Bispectrum Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916112683237376 |
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| author | Ghafoori, Sima Rabiee, Ali Cetera, Anna Abiri, Reza |
| author_facet | Ghafoori, Sima Rabiee, Ali Cetera, Anna Abiri, Reza |
| contents | The bispectrum stands out as a revolutionary tool in frequency domain analysis, leaping the usual power spectrum by capturing crucial phase information between frequency components. In our innovative study, we have utilized the bispectrum to analyze and decode complex grasping movements, gathering EEG data from five human subjects. We put this data through its paces with three classifiers, focusing on both magnitude and phase-related features. The results highlight the bispectrum's incredible ability to delve into neural activity and differentiate between various grasping motions with the Support Vector Machine (SVM) classifier emerging as a standout performer. In binary classification, it achieved a remarkable 97\% accuracy in identifying power grasp, and in the more complex multiclass tasks, it maintained an impressive 94.93\% accuracy. This finding not only underscores the bispectrum's analytical strength but also showcases the SVM's exceptional capability in classification, opening new doors in our understanding of movement and neural dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bispectrum Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types Ghafoori, Sima Rabiee, Ali Cetera, Anna Abiri, Reza Signal Processing The bispectrum stands out as a revolutionary tool in frequency domain analysis, leaping the usual power spectrum by capturing crucial phase information between frequency components. In our innovative study, we have utilized the bispectrum to analyze and decode complex grasping movements, gathering EEG data from five human subjects. We put this data through its paces with three classifiers, focusing on both magnitude and phase-related features. The results highlight the bispectrum's incredible ability to delve into neural activity and differentiate between various grasping motions with the Support Vector Machine (SVM) classifier emerging as a standout performer. In binary classification, it achieved a remarkable 97\% accuracy in identifying power grasp, and in the more complex multiclass tasks, it maintained an impressive 94.93\% accuracy. This finding not only underscores the bispectrum's analytical strength but also showcases the SVM's exceptional capability in classification, opening new doors in our understanding of movement and neural dynamics. |
| title | Bispectrum Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2402.01026 |