Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preli...

Power and magnitude square coherence estimates evaluated for EEG of alcoholics and control participants were used to attempt an automated discrimination of individuals suffering alcohol dependence. The estimates were obtained for non-overlapping consecutive EEG fragments of 0.5 second duration with parametric analyzers and used as features for Euclidean, Fisher, and Regression-based classifiers...

Electroencephalogram (EEG) recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is inv...

We propose an EEG classification algorithm for the mental task BCI paradigm that uses Echo State Networks (ESN). In this approach, ESN are used to model the dynamics of EEG during each of several mental tasks. Classification is performed by applying several of these models and assigning the class label associated with the ESN that produces the lowest forecasting error. Experiments performed on ...

This paper described the relationship between EEG signals and MI in BCI system. EEG signals are used to classify the direction of motioninto two kinds: left and right. We extracted features from original EEG data using STFT and put them into CNN. The result showed that the framework of STFT-CNN had higher average test accuracy. Furthermore, the generations of motor imagery were analyzed, and th...

With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded fr...

OBJECTIVE 
 Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. ...

OBJECTIVE To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). METHODS Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration w...

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been...

In this paper, we present a method of feature extraction for motor imagery single trial EEG classification, where we exploit nonnegative matrix factorization (NMF) to select discriminative features in the time-frequency representation of EEG. Experimental results with motor imagery EEG data in BCI competition 2003, show that the method indeed finds meaningful EEG features automatically, while s...