| _version_ | 1866901888356581376 |
|---|---|
| author | Fedia, Arfaoui Sellami, Akrem Farah, Imed Riadh |
| author_facet | Fedia, Arfaoui Sellami, Akrem Farah, Imed Riadh |
| contents | <p>This paper explores Deep Learning (DL) method to classify Motor Imagery (MI)<br>based on electroencephalogram (EEG) brainwaves data using the two feature extractors<br>Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT). The contribution of this paper is in the processing phase, more precisely in the intermediate level by<br>applying the fuzzy logic fusion method to combine the information from these two methods.<br>The objective is to predict the MI from EEG signals after applying several algorithms and<br>techniques.<br>Convolutional Neural Network (CNN) was applied in 2a dataset from the brain computer<br>interface (BCI) Competition IV during the classification phase, whence comparison between<br>CNN and different classifiers is made, while considering the comparison between the fusion<br>method and each feature extractor separately.<br>The results proved that CNN is the best classifier for each of the classes and for the overall data set. Thus they showed that the adapted fusion method performed well, hence it<br>improved the sense of class prediction by recording the best classification rates.<br>Keywords : Brain Computer Interface, Motor Imagery, Electroencephalography, Classification, Deep Learning</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15585363 |
| institution | Zenodo |
| language | |
| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | An EEG data fusion approach based on deep learning for brain motor image classification Fedia, Arfaoui Sellami, Akrem Farah, Imed Riadh <p>This paper explores Deep Learning (DL) method to classify Motor Imagery (MI)<br>based on electroencephalogram (EEG) brainwaves data using the two feature extractors<br>Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT). The contribution of this paper is in the processing phase, more precisely in the intermediate level by<br>applying the fuzzy logic fusion method to combine the information from these two methods.<br>The objective is to predict the MI from EEG signals after applying several algorithms and<br>techniques.<br>Convolutional Neural Network (CNN) was applied in 2a dataset from the brain computer<br>interface (BCI) Competition IV during the classification phase, whence comparison between<br>CNN and different classifiers is made, while considering the comparison between the fusion<br>method and each feature extractor separately.<br>The results proved that CNN is the best classifier for each of the classes and for the overall data set. Thus they showed that the adapted fusion method performed well, hence it<br>improved the sense of class prediction by recording the best classification rates.<br>Keywords : Brain Computer Interface, Motor Imagery, Electroencephalography, Classification, Deep Learning</p> |
| title | An EEG data fusion approach based on deep learning for brain motor image classification |
| url | https://doi.org/10.5281/zenodo.15585363 |