Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Neural networks

Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods*1

In the article there are presented the results of recognition of seven emotional states (neutral, joy, sadness, surprise, anger, fear, disgust) based on facial expressions. Coefficients describing elements of facial expressions, registered for six subjects, were used as features. The features have been calculated for three-dimensional face model. The classification of features were performed using k-NN classifier and MLP neural network.

Emotion recognition using facial expressions

In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp ‐ the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system re ads the user's “intentions” by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.

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Brain-computer interface as measurement and control system the review paper

In this paper the solution of the specialized measuring system for electrocardiography (ECG) signal recording and introductory recognition is presented. The project aims at designing the complete PC-based Virtual Instrument as a "testing platform" for acquisition, processing, presenting, and distributing ECG data. A new design involving the latest technique in signal simulation was developed to create a controllable model of the electrocardiography signal. Then it was implemented for testing of the developed QRS detection algorithm, based on the time-frequency analysis method. The processing stage involving discrete wavelet transform was used to detect QRS complexes in the ECG signal. By using the controlled signal model and the real ones, the algorithm was shown to be advantageous in reducing ventilation artifacts and motion noise, resulting in good accuracy.

Effective simulation of signals for testing ECG analyzer

A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform (DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results then the algorithm based on discrete Fourier transform (DFT).

A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms

In the last few years Vector Quantisation has become an important technique in speech and image compression and recognition. The jump from one dimension to the multiple ones allows wealth of new ideas, concepts, techniques and applications. In this paper there is enclosed a desription of vector quantisation idea, the design techniques, optimization methods and practical implementations.

Remigiusz J. Rak

Papers

Emotion recognition using facial expressions

Brain-computer interface as measurement and control system the review paper

Effective simulation of signals for testing ECG analyzer

A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms

Vector Quantisation