A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG
This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.868642/full |
| _version_ | 1818211527169146880 |
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| author | Pasin Israsena Setha Pan-Ngum |
| author_facet | Pasin Israsena Setha Pan-Ngum |
| author_sort | Pasin Israsena |
| collection | DOAJ |
| description | This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG. |
| first_indexed | 2024-12-12T05:33:55Z |
| format | Article |
| id | doaj.art-0ebd23f8ffab4924a0bf88ddfca7b400 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-12T05:33:55Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-0ebd23f8ffab4924a0bf88ddfca7b4002022-12-22T00:36:13ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-05-011610.3389/fncom.2022.868642868642A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEGPasin Israsena0Setha Pan-Ngum1National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), Pathumthani, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandThis paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.https://www.frontiersin.org/articles/10.3389/fncom.2022.868642/fullbrain-computer interfaceSSVEPCNNear-EEGbinaural |
| spellingShingle | Pasin Israsena Setha Pan-Ngum A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG Frontiers in Computational Neuroscience brain-computer interface SSVEP CNN ear-EEG binaural |
| title | A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG |
| title_full | A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG |
| title_fullStr | A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG |
| title_full_unstemmed | A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG |
| title_short | A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG |
| title_sort | cnn based deep learning approach for ssvep detection targeting binaural ear eeg |
| topic | brain-computer interface SSVEP CNN ear-EEG binaural |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.868642/full |
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