensive computational load, as well as large fluctuation caused by manual feature extraction [19,31], the EFD combined with pre-train CNN models is proposed to contrive a non-complex and automatic feature extraction model. To the best of our knowledge and understanding, this study is the first attempt to combine EFD with any kind of CNN model and estimate its utility for MI EEG problems.…”

Section: Objectives and Contributionsmentioning

confidence: 99%

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Exploiting Asymmetric EEG Signals with EFD in Deep Learning Domain for Robust BCI

Huang

Miao

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Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, non-complex, and subject-invariant system is far-reaching. Thereupon, we put forward an MI EEG segregation pipeline in the deep-learning domain in an effort to curtail the existing limitations. Our method amalgamates multiscale principal component analysis (MSPCA), a novel empirical Fourier decomposition (EFD) signal resolution method with Hilbert transform (HT), followed by four pre-trained convolutional neural networks for automatic feature estimation and segregation. The conceived architecture is validated upon three binary class datasets: IVa, IVb from BCI Competition III, GigaDB from the GigaScience repository, and one tertiary class dataset V from BCI competition III. The average 10-fold outcomes capitulate 98.63%, 96.33%, and 89.96%, the highest classification accuracy for the aforesaid datasets accordingly using the AlexNet CNN model in a subject-dependent context, while in subject-independent cases, the highest success score was 97.69%, outperforming the contemporary studies by a fair margin. Further experiments such as the resolution scale of EFD, comparison with other signal decomposition (SD) methods, deep feature extraction, and classification with machine learning methods also accredits the supremacy of our proposed EEG signal processing pipeline. The overall findings imply that pre-trained models are reliable in identifying EEG signals due to their capacity to maintain the time-frequency structure of EEG signals, non-complex architecture, and their potential for robust classification performance.

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