Investigation of Functional Connectivity During Working Memory Task and Hemispheric Lateralization in Left- and Right- Handers Measured by fNIRS

Shirzadi, Sima; Einalou, Zahra; Dadgostar, Mehrdad

doi:10.1016/j.ijleo.2020.165347

Cited by 14 publications

(12 citation statements)

References 81 publications

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“…According to the mother wavelet function, discrete WT (DWT) can decompose the signals to the different levels by shifting and dilating mother function in time. The decomposed signal can be reconstructed by selecting suitable coefficients in the time domain 29 . DWT generates a set of approximation and detail coefficients by repeating the decomposition of the signal and passing the signal through low‐pass and high‐pass filters, which any of them has a different frequency band.…”

Section: Wavelet Transformmentioning

confidence: 99%

“…The decomposed signal can be reconstructed by selecting suitable coefficients in the time domain. 29 DWT generates a set of approximation and detail coefficients by repeating the decomposition of the signal and passing the signal through low-pass and high-pass filters, which any of them has a different frequency band. The signal x(t) can be obtained by DWT for M total shifting and scaling.…”

Section: Introductionmentioning

confidence: 99%

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Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition

Doroudi

Lavassani

Shahrouzi

et al. 2022

Structural Contr & Hlth

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Section: Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition

Doroudi

Lavassani

Shahrouzi

et al. 2022

Structural Contr & Hlth

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“…2 Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. 3 Department of Electrical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.…”

Section: Authors' Contributionsmentioning

confidence: 99%

“…them into the signals related to device control. The input signals may be electromagnetic [1], infrared spectroscopy [2][3][4][5][6][7], magnetic resonance imaging [8,9], electro-echography [10] and EEG signal [11,12]. Among these techniques, the EEG signal is used more widely due to its simplicity, low cost, non-invasiveness and ability to register and analyze the data.…”

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confidence: 99%

Cursor movement detection in brain-computer-interface systems using the K-means clustering method and LSVM

et al. 2021

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In this study, we present the detection of the up-downward as well as the right- leftward motion of cursor based on feature extraction. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up-downward and left- rightward movements in each person to increase by 2–10 %.

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“…Regarding its implementation, a BCI system evaluates the specific features of the brain activity, and accordingly translates them into the signals related to device control. The input signals may be electromagnetic [1], infrared spectroscopy [2][3][4][5][6][7], magnetic resonance imaging [8,9], electro-echography [10] and EEG signal [11,12]. Among these techniques, the EEG signal is used more widely due to its simplicity, low cost, non-invasiveness and ability to register and analyze the data.…”

Section: Introductionmentioning

confidence: 99%

Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

Mohammadi

Einalou

Hosseinzadeh

et al. 2020

Preprint

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In this study, we present the detection of the up- and downward as well as the right- and leftward motion of cursor based on feature extraction. Feature Extraction and selection for finding the proper classifier among the data mining methods are of great importance. In the proposed method, the hybrid K-means clustering algorithm and the linear support vector machine (LSVM) classifier have been used for extracting the important features and detecting the cursor motion. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. The database of the Karadeniz Technical University of Turkey has been used in the present article. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up- and downward and left- and rightward movements in each person to increase by 2–10%.

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Investigation of Functional Connectivity During Working Memory Task and Hemispheric Lateralization in Left- and Right- Handers Measured by fNIRS

Cited by 14 publications

References 81 publications

Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition

Identifying the dynamic characteristics of super tall buildings by multivariate empirical mode decomposition

Cursor movement detection in brain-computer-interface systems using the K-means clustering method and LSVM

Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

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