Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mahdi Khezri; Seyed Mohammad Firoozabadi; Seyed Ahmad Reza Sharafat
Volume 8, Issue 4 , February 2015, , Pages 339-358
Abstract
In this study, we propose decision level fusion of multimodal physiological signals to design an affect identification system using the MIT database. Four types of physiological signals, including blood volume pressure (BVP), respiration rate (RSP), skin conductance and facial muscles activities (fEMG) ...
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In this study, we propose decision level fusion of multimodal physiological signals to design an affect identification system using the MIT database. Four types of physiological signals, including blood volume pressure (BVP), respiration rate (RSP), skin conductance and facial muscles activities (fEMG) were utilized as affective modalities. To collect the above-mentioned database, researchers used personalized imagery to elicit the desired affective states from a single subject and recorded the corresponding physiological signals simultaneously. In this study, the best subset of features for each signal was determined using previously calculated time and frequency domain features. To this end, sequential floating forward selection (SFFS) and RELIEF feature selection algorithms were evaluated. A new feature set, formed by concatenating the selected features, was partitioned into three subsets. Each subset was then fed into a classifier to identify the desired affective states. The majority voting method was applied to fuse the results obtained by the subsystems. Three types of classification methods, namely SVM, LDA and KNN were evaluated to design an affect identification system. The results showed remarkable performance from the system in identifying the desired scenarios with an acceptable accuracy and speed of response. Using the RELIEF feature selection method, along with SVM as a classifier, an overall recognition accuracy of 93.8% was obtained, which is better than the results reported with the use of the above-mentioned database so far.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Mehdi Ramezani; Ahmad Reza Sharafat
Volume 4, Issue 2 , June 2010, , Pages 123-134
Abstract
In this paper, we propose a novel approach for classification of surface electromyogram (sEMG) signal with a view to controlling myoelectric prosthetic devices. The sEMG signal generated during isometric contraction is modeled by a stochastic process whose probability density function (PDF) is non- Gaussian ...
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In this paper, we propose a novel approach for classification of surface electromyogram (sEMG) signal with a view to controlling myoelectric prosthetic devices. The sEMG signal generated during isometric contraction is modeled by a stochastic process whose probability density function (PDF) is non- Gaussian for low levels of applied force. Since the PDF of ambient noise is assumed to be Gaussian, we extract correntropy features, as they contain information on non-Gaussian components (the sEMG signal) only; and utilize the linear discriminant analysis (LDA) to classify the sEMG signal using correntropy features. Our proposed method has lower classification error and requires much less computations as compared to other existing advanced methods.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Kianoush Nazarpour; Ahmad Reza Sharafat; Seyed Mohammad Firouzabadi
Volume 1, Issue 3 , June 2007, , Pages 189-199
Abstract
A novel approach to surface electromyogram (sEMG) signal classification using its higher order statistics (HOS) is presented in this study. As the probability density function of the sEMG during isometric contraction in some cases is very close to the Gaussian distribution, it is frequently assumed to ...
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A novel approach to surface electromyogram (sEMG) signal classification using its higher order statistics (HOS) is presented in this study. As the probability density function of the sEMG during isometric contraction in some cases is very close to the Gaussian distribution, it is frequently assumed to be Gaussian. As this assumption is not valid when the force is small, in this paper, we consider the non-Gaussian characteristics of the sEMG, and compute the second-, the third- and the fourth order statistics of the sEMG as its features. These features are used to classify four upper limb primitive motions, i.e., elbow flexion (EF), elbow extension (EE), forearm supination (FS), and forearm pronation (FP). We used the sequential forward selection (SFS) method to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information, and apply the Knearest neighbor method for classification. Our approach is robust against statistical variations in noise, and does not require additional computations compared to existing methods for providing high rates of correct classification of the sEMG, which makes it useful in devising real-time sEMG controlled prostheses.
