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Mixed-norm regularization for brain decoding.

Flamary R, Jrad N, Phlypo R, Congedo M, Rakotomamonjy A - Comput Math Methods Med (2014)

Bottom Line: For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities.The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection.The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire Lagrange, UMR7293, Université de Nice, 00006 Nice, France.

ABSTRACT
This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

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Selected sensors for the EPFL dataset. The line width of the circle is proportional to the number of times the sensor is selected for different splits. No circle means that the sensor has never been selected.
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fig2: Selected sensors for the EPFL dataset. The line width of the circle is proportional to the number of times the sensor is selected for different splits. No circle means that the sensor has never been selected.

Mentions: A visualization of the electrodes selected by GSVM-a can be seen in Figure 2 for the EPFL dataset and in Figure 3 for the UAM dataset. Interestingly, we observe that for the EPFL dataset, the selected channels are highly dependent on the subject. The most recurring ones are the following: FC1 C3 T7 CP5 P3 PO3 PO4 Pz and the electrodes located above visual cortex O1, Oz, and O2. We see sensors from the occipital area that are known to be relevant [12] for P300 recognition, but sensors such as T7 and C3, from other brain regions, are also frequently selected. These results are however consistent with those presented in the recent literature [4, 18].


Mixed-norm regularization for brain decoding.

Flamary R, Jrad N, Phlypo R, Congedo M, Rakotomamonjy A - Comput Math Methods Med (2014)

Selected sensors for the EPFL dataset. The line width of the circle is proportional to the number of times the sensor is selected for different splits. No circle means that the sensor has never been selected.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016929&req=5

fig2: Selected sensors for the EPFL dataset. The line width of the circle is proportional to the number of times the sensor is selected for different splits. No circle means that the sensor has never been selected.
Mentions: A visualization of the electrodes selected by GSVM-a can be seen in Figure 2 for the EPFL dataset and in Figure 3 for the UAM dataset. Interestingly, we observe that for the EPFL dataset, the selected channels are highly dependent on the subject. The most recurring ones are the following: FC1 C3 T7 CP5 P3 PO3 PO4 Pz and the electrodes located above visual cortex O1, Oz, and O2. We see sensors from the occipital area that are known to be relevant [12] for P300 recognition, but sensors such as T7 and C3, from other brain regions, are also frequently selected. These results are however consistent with those presented in the recent literature [4, 18].

Bottom Line: For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities.The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection.The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire Lagrange, UMR7293, Université de Nice, 00006 Nice, France.

ABSTRACT
This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

Show MeSH