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.
Over the last few decades, the use of electroencephalography (EEG) signals for motor imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep learning have also gained widespread attention and used in various application such as natural language processing, computer vision and speech processing. However, deep learning has been rarely used for MI EEG signal classification...
In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained...
Nowadays classification of electroencephalography (EEG) signals have brought new perspectives in the understanding of the brain. Establishing associated characteristics to certain stimulus in EEG is a monumental work due to complexity of the brain responses. For EEG classification several methods have been proposed. Among various statistical methods, Neural Networks (NN) have demonstrated capability...
Signal classification is based on the extraction of several features that will be used as inputs of a classifier. The selection of these features is one of the most crucial parts, because they will design the search space, and, therefore, will determine the difficulty of the classification. Usually, these features are selected by using some prior knowledge about the signals, but there is no method...
This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) classifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power...
P300 speller is a traditional brain computer interface paradigm and focused by lots of current BCI researches. In this paper a support vector machine based recursive feature elimination method was adapted to select the optimal channels for character recognition. The margin distance between target and nontarget stimulus in feature space was evaluated by training SVM classifier and then the features...
A simple method is developed for selecting effective channels and feature dimensions automatically at the training stage of BCI systems. The method is applied on feature vectors constructed with all the EEG channels used for recording. Performance was evaluated with EEG data which was preprocessed by band pass filtering and feature vectors constructed by band powers. The classification method used...
Brain computer interface is one of the most recent and latest hot field in Computer Science which emerged in order to help some handicapped people. This paper investigates different classification algorithms that deal with the BCI P300 speller diagram. The system used is composed of an ensemble of Support vector machines. Three different methods are used, namely weighted ensemble of SVM, channel selection...
This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired...
In BCI research community, support vector machine (SVM) is an effective method for motor imagery (MI)-based electroencephalographic (EEG) classification. However, the computation of decision function during SVM classification stage for a new EEG trial is time-consuming due to the large number of support vectors (SV). This paper proposes a new method to reduce the number of support vectors so that...
Brain Computer Interface Systems (BCIs) allow the identification of volitive brain activity patterns. This allows their use as input channels for alternative communication and computer access systems by patients suffering from severe motor disabilities. This paper presents preliminary results obtained after extracting four different features from EEG signals in order to recognize the activity patterns...
In this paper, we propose a tensorial approach to single trial recognition in a EEG-based BCI system related to movement related potentials. In this approach input data are considered as tensors instead of more conventional vector or matrix representations. Feature extraction for multiway EEG spectral tensors is solved by using tensor (multi-array) decompositions. For the same EEG motor imagery dataset,...
Brain Computer Interface (BCI) technology allows a person to control a device by bypassing the use of muscular activity. In previous studies, Signal processing and classification methods play a decisive role in the performance accuracy in BCI application, and there is a current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper designs...
Feature extraction is an important technique for complex, multivariate data containing various attributes. In this paper, we propose new detection schemes to help diagnosing epilepsy and detecting the onset of epileptic seizures. These schemes are based on the dynamic principle component analysis (PCA) approach and on partially extracted features. We propose a detection performance measure for evaluation...
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 work describes the development and evaluation of a recognizer for different levels of cognitive workload in the car. We collected multiple biosignal streams (skin conductance, pulse, respiration, EEG) during an experiment in a driving simulator in which the drivers performed a primary driving task and several secondary tasks of varying difficulty. From this data, an SVM based workload classifier...
The non-stationary nature of the electroencephalogram (EEG) poses a major challenge for the successful operation of a brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements and the proceeding multiple sessions can originate as a result of alterations in the subject's brain process, new cortical activities, change of recording conditions...
Machine Learning techniques are routinely applied to Brain Computer Interfaces in order to learn a classifier for a particular user. However, research has shown that classification techniques perform better if the EEG signal is previously preprocessed to provide high quality attributes to the classifier. Spatial and frequency-selection filters can be applied for this purpose. In this paper, we propose...
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...
Electroencephalography (EEG) analysis by physicians is intricate, time consuming and needs to experience. Therefore automated systems for EEG analysis and classification are able to help physician. EEG signal in the field of time is raw and complex so it's not suitable for automated system. Therefore appropriate features of EEG signal becomes extraction using signal processing methods (in this paper...
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.