Classification in Frequency Domain of EEG Signals of Motor Imagery for Brain Computer Interfaces

In this study the classification of the EEG signals recorded during motor imagery for curser movement in brain computer interfaces is examined, in which the feature vectors obtained in frequency domain is used and then the linear transformations are applied for reducing the size of the feature vectors.


Investigation of haptic manipulators with linear equations of motion
Kızılbey, Aras; Soylu, Reşit; Department of Mechanical Engineering (2019)
In this thesis, linearization of the equations of motion of haptic interfaces and the effects of such linearization on haptic applications are examined. Three and six DOF configurations of the Phantom Premium™ 1.5 have been selected as the haptic manipulators to be investigated. By utilizing the generic computer code that has been developed for hybrid manipulators composed of revolute and prismatic joints, the equations of motion for the aforementioned two haptic manipulator types are derived in symbolic fo...
Learning Deep Temporal Representations for fMRI Brain Decoding
Firat, Orhan; Aksan, Emre; Oztekin, Ilke; Yarman Vural, Fatoş Tunay (2015-07-11)
Functional magnetic resonance imaging (fMRI) produces low number of samples in high dimensional vector spaces which is hardly adequate for brain decoding tasks. In this study, we propose a combination of autoencoding and temporal convolutional neural network architecture which aims to reduce the feature dimensionality along with improved classification performance. The proposed network learns temporal representations of voxel intensities at each layer of the network by leveraging unlabeled fMRI data with re...
Evaluation of multivariate adaptive non-parametric reduced-order model for solving the inverse electrocardiography problem: a simulation study
Onak, Onder Nazim; Serinağaoğlu Doğrusöz, Yeşim; Weber, Gerhard Wilhelm (Springer Science and Business Media LLC, 2019-05-01)
In the inverse electrocardiography (ECG) problem, the goal is to reconstruct the heart's electrical activity from multichannel body surface potentials and a mathematical model of the torso. Over the years, researchers have employed various approaches to solve this ill-posed problem including regularization, optimization, and statistical estimation. It is still a topic of interest especially for researchers and clinicians whose goal is to adopt this technique in clinical applications. Among the wide range of...
Linear Separability Analysis for Stacked Generalization Architecture
Ozay, Mete; Vural, Fatos T. Yarman (2009-04-11)
Stacked Generalization algorithm aims to increase the individual classification performances of the classifiers by combining the information obtained from various classifiers in a multilayer architecture by either linear or nonlinear techniques. Performance of the algorithm varies depending on the application domains and the space analyses that affect the classification performances could riot be applied successfully.
Analysis and classification of spelling paradigm EEG data and an attempt for optimization of channels used
Yıldırım, Asil; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2010)
Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. Spelling Paradigm is a BCI application which aims to construct the words by finding letters using P300 signals recorded via channel electrodes attached to the diverse points of the scalp. Reducing the letter detection error rates and increasing the speed of...
Citation Formats
U. Halıcı, “Classification in Frequency Domain of EEG Signals of Motor Imagery for Brain Computer Interfaces,” 2009, Accessed: 00, 2020. [Online]. Available: