The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
Brain-Computer Interface (BCI) research hopes to improve the quality of life for people with severe motor disabilities by providing a capability to control external devices using their thoughts. To control a device through BCI, neural signals of a user must be translated to meaningful control commands using various machine learning components, e.g. feature extraction, dimensionality reduction and...
The P300 Speller is a Brain Computer Interface that enables communication using the EEG signal. The P300 wave is an Event Related Potential that occurs as a response to a familiar stimulus. This system can be used to aid persons who are unable to communicate via conventional methods. In this paper, the P300 Speller has been modified to allow communication in three languages: English, Sinhala and Tamil...
Our cognitive abilities can help us communicate without any visible action or words. Brain Computer Interfaces (BCI) achieves this communication using the brain waves. But for practical applications, system using BCI must be fast and accurate. In this paper, we present a method that uses SVM classifiers over ensemble of averaged data. Averaging data over number of trails removes the random noise and...
In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and...
We present a study of a support vector machine (SVM) application to brain-computer interface (BCI) paradigm. Four SVM kernel functions are evaluated in order to maximize classification accuracy of a four classes-based BCI paradigm utilizing a code-modulated visual evoked potential (cVEP) response within the captured EEG signals. Our previously published reports applied only the linear SVM, which already...
P300 speller for Brain-Computer Interface systems aim to provide a direct communication between computer - machine and human brain, without any muscular activity. The communication is provided by detecting the presence of P300 Event Related Potentials (ERPs) in the electroencophelogram (EEG) signals, recorded from scalp. The major problem associated with P300 spellers is the stratification of EEGs...
EEG is the most economical and effective tool for understanding the complex dynamic behavior of the brain and studying its physiological states. In the present work, hierarchical computer aided diagnostic system (HCAD) for classification of normal, ictal and inter-ictal of EEG signals is proposed. In the present work, three different HCAD systems comprising of SVM, KNN and PNN classifiers are proposed...
Mental tasks classification such as motor imagery based on EEG signals is a challenging issue in brain-computer interface (BCI) systems. Automatic classifier tuning seems to be an essential component in real-time BCI systems which makes the interface more reliable and easy to use and may offer the optimum configuration of classifier. This paper investigates the robustness of Least-Square Support Vector...
The MLSP competition (2010) purpose is to design a pattern recognition system for “mind reading”. This paper is a study of the EEG competition dataset and the crafting of the third place winning method. It shortly presents our signal processing methods for feature extraction, and channel selection. We accurately tuned all the parameters of these preprocessing stage before feeding a Gaussian SVM classifier...
This paper proposes an emotional stress recognition system with EEG signals using higher order spectra (HOS). A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional stress states of participants, Calm neutral and Negatively exited. After pre-processing the signals, higher order spectra are employed to extract...
Accurate classification of left and right hand motor imagery of EEG is an important issue in brain-computer interface (BCI). Here, discrete wavelet transform was firstly applied to extract the features of left and right hand motor imagery in EEG. Secondly, Fisher Linear Discriminant Analysis was used with two different threshold calculation methods and obtained good misclassification rate. We also...
The human neural responses associated with cognitive events, referred as event related potentials (ERPs), can provide reliable inference for target image detection. Incremental learning has been widely investigated to deal with large datasets. To solve the problem of data growing over time in cross session studies, we apply an incremental learning support vector machines (SVM) method on single-trial...
Automatic classification of electroencephalography (EEG) signals, for different type of mental activities, is an active area of research and has many applications such as brain computer interface (BCI) and medical diagnoses. We introduce a simple yet effective way to use Kullback-Leibler (KL) divergence in the classification of raw EEG signals. We show that k-nearest neighbor (k-NN) algorithm with...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.