An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch
Touch sensation is a key modality that allows humans to understand and interact with their environment. More often than not, touch sensation depends on vision to accumulate and validate the received information. The ability to distinguish between materials and surfaces through active touch consists...
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2024-02-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6455 |
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| author | Andreas Miltiadous Vasileios Aspiotis Dimitrios Peschos Katerina D. Tzimourta Al Husein Sami Abosaleh Nikolaos Giannakeas Alexandros Tzallas |
| author_facet | Andreas Miltiadous Vasileios Aspiotis Dimitrios Peschos Katerina D. Tzimourta Al Husein Sami Abosaleh Nikolaos Giannakeas Alexandros Tzallas |
| author_sort | Andreas Miltiadous |
| collection | DOAJ |
| description | Touch sensation is a key modality that allows humans to understand and interact with their environment. More often than not, touch sensation depends on vision to accumulate and validate the received information. The ability to distinguish between materials and surfaces through active touch consists of a complex of neurophysiological operations. To unveil the functionality of these operations, neuroimaging and neurophysiological research tools are employed, with electroencephalography being the most used. In this paper, we attempt to distinguish between brain states when touching different natural textures (smooth, rough, and liquid). Recordings were obtained with a commercially available EEG wearable device. Time and frequency-based features were extracted, transformed with PCA decomposition, and an ensemble classifier combining Random Forest, Support Vector Machine, and Neural Network was utilized. High accuracy scores of 79.64% for the four-class problem and 89.34% for the three-class problem (Null-Rough-Water) were accordingly achieved. Thus, the methodology's robustness indicates its ability to classify different brain states under haptic stimuli.
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| first_indexed | 2024-03-08T04:04:31Z |
| format | Article |
| id | doaj.art-42ea3c4567d843a0bdf57dfa29f36e30 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-08T04:04:31Z |
| publishDate | 2024-02-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-42ea3c4567d843a0bdf57dfa29f36e302024-02-09T06:06:16ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-02-0114110.48084/etasr.6455An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active TouchAndreas Miltiadous0Vasileios Aspiotis1Dimitrios Peschos2Katerina D. Tzimourta3Al Husein Sami Abosaleh4Nikolaos Giannakeas5Alexandros Tzallas6Department of Informatics and Telecommunications, University of Ioannina, GreeceFaculty of Medicine, University of Ioannina, GreeceFaculty of Medicine, University of Ioannina, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, GreeceOpen Lab, Newcastle University, UKDepartment of Informatics and Telecommunications, University of Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, GreeceTouch sensation is a key modality that allows humans to understand and interact with their environment. More often than not, touch sensation depends on vision to accumulate and validate the received information. The ability to distinguish between materials and surfaces through active touch consists of a complex of neurophysiological operations. To unveil the functionality of these operations, neuroimaging and neurophysiological research tools are employed, with electroencephalography being the most used. In this paper, we attempt to distinguish between brain states when touching different natural textures (smooth, rough, and liquid). Recordings were obtained with a commercially available EEG wearable device. Time and frequency-based features were extracted, transformed with PCA decomposition, and an ensemble classifier combining Random Forest, Support Vector Machine, and Neural Network was utilized. High accuracy scores of 79.64% for the four-class problem and 89.34% for the three-class problem (Null-Rough-Water) were accordingly achieved. Thus, the methodology's robustness indicates its ability to classify different brain states under haptic stimuli. https://etasr.com/index.php/ETASR/article/view/6455multisensorymachine learningPCAensemble method |
| spellingShingle | Andreas Miltiadous Vasileios Aspiotis Dimitrios Peschos Katerina D. Tzimourta Al Husein Sami Abosaleh Nikolaos Giannakeas Alexandros Tzallas An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch Engineering, Technology & Applied Science Research multisensory machine learning PCA ensemble method |
| title | An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch |
| title_full | An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch |
| title_fullStr | An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch |
| title_full_unstemmed | An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch |
| title_short | An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch |
| title_sort | ensemble method for eeg based texture discrimination during open eyes active touch |
| topic | multisensory machine learning PCA ensemble method |
| url | https://etasr.com/index.php/ETASR/article/view/6455 |
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