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Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

Martinez-Leon JA, Cano-Izquierdo JM, Ibarrola J - Comput Intell Neurosci (2015)

Bottom Line: In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate.The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest.Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.

View Article: PubMed Central - PubMed

Affiliation: Universidad Polit├ęcnica de Cartagena, Campus Muralla del Mar, Calle Doctor Fleming S/N, 30202 Cartagena, Spain.

ABSTRACT
This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.

No MeSH data available.


J(xj) values for the three users and sessions. The value (1/C)C is represented by a solid line.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: J(xj) values for the three users and sessions. The value (1/C)C is represented by a solid line.

Mentions: Figure 5 provides the results obtained for the three users of the BCI Competition III Data Set V database. The value of J(xj) has been calculated in a separate way for each one of the three learning sessions within the data. Given that the lower values on the figures are related to high discriminant features, the existence of a reduced number of features with a high discriminant character can be stated.


Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

Martinez-Leon JA, Cano-Izquierdo JM, Ibarrola J - Comput Intell Neurosci (2015)

J(xj) values for the three users and sessions. The value (1/C)C is represented by a solid line.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4419264&req=5

fig5: J(xj) values for the three users and sessions. The value (1/C)C is represented by a solid line.
Mentions: Figure 5 provides the results obtained for the three users of the BCI Competition III Data Set V database. The value of J(xj) has been calculated in a separate way for each one of the three learning sessions within the data. Given that the lower values on the figures are related to high discriminant features, the existence of a reduced number of features with a high discriminant character can be stated.

Bottom Line: In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate.The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest.Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.

View Article: PubMed Central - PubMed

Affiliation: Universidad Polit├ęcnica de Cartagena, Campus Muralla del Mar, Calle Doctor Fleming S/N, 30202 Cartagena, Spain.

ABSTRACT
This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.

No MeSH data available.