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Bioelectrical Signal Processing In Cardiac And Neurological Applications

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

A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior. Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors. Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach. Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation.

/pdf/emotion-classification-through-nonlinear-eeg-analysis-using-do0atd2f87.pdf

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.

Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images

An automatic artifact extraction system is proposed based on a hybridization of Stone’s BSS and genetic algorithm. This hybridization is called evolutionary Stone’s BSS algorithm (ESBSS). Original Stone’s BSS used short- and long-term half-life parameters as constant values, and the changes in these parameters will be affecting directly the separated signals; also there is no way to determine the best parameters. The genetic algorithm is a suitable technique to overcome this problem by finding randomly the optimum half-life parameters in Stone’s BSS. The proposed system is used to extract automatically the common artifacts such as ocular and heart beat artifacts from EEG mixtures without prejudice to the data; also there is no notch filter used in the proposed system in order not to lose any useful information.

/pdf/automatic-extraction-system-for-common-artifacts-in-eeg-23huj43yen.pdf

Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm

/pdf/blind-source-separation-techniques-based-eye-blinks-m6gf7aljm5.pdf

Blind Source Separation Techniques Based Eye Blinks Rejection in EEG Signals

An efficient Stone’s BSS (ESBSS) algorithm is proposed based on the joint between original Stone’s BSS (SBSS) and genetic algorithm (GA). Original Stone’s BSS has several advantages compared with independent component analysis (ICA) techniques, where the BSS problem in Stone’s BSS is simplified to generalized eigenvalue decomposition (GEVD), but it’s susceptible to the local minima problem. Therefore, GA is used to overcome this problem and to enhance the separation process. Performance of the proposed algorithm is first tested through a different pdf source, followed by artifact extraction test for EEG mixtures then compared with the original Stone’s BSS (SBSS) and other BSS algorithms. The results demonstrate proposed algorithm efficiency in real time blind extraction of both superGaussian and sub-Gaussian signals from their mixtures.

http://www.sersc.org/journals/IJSIP/vol7_no2/39.pdf

Enhancement of Source Separation Based on Efficient Stone's BSS Algorithm

Several problems in EEG-brain signal analysis are not solved, such as presence of an artifact during the recording process, particularly the eye artifact (ElectroOculoGram (EOG)) which makes the analysis of EEG-brain signals very difficult. Blind source separation technique is one of the important techniques used to clean the EEG signals from different types of artifacts. Independent component analysis (ICA) techniques are widely used for this purpose, but unfortunately the ICA techniques have inherent shortcoming such as source ambiguity and unordered components. Therefore, the researchers used ICA-Reference algorithm. The main problem in ICA-Reference algorithm is to find clean reference signal to extract the wanted signal. Recently, many algorithms proposed to generate the artifact reference, but unfortunately, clean artifact signal not satisfied. In this paper wavelet denoising technique is used to solve this problem by decompose the artifact reference signal into pure artifact signal and residual neural signal. The proposed algorithm used frontal channels instead of EOG channels to extract the EOG reference signal.

Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based on Wavelet Denoising Technique

With increasing scale and complexity of emerging networked applications, the concept of system of systems has emerged recently as a new way to address complex problems in a systematic manner. A key problem in this context is lack of a unifying framework for development and management of these systems. To avoid potential failures in building these systems, new methods, models, and techniques have been proposed. A more methodological approach is the model-based process. Systems modelling language (SysML) was created to implement this process; nevertheless, an underlying tool for expressing the unified totality of a system's processes and concepts is lacking. This paper proposes a new approach to a basic framework in this domain. As a case study, this paper applies a flow-based methodology to model physical actualisation of mechanics, electronics, information, actions, and control at an international airport. The resultant conceptual specification demonstrates that the methodology is capable of modelling various types of processes.

Ahmed Kareem Abdullah

Papers

Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm

Blind Source Separation Techniques Based Eye Blinks Rejection in EEG Signals

Enhancement of Source Separation Based on Efficient Stone's BSS Algorithm

Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based on Wavelet Denoising Technique

Flow-based systems modelling: application in airport system description