Asynchronous Control of P300-Based Brain-Computer Interfaces Using Sample Entropy
| Autor: | Eduardo Santamaría-Vázquez, Roberto Hornero, Víctor Martínez-Cagigal |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Speech recognition 0206 medical engineering General Physics and Astronomy Linear classifier lcsh:Astrophysics 02 engineering and technology sample entropy Electroencephalography event-related potentials Article 03 medical and health sciences 0302 clinical medicine brain–computer interfaces Event-related potential lcsh:QB460-466 medicine Entropy (information theory) lcsh:Science Oddball paradigm Brain–computer interface medicine.diagnostic_test P300-evoked potentials multiscale entropy 020601 biomedical engineering lcsh:QC1-999 Sample entropy Asynchronous communication lcsh:Q asynchrony 030217 neurology & neurosurgery lcsh:Physics oddball paradigm |
| Zdroj: | Entropy Volume 21 Issue 3 Entropy, Vol 21, Iss 3, p 230 (2019) |
| ISSN: | 1099-4300 |
| Popis: | Brain&ndash computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has a twofold purpose: (i) to characterize both control and non-control states by examining the regularity of electroencephalography (EEG) signals and (ii) to assess the efficacy of a scaled version of the sample entropy algorithm to provide asynchronous control for BCI systems. Ten healthy subjects participated in the study, who were asked to spell words through a visual oddball-based paradigm, attending (i.e., control) and ignoring (i.e., non-control) the stimuli. An optimization stage was performed for determining a common combination of hyperparameters for all subjects. Afterwards, these values were used to discern between both states using a linear classifier. Results show that control signals are more complex and irregular than non-control ones, reaching an average accuracy of 94.40 % in classification. In conclusion, the present study demonstrates that the proposed framework is useful in monitoring the attention of a user, and granting the asynchrony of the BCI system. |
| Databáze: | OpenAIRE |
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