Identification of Relevant Inter-channel EEG Connectivity Patterns: A Kernel-Based Supervised Approach

Hurtado-Rincón, Juana Valeria; Martínez-Vargas, Juan David; Rojas-Jaramillo, Sebastian; Giraldo, Eduardo; Castellanos-Domínguez, Germán

doi:10.1007/978-3-319-47103-7_2

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, we employ a Center Kernel Alignment (CKA)-based functional to learn a linear projection that encodes all discriminative input features, benefiting from the non-linear notion of similarity behind the studied kernels (Cortes et al, 2012 ). The present approach is an extension of our previous Kernel Relevance Analysis strategy describe in (Arias-Mora et al, 2015 ; Hurtado-Rincón et al, 2016 ). In particular, EKRA can be implemented as both feature selection (EKRA-S) and enhanced feature selection (EKRA-ES) tool.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, an open issue is the definition of the kernel transformation that can be more connected with the appropriate type of application nonlinearity (Zimmer et al, 2015 ). Nevertheless, more efforts are spent in the development of a metric learning that allows a kernel to adjust the importance of individual features of tasks under consideration, usually exploiting a given amount of supervisory information (Hurtado-Rincón et al, 2016 ). Hence, the kernel-based relevance analysis can handle the estimated weights to highlight the features or dimensions relevant for improving the classification performance (Brockmeier et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

Álvarez-Meza

Gutiérrez

Castellanos-Domínguez

2017

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.

show abstract

Section: Introductionmentioning

confidence: 99%