MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?
Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the ot...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | TECNALIA Research & Innovation |
| Repositorio: | TECNALIA Publications |
| Idioma: | inglés |
| OAI Identifier: | oai:dsp.tecnalia.com:11556/3641 |
| Acceso en línea: | https://hdl.handle.net/11556/3641 |
| Access Level: | acceso abierto |
| Palabra clave: | BCI inefficiency Brain-computer interface (BCI) cross-frequency coupling performance predictor sensorimotor rhythms (SMRs) Internal Medicine General Neuroscience Biomedical Engineering Rehabilitation |
| Sumario: | Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called MEANSP to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. MEANSP has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm. |
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