# Distance-Based Common Spatial Patterns dbcsp is a package which offers a way to apply Distance-Based Common Spatial Patterns (DB-CSP) techniques in different fields, both classical Common Spatial Patterns (CSP) as well as DB-CSP. package ## Examples ```r # There is an example dataset called AR.data shipped with the package. # It contains the skeleton data extracted from videos of people performing six different actions, # recorded by a humanoid robot. So, it has 6 different classes. # As the whole process is performed pairwise, first two classes are selected (some instances are # saved to use later as test) handshake <- AR.data$handshake[1:15] ignore <- AR.data$ignore[1:15] # Now, the dbcsp object can be created, where q represents the number of vectors used in # the projection, the obtained filter will have 2*q dimension. # By default, euclidean distance is used. To change it, just select another distance type. # A mixture with euclidean distance and another one can be performed too, changing the mixture # parameter value. # mydbcsp <- new('dbcsp', handshake, ignore, type="minkowski", p=0.2) # A user-defined custom distance can be used too. fn <- function(x, y) mean(1 - cos(x - y)) mydbcsp <- new("dbcsp", X1 = handshake, X2 = ignore, type="fn") # Regarding the classification, train, predict and selectQ functions can be used. # An LDA model can be train with the features extracted after performing the CSP, both with the # train function or using the training=TRUE parameter when creating the dbcsp object mydbcsp <- new('dbcsp', handshake, ignore, q=10, training=TRUE, fold = 1) # Once the object is created, print and summary functions can be used to see some details print(mydbcsp) summary(mydbcsp) # The predict function returns the predicted labels for the selected test data. # And if true_labels are indicated, the obtained accuracy is also printed handshake_test <- AR.data$handshake[41:45] ignore_test <- AR.data$ignore[41:45] test_data <- c(handshake_test, ignore_test) true_labels <- c(rep('handshake',length(handshake_test)),rep('ignore',length(ignore_test))) predictions <- predict(mydbcsp, test_data,true_labels) # To help us deciding which is the best dimension to use when performing the CSP algorithm, the # selectQ function can be used. Instead of using train_size to validate with train/test split, # cross validation can be performed too. bestQ <- selectQ(mydbcsp, Q=c(2,3,5), train_size=0.8) # A plot can also be obtained, which displays the signals before and after the CSP projection # With the vectors parameter it can be decided which dimensions to show and if we just want to # plot the transformed signals, the before parameter must be set to FALSE plot(mydbcsp,class=2,index=1,before=FALSE,vectors = 1:5, legend=TRUE) # A boxplot can also be obtained to show the features achieved after the CSP (the variances of # the transformed signals) which are used to perform the classification # In the CSP algorithm the vectors work in pairs (the first q vectors maximize the variance of # one class and minimize the variance of the other, while the last q vectors do the opposite), # by default the vectors are showed in pairs, but this can be changed. # The logarithm of the variances are plotted, but the value of the variances are shown # when changing show_log parameter to FALSE boxplot(mydbcsp, vectors=c(2,4,8)) ``` ## See Also `dbcsp`, `print`, `summary`, `train`, `selectQ`, `predict`, `plot`, `boxplot` ## Author(s) Itsaso Rodriguez Moreno itsaso.rodriguez@ehu.eus Itziar Irigoien itziar.irigoien@ehu.eus Concepción Arenas carenas@ub.edu

dbcsp-package