g.BSanalyze: Classify-toolbox - gtec's software package for linear and non-linear data set classification

Classification enables to categorize patterns and signal features of biosignals into different classes.

In the learning phase of supervised learning, classifiers are trained with signal patterns and signal features with known class information. After the training phase the classifier is ready to categorize new signal patterns into the specific classes.

One application of classification is to discriminate EEG patterns in brain-computer-interface experiments (e.g. into LEFT and RIGHT hand motor imageries). Optimized feature selection can be performed via Distinction Sensitive Learning Vector Quantization (DSLVQ). This method yields the most relevant features for optimal data set classification.

The graphical user interface for g.CLASSIFYtoolbox  enables you to classify signal patterns of whole trials, within specified time segments or at certain time points. Signal features are combined in a so-called "feature matrix". This matrix is the basis for training of all linear and non-linear classification methods, among them a multi-class linear discriminant analysis, radial basis function neural network or multi-layer perceptron neural network. You can load and save your preferred processing steps as a script program and automatically process your data in batch mode.