Brain Computer Interface (BCI) technologies use neural activity to implement a direct communication channel for healthy and disable subjects. To achieve this, many investigations look to improve BCI precision by increasing the number of electrodes with standard configurations, ignoring inter- A nd intra-subject variability. To control this variability in event-related potential (ERP)-based BCIs we propose to investigate the cumulative peak difference, an intrinsic characteristic of ERP, as a measure for electrode selection. The results shown in this work indicate that the proposed method improved accuracy and bitrate in all analyzed electrode sets. Our work contributes to the management of inter- A nd intra-subject variability which helps to design accurate and low-cost BCIs.
|Title of host publication||2018 6th International Conference on Brain-Computer Interface, BCI 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - 9 Mar 2018|
|Event||6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of|
Duration: 15 Jan 2018 → 17 Jan 2018
|Name||2018 6th International Conference on Brain-Computer Interface, BCI 2018|
|Conference||6th International Conference on Brain-Computer Interface, BCI 2018|
|Country/Territory||Korea, Republic of|
|Period||15/01/18 → 17/01/18|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was funded by Spanish projects of Ministerio de Economía y Competitividad/FEDER TIN2014-54580-R, DPI2015-65833-P (http://www.mineco.gob.es/) and Predoctoral Research Grants 2015-AR2Q9086 of the Government of Ecuador through the SENESCYT.
© 2018 IEEE.
- Bayesian Linear Discriminant Analysis (BLDA)
- Brain Computer Interface (BCI)
- Event-related potentia (ERP)
- Inter-subject variability
- Intra-subject variability
- P300 component
- Personalized Brain-Machine Interfaces