Cutting stock problem (CSP) is a of cutting an object into several smaller objects to fulfill the existing demand with minimum unused remaining. Besides minimizing remaining object, sometimes there another additional in CSP, namely number different patterns. This happens because setup cost for each pattern. study shows way obtain patterns (CSP). An example modeled linear programming and then so...

Common spatial patterns (CSP) is a popular feature extraction method for discriminating between positive and negative classes in electroencephalography (EEG) data. Two probabilistic models for CSP were recently developed: probabilistic CSP (PCSP), which is trained by expectation maximization (EM), and variational Bayesian CSP (VBCSP) which is learned by variational approximation. Parameter expa...

This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery. This framework involves the concept of Riemannian geometry in the manifold of covariance matrices. The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the topology of the manifold of s...

Quantified constraint satisfaction is the generalization of constraint satisfaction that allows for both universal and existential quantifiers over constrained variables, instead of just existential quantifiers. We study quantified constraint satisfaction problems CSP(Q,S), where Q denotes a pattern of quantifier alternation ending in exists or the set of all possible alternations of quantifier...

One dimensional cutting stock problem (1D-CSP) is one of the representative combinatorial optimization problems, which has many applications in, e.g., steel, paper and fiber industries. To define an instance of 1D-CSP, we are given sufficient number of stock rolls which have the same length L, and m types of products with given lengths (l1, l2, . . . , lm) and demands (d1, d2, . . . , dm). A cu...

Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate clas...

Classification of multichannel electroencephalogram (EEG) recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). Frequency bands and channels configuration that relate to brain activities associated with BCI tasks are often pre-decided as default in EEG analysis without deliberations. However, a steady configuration usually loses effects due to ...

Brain-Computer Interface (BCI) systems aim to translate the intent of a subject measured from brain signals e.g. EEG into control commands. A popular paradigm for BCI communication is motor imagery i.e. subjects perform the imagination of movements with their feet or hands, the imagined movements are detected and translated into computer commands. A major challenge in BCI research are non-stati...

In this paper we propose, describe, and evaluate a novel deep learning method for classifying binary motor imagery data. This model is designed to perform CSP-like feature extractions. It can be seen as a neural network with a specifically designed architecture where the latent space corresponds naturally to the features found in CSP methods. Our model allows for easy generalization from spatia...