Method for EEG Feature Extraction Based on Morphological Pattern Spectrum

Han, Lijing; Zhang, Lijun; Yang, Jianhong; Li, Min; Xu, Jun

doi:10.1109/icsap.2009.19

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Information about morphology and size of thermal images is then contained in the pattern spectrum, which is used to form feature vectors, as explained in the previously reported work [21]. In recent years, the pattern spectrum has been used in several applications: to classify the mental tasks in braincomputer interfaces [31], for an automatic classification of noisy and incomplete shoeprint images [32], and for pattern recognition of partial discharge in high voltage [33].…”

Section: Mathematical Morphologymentioning

confidence: 99%

Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

Hernandez-Contreras

Peregrina-Barreto

Rangel-Magdaleno

et al. 2015

Infrared Physics & Technology

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Section: Mathematical Morphologymentioning

confidence: 99%

Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

Hernandez-Contreras

Peregrina-Barreto

Rangel-Magdaleno

et al. 2015

Infrared Physics & Technology

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“…Information at different scales obtained from multiscale MM needs to be properly summarized or described to facilitate feature extraction (Yu et al, 2009). Pattern spectrum is a commonly adopted measure and led to the development of many pattern recognition methods (Maragos, 1989;Weng, 2008;Han et al, 2009), such as support vector machine (SVM), proximal support vector machine (PSVM) and neural network.…”

Section: Feature Extraction In Multi-scale Mathematical Morphologymentioning

confidence: 99%

A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis

Chen

Sun

et al. 2013

Journal of Vibration and Control

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The condition monitoring and fault diagnosis of rolling element bearings play an important role in the safe and reliable operation of rotating machinery. Feature extraction based on vibration signals is an effective means to identify the operating condition of rolling element bearings. Methods based on multi-scale mathematical morphology (MM) have recently been developed to extract features from one-dimensional signals. In this paper, a new double-dot structuring element (SE) is constructed for multi-scale MM. A pattern spectrum, obtained from the multi-scale MM, is used as a feature extraction index. A correlation analysis gives the final identification result by utilizing information over a whole pattern spectrum. Compared with the most commonly used flat SE, the double-dot SE can extract more features of original signals at different scales. Vibration signals, measured from defective bearings with outer race faults, inner race faults and ball faults, are used to evaluate the fault detection ability of the proposed SE and bearing fault diagnosis method. Results show that faults at different levels can be identified, including ball fault; and the location of outer race fault can also be differentiated.

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“…Only addition, subtraction and extreme operations are usually employed, and that is why it has good real-time performance. Morphological pattern spectrum [5,6] is a multiscale analysis method, which describes morphological information difference among different scales, and could be a new feature extraction method in pattern recognition problem. In this paper, pattern spectrum values of throwing steel acoustic signals are first extracted and support vector machine classifier is then selected to distinguish two conditions.…”

Section: Introductionmentioning

confidence: 99%

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

Yang

Lin

et al. 2012

AMR

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Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

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Method for EEG Feature Extraction Based on Morphological Pattern Spectrum

Cited by 5 publications

References 5 publications

Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

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