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The paper presents the application of a single-class Support Vector Machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG registration. The diagnostic features used in recognition are derived from the directed transfer function description, determined for different ranges of EEG signals. The results of the performed numerical experiments for the localization...
Results in literature show that the convergence of the Short-Term Maximum Lyapunov Exponent (STLmax) time series, extracted from intracranial EEG recorded from patients affected by intractable temporal lobe epilepsy, is linked to the seizure onset. When the STLmax profiles of different electrode sites converge (high entrainment) a seizure is likely to occur. In this paper Renyipsilas Mutual information...
Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data. It was successfully applied to learn spectral features from EEG data. However, the size of a data matrix grows, NMF suffers from dasiaout of memorypsila problem. In this paper we present a memory-reduced method where we downsize the data matrix using CUR decomposition before NMF is applied. Experimental...
Extracting brain rhythms from EEG signals has many applications including Brain Computer Interfacing. Here, we demonstrate how ICA with Reference (ICA-R) is used to extract brain rhythms, using appropriate reference signals. In particular, we evaluate four criteria for generating reference signals to use with ICA-R. We demonstrate the performance of these techniques in extracting mu and beta rhythms...
In this paper, an electroencephalogram (EEG)-based brain computer interface (BCI) is proposed for two dimensional cursor control. The horizontal and vertical movements of the cursor are controlled by mu/beta rhythm and P300 potential respectively. The main advantages of this system are: (i) two almost independent control signals are produced simultaneously; (ii) the cursor can be moved from a random...
Energy is very important in electroencephalogram (EEG) signal classification. In this paper, a criterion called extreme energy difference (EED) is devised, which is a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EED criterion, and therefore EED can be considered...
In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used...
This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10...
The paper investigates the possibility of using empirical mode decomposition (EMD) method to detect the mu rhythm of motor imagery EEG signal. Recently the mu rhythm by motor imagination has been used as a reliable EEG pattern for brain-computer interface (BCI) system. Considering the non-stationary characteristics of the motor imagery EEG, the EMD method is proposed to detect the mu rhythm during...
A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm...
This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI competitions II and III. The feature extraction can be performed in real time with low-cost discrete wavelet transforms. Classification results obtained with the new feature vectors are close to the state of art techniques, while...
Though the olfactory model entitled KIII has been widely used to pattern recognition, it only can give bare prediction. Combining EM model with the transductive confidence machine, a novel method to recognize hypoxia electroencephalogram (EEG) with a preset confidence level is proposed in this paper. This method can make prediction with confidence measure rather than bare prediction. The experimental...
Artificial emotion study will be of utmost importance in future artificial intelligence research. In this paper, an emotion understanding system based on brain activity and ldquoGISTrdquo is newly proposed to categorize emotions reflected by natural scenes. According to the strong relationship of human emotion and the brain activity, functional magnetic resonance imaging (fMRI) and electroencephalography...
A Brain-Computer Interface (BCI) is an interface that directly analyzes brain activity to transform user intentions into commands. Many known techniques use the P300 event-related potential by extracting relevant features from the EEG signal and feeding those features into a classifier. In these approaches, feature extraction becomes the key point, and doing it by hand can be at the same time cumbersome...
Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability...
Recent developments in nonlinear dynamics and the theory of chaos have shown deterministic chaotic property of EEGs. Such evidences made the researchers try to take advantage of the chaotic behavior in artificial neural networks. According to the natural selection theory a good problem-solver should have two main properties: The ability of emerging various solutions for problem and existence of a...
The KIV model is a biologically inspired hierarchical model that describes non-linear dynamics found in brains. Previous animal and human EEG measurements indicated the presence of jumps in the spatio-temporal EEG patterns, which are relevant to cognitive processing. The present work introduces the KIV model to simulate phase transitions in EEG signals. Phase transitions have non-stationary and intermittent...
Model field theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components...
Recent research has shown that neural networks (NNs) or self-organizing fuzzy NNs (SOFNNs) can enhance the separability of motor imagery altered electroencephalogram (EEG) for brain-computer interface (BCI) systems. This is achieved via the neural-time-series-prediction-preprocessing (NTSPP) framework where SOFNN prediction models are trained to specialize in predicting the EEG time-series recorded...
A BCI-FES training platform has been designed for rehabilitation on chronic stroke patients to train their upper limb motor functions. The conventional functional electrical stimulation (FES) was driven by userspsila intention through EEG signals to move their wrist and hand. Such active participation was expected to be important for motor rehabilitation according to motor relearning theory. The common...
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