Seyedeh Saeideh Zahedi Haghighi; Sayed Mahmoud Sakhaei; Mohammadreza Daliri
Volume 13, Issue 2 , August 2019, , Pages 95-104
Abstract
Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary ...
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Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary property of EEG, intrinsic mode functions (IMF) extracted by using empirical mode decomposition (EMD) and then first 3 IMFs selected. Each IMF converts into smaller pieces with a one-second window and power feature has been extracted from each piece. Then, by using a suitable mapping, the position of the electrodes in the 10-20 system becomes the position of the pixels in the picture. The extracted features are considered as pixel color components. To determine the class of valence, the set of all generated pictures is given as input to a deep learning network and output determine the high or low class of valence. The same process is used to determine the class of arousal. To examining the method, the DEAP dataset is used. By choosing 17×17 for the image size, the mean accuracy and standard deviation were obtained of 78.58% and 3.9 for the valence and 78.66% and 3.1 for the arousal which that shows a significant improvement compared to similar tasks.
Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Hannaneh Keyhanian; Sayed Mahmoud Sakhaei
Volume 12, Issue 3 , November 2018, , Pages 235-248
Abstract
The method of multi-beam beamforming is a low-computational adaptive beamforming method in which, instead of calculating the covariance matrix and inverting it for each point of the image, only one matrix is calculated for all points on the same radial distance. Then, to reduce the complexity of the ...
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The method of multi-beam beamforming is a low-computational adaptive beamforming method in which, instead of calculating the covariance matrix and inverting it for each point of the image, only one matrix is calculated for all points on the same radial distance. Then, to reduce the complexity of the inverse matrix calculation, the problem is solved in the beamspace domain. We introduce a new two-stage method to reduce the complexity of the minimum variance (MV) beamforming method, which outperforms the beamspace method in computational burden aspect in multi-beam method. In the first step, instead of using the signals of all array elements in calculating the covariance matrix, the signals of a decimated one are chosen such that the resulting covariance matrix contains all the correlation information of the signals. In the second stage, the weights of all elements of the array are determined by a proper interpolation method from the weights of the decimated array. According to the simulation results of point targets and cyst phantom, the new method has a performance similar to that of the beamspace multi-beam method in terms of resolution, contrast, and robustness against the errors with at least 3 times lower computational burden.