Feature extraction of motor imagery electroencephalography based on time-frequency-space domains / 生物医学工程学杂志

The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.

Application of the quality evaluation of traditional Chinese herbal medicines using chromatography of fingerprints / 生物医学工程学杂志

Chromatography of fingerprint as an important tool has been used in identification and quality control of herbal medicines, and it is gaining more and more attention. Among the various methods, chromatography gradually becomes the mainstream for its characteristics. This paper describes the techniques of chromatography of fingerprint including pretreatments for sample data set, the establishment of chromatographic fingerprint and fingerprint visualization. It emphasizes several analysis methods and their scope of application. Visualization technology combined with fingerprint makes analysis more intuitive. Finally, existing key problems and future works were also discussed.

Empirical mode of combination of the wavelet threshold filtering and empirical mode decomposition (EMD) based on energy estimate / 生物医学工程学杂志

According to the frequency overlapping of intrinsic mode function (IMF) based on the temporal and spatial filtering of empirical mode decomposition (EMD), which will lead to the question of useful signals and noises filtered together, we proposed a method that numbers of IMF is determined by energy estimate, temporal and spatial filtering combing wavelet threshold and EMD integrating wavelet local signal characteristics of time and scale domain. This method not only used multi-resolution wavelet transform features, but also combined the EMD and Hilbert decomposition of the adaptive spectral analysis of instantaneous frequency and significance of the relationship between energy, so as to solve the problem of useful signal being weakened. With MIT/BIH ECG database standard data subjects, experimental results showed it was an effective method of data processing for handling this type of physiological signals under strong noise.