| Reference code: | PT/FB/BL-2018-306.02 |
| Location: | BF-GMS
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Title:
| Classification of erroneous actions using EEG frequency features: implications for BCI performance
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| Publication year: | 2021
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URL:
| https://ieeexplore.ieee.org/abstract/document/9630509
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| Abstract/Results: | ABSTRACT:
Several studies have demonstrated that error-related neuronal signatures can be successfully detected and used to improve the performance of brain-computer interfaces. However, this has been tested mainly in well-controlled environments and based on temporal features, such as the amplitude of event-related potentials. In this study, we propose a classification algorithm combining frequency features and a weighted SVM to detect the neuronal signatures of errors committed in a complex saccadic go/no-go task. We follow the hypothesis that frequency features yield better discrimination performance in complex tasks, generalize better, and require fewer pre-processing steps. When combining temporal and frequency features, we achieved a balanced classification accuracy of 75% - almost the same as using only frequency features. On the other hand, when using only temporal features, the balanced accuracy decreased to 66%. These findings show that subjects' performance can be automatically detected based on frequency features of error-related neuronal signatures. Additionally, our results revealed that features computed in the pre-response time contribute to the discrimination between correct and erroneous responses, which suggests the existence of error-related patterns even before response execution.
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| Accessibility: | Document exists in file
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Copyright/Reproduction:
| By permission
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Language:
| eng
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Author:
| Dias, C.
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Secondary author(s):
| Costa, D. M., Sousa, T., Castelhano, J., Figueiredo, V., Pereira, A. C., Castelo-Branco, M.
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Document type:
| Conference paper
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Number of reproductions:
| 1
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Reference:
| Dias, C., Costa, D. M., Sousa, T., Castelhano, J., Figueiredo, V., Pereira, A. C., & Castelo-Branco, M. (2021). Classification of erroneous actions using EEG frequency features: implications for BCI performance. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 629-632. https://doi.org/10.1109/EMBC46164.2021.9630509
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| Indexed document: | No
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| Keywords: | Support vector machines / Feature extraction / Electroencephalography / Brain-computer interfaces / Biology / Classification algorithms / Task analysis
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