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Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.

Iturrate I, Grizou J, Omedes J, Oudeyer PY, Lopes M, Montesano L - PLoS ONE (2015)

Bottom Line: This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials.The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration.Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

View Article: PubMed Central - PubMed

Affiliation: Chair in Brain-Machine Interface (CNBI) and Center for Neuroprosthetics (CNP), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Instituto de Investigación en Ingeniería de Sistemas (I3A), Universidad de Zaragoza, Zaragoza, Spain.

ABSTRACT
This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

No MeSH data available.


Related in: MedlinePlus

Grand-averaged signals for Fz, FCz and CPz channels.Red and blue lines represent averaged signals for the error and correct conditions respectively, where the movement onset is marked as time 0 ms. Statistical differences (unpaired t-tests, p-values < 1·10−6) between error and correct averages are marked by shadowed areas.
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pone.0131491.g002: Grand-averaged signals for Fz, FCz and CPz channels.Red and blue lines represent averaged signals for the error and correct conditions respectively, where the movement onset is marked as time 0 ms. Statistical differences (unpaired t-tests, p-values < 1·10−6) between error and correct averages are marked by shadowed areas.

Mentions: Fig 2 shows the grand-averaged signals obtained during the online experiments. Error and correct assessments generated different evoked responses posterior to the device actions. The difference between error and correct potentials appeared on fronto-central locations at around 400 and 600 ms, where the error potential exhibited significantly larger positive and negative peaks respectively (unpaired t-tests, p < 1·10−6). These evoked responses were in line with previous experiments using error potentials [17, 18].


Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.

Iturrate I, Grizou J, Omedes J, Oudeyer PY, Lopes M, Montesano L - PLoS ONE (2015)

Grand-averaged signals for Fz, FCz and CPz channels.Red and blue lines represent averaged signals for the error and correct conditions respectively, where the movement onset is marked as time 0 ms. Statistical differences (unpaired t-tests, p-values < 1·10−6) between error and correct averages are marked by shadowed areas.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488878&req=5

pone.0131491.g002: Grand-averaged signals for Fz, FCz and CPz channels.Red and blue lines represent averaged signals for the error and correct conditions respectively, where the movement onset is marked as time 0 ms. Statistical differences (unpaired t-tests, p-values < 1·10−6) between error and correct averages are marked by shadowed areas.
Mentions: Fig 2 shows the grand-averaged signals obtained during the online experiments. Error and correct assessments generated different evoked responses posterior to the device actions. The difference between error and correct potentials appeared on fronto-central locations at around 400 and 600 ms, where the error potential exhibited significantly larger positive and negative peaks respectively (unpaired t-tests, p < 1·10−6). These evoked responses were in line with previous experiments using error potentials [17, 18].

Bottom Line: This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials.The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration.Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

View Article: PubMed Central - PubMed

Affiliation: Chair in Brain-Machine Interface (CNBI) and Center for Neuroprosthetics (CNP), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Instituto de Investigación en Ingeniería de Sistemas (I3A), Universidad de Zaragoza, Zaragoza, Spain.

ABSTRACT
This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

No MeSH data available.


Related in: MedlinePlus