Classification of motor imagery tasks in EEG signal and its application to a brain-computer interface for controlling assistive environmental devices

Acar, Erman
This study focuses on realization of a Brain Computer Interface (BCI)for the paralyzed to control assistive environmental devices. For this purpose, different motor imagery tasks are classified using different signal processing methods. Specifically, band-pass filtering, Laplacian filtering, and common average reference (CAR) filtering areused to enhance the EEG signal. For feature extraction; Common Spatial Pattern (CSP), Power Spectral Density (PSD), and Principal Component Analysis (PCA) are tested. Linear Feature Normalization (LFN), Gaussian Feature Normalization (GFN), and Unit-norm Feature Vector Normalization (UFVN) are studied in Support Vector Machine (SVM) and Artificial Neural Network (ANN) classification. In order to evaluate and compare the performance of the methodologies, classification accuracy, Cohen’s kappa coefficient, and Nykopp’s information transfer are utilized. The first experiments on classifying motor imagery tasks are realized on the 3-class dataset (V) provided for BCI Competition III. Also, a 4-class problem is studied using the dataset (IIa) provided for BCI Competition IV. Then, 5 different tasks are studied in the METU Brain Research Laboratory to find the optimum number and type of tasks to control a motor imagery based BCI. Thereafter, an interface is designed for the paralyzed to control assistive environmental devices. Finally, a test application is implemented and online performance of the design is evaluated.


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...
Application of Wiener Deconvolution Model in P300 Spelling Paradigm
Erdogan, Balkar; Gençer, Nevzat Güneri (2009-01-01)
Spelling Paradigm first introduced by Farwell and Donchin, is one of the Brain Computer Interface (BCI) applications that enables paralyzed people to communicate with their environment. In such a problem, user needs to focus on the characters which are randomly flashed row or column-wise on the computer screen in a small period of time. The accuracy in spelling words is the main problem in this scheme and the duration of the correct prediction is quite important. The purpose of this work is twofold: to anal...
Classification of 4-class Motor Imagery EEG Data with Common Sparse Spectral Spatial Pattern Method
Akinci, Berna; Gençer, Nevzat Güneri (2009-01-01)
Brain Computer Interface aims to provide a communication system with external media via thougths. For this purpose, brain signals are acquired from the scalp by EEG device and processed for characterization. In this work, the classification of movement imagery EEG data has been studied for left hand, right hand, foot and tongue movement imagination cases. Common Spatial Patterns (CSP) method and temporal filters have been used in classification and Common Sparse Spectral Spatial Patterns (CSSSP) method has ...
Prototype Hardware Design for Brain Computer Interface Applications
Erdogan, Balkar; Akinci, Berna; Acar, Erman; Usakli, Ali Buelent; Gençer, Nevzat Güneri (2009-01-01)
Brain Computer Interface (BCI) is an alternative communication pathway between the human brain and outside world in which only the brain activity is interpreted in a special way. These systems are based on the electrical activity of the brain that can be measured via Electroencephalography (EEG) devices. BCI enables people with severe motor disorders (like ALS) to communicate with their environment or control a wheelchair for their movement by using the EEG signals. In this study, a prototype data acquisito...
Classification in Frequency Domain of EEG Signals of Motor Imagery for Brain Computer Interfaces
Halıcı, Uğur (2009-05-22)
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.
Citation Formats
E. Acar, “Classification of motor imagery tasks in EEG signal and its application to a brain-computer interface for controlling assistive environmental devices,” M.S. - Master of Science, Middle East Technical University, 2011.