Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

Rodríguez-Moreno, Itsaso; Martínez-Otzeta, José María; Sierra, Basilio; Irigoien, Itziar; Rodriguez, Igor; Goienetxea, Izaro

doi:10.3390/app10228075

Cited by 2 publications

(1 citation statement)

References 28 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of the present work is to introduce a novel Distance-Based generalization of it (DB-CSP). This generalization is of great interest, since these techniques can also offer good solutions in other fields where multivariate time series data arise beyond pure electroencephalography data [Poppe, 2010, Rodríguez-Moreno et al, 2020.…”

Section: Introductionmentioning

confidence: 99%

dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

Rodriguez,

Irigoien,

Sierra

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Common Spacial Patterns (CSP) is a widely used method to analyse electroencephalography (EEG) data, concerning the supervised classification of brain's activity. More generally, it can be useful to distinguish between multivariate signals recorded during a time span for two different classes. CSP is based on the simultaneous diagonalization of the average covariance matrices of signals from both classes and it allows to project the data into a low-dimensional subspace. Once data are represented in a low-dimensional subspace, a classification step must be carried out. The original CSP method is based on the Euclidean distance between signals and here, we extend it so that it can be applied on any appropriate distance for data at hand. Both, the classical CSP and the new Distance-Based CSP (DB-CSP) are implemented in an R package, called dbcsp.

show abstract

Section: Introductionmentioning

confidence: 99%

dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

Rodriguez,

Irigoien,

Sierra

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

CatSight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern

Florez

Rodríguez-Moreno

Artetxe

et al. 2023

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Detecting changes in data streams, with the data flowing continuously, is an important problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution may vary after a change in the machine’s operating point; this situation is known as concept drift, and it is key to detecting this change. One drawback of conventional machine learning algorithms is that they are usually static, trained offline, and require monitoring at the input level. A change in the distribution of data, in the relationship between the input and the output data, would result in the deterioration of the predictive performance of the models due to the lack of an ability to generalize the model to new concepts. Drift detecting methods emerge as a solution to identify the concept drift in the data. This paper proposes a new approach for concept drift detection—a novel approach to deal with sudden or abrupt drift, the most common drift found in industrial processes-, called CatSight. Briefly, this method is composed of two steps: (i) Use of Common Spatial Patterns (a statistical approach to deal with data streaming, closely related to Principal Component Analysis) to maximize the difference between two different distributions of a multivariate temporal data, and (ii) Machine Learning conventional algorithms to detect whether a change in the data flow has been occurred or not. The performance of the CatSight method, has been evaluated on a real use case, training six state of the art Machine Learning (ML) classifiers; obtained results indicate how adequate the new approach is.

show abstract

Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

Cited by 2 publications

References 28 publications

dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

CatSight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern

Contact Info

Product

Resources

About