Disordered high-frequency oscillation in face processing in schizophrenia patients

Liu, Miaomiao; Pei, Guangying; Peng, Yinuo; Wang, Changming; Yan, Tianyi; Wu, Jinglong

doi:10.1097/md.0000000000009753

Cited by 7 publications

(8 citation statements)

References 52 publications

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“…The direction parameter of each key point is specified by the gradient direction distribution characteristic of the key point neighbouring pixels to make the operator have rotation invariance [27]

m ()(, x, y) = \sqrt{(\begin{matrix} 1em4pt \\ L (x + 1, y) - L {()(, x - 1, y)}^{2} \\ + L (x, y + 1) - L {()(, x, y - 1)}^{2} \end{matrix})}

\begin{matrix} right leftthickmathspace.5em \\ θ (x, y) = a \tan 2 \\ ((\begin{matrix} 1em4pt \\ L (x, y + 1) \\ - L (y - 1) \end{matrix}) / (\begin{matrix} 1em4pt \\ L (x + 1, y) \\ - L (x - 1, y) \end{matrix})) \end{matrix}

Equation (8) is the modulus and direction formula for the gradient at

()(, x, y)

. The scale used by L is the scale at which each key point is located [28]. In the actual calculation, the sample is sampled in the neighbourhood window centred on the key point, and the histogram is used to count the gradient direction of the neighbourhood pixel.…”

Section: Methodsmentioning

confidence: 99%

“…Among them, every 10° is one column, and there are 36 columns in total. The peak of the histogram represents the main direction of the neighbourhood gradient at the key point [28]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Equation ( 8) is the modulus and direction formula for the gradient at x, y . The scale used by L is the scale at which each key point is located [28]. In the actual calculation, the sample is sampled in the neighbourhood window centred on the key point, and the histogram is used to count the gradient direction of the neighbourhood pixel.…”

Section: Algorithm Model Constructionmentioning

confidence: 99%

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Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

Zheng

Tong

Zhao³

et al. 2020

IET image process

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The brain avatar of schizophrenic patients is different from the normal human brain avatar, and it is difficult to overcome the complex environmental effects of the brain through traditional magnetic resonance imaging (MRI). In order to improve the accuracy of MRI in detecting brain information in patients with schizophrenia, this study is based on the support vector machine classification algorithm and combined with multimodal MRI detection method to construct a detection model suitable for patients with schizophrenia. In addition, this study combines the existing test cases to divide the brain into regions and design a comparative experiment to study the accuracy of the model proposed in this study. Finally, the study draws the results by sub‐regional comparison. Studies have shown that the algorithm model of this study has certain effects on brain detection in patients with schizophrenia, and can be applied to practice, and can provide theoretical reference for subsequent related research.

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m ()(, x, y) = \sqrt{(\begin{matrix} 1em4pt \\ L (x + 1, y) - L {()(, x - 1, y)}^{2} \\ + L (x, y + 1) - L {()(, x, y - 1)}^{2} \end{matrix})}

\begin{matrix} right leftthickmathspace.5em \\ θ (x, y) = a \tan 2 \\ ((\begin{matrix} 1em4pt \\ L (x, y + 1) \\ - L (y - 1) \end{matrix}) / (\begin{matrix} 1em4pt \\ L (x + 1, y) \\ - L (x - 1, y) \end{matrix})) \end{matrix}

Equation (8) is the modulus and direction formula for the gradient at

()(, x, y)

Section: Methodsmentioning

confidence: 99%

“…Among them, every 10° is one column, and there are 36 columns in total. The peak of the histogram represents the main direction of the neighbourhood gradient at the key point [28]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Section: Algorithm Model Constructionmentioning

confidence: 99%

See 1 more Smart Citation

Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

Zheng

Tong

Zhao³

et al. 2020

IET image process

View full text Add to dashboard Cite

show abstract

“…Then, we obtained the purified EEG signals of each group of experimental subjects. A new estimation of the time-frequency (TF) energy based on the wavelet transform of the artifact-free data was used, which provided a better compromise between time and frequency resolutions than short-term Fourier transforms [ 42 , 43 ]. The artifact-free data were convoluted by complex Morlet’s wavelets, which have a Gaussian shape both in the time domain and in the frequency domain around its central frequency.…”

Section: Methodsmentioning

confidence: 99%

Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment

Pei

Chen

et al. 2018

Sensors

Self Cite

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Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by regulating brain activity, which can lead to cognitive enhancement in healthy people and better rehabilitation in patients. The increased use of EEG neurofeedback highlights the urgent need to reduce the discomfort and preparation time and increase the stability and simplicity of the system’s operation. Based on brain-computer interface technology and a multithreading design, we describe a neurofeedback system with an integrated design that incorporates wearable, multichannel, dry electrode EEG acquisition equipment and cognitive function assessment. Then, we evaluated the effectiveness of the system in a single-blind control experiment in healthy people, who increased the alpha frequency band power in a neurofeedback protocol. We found that upregulation of the alpha power density improved working memory following short-term training (only five training sessions in a week), while the attention network regulation may be related to other frequency band activities, such as theta and beta. Our integrated system will be an effective neurofeedback training and cognitive function assessment system for personal and clinical use.

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“…To reject artifacts, independent component analysis (ICA) was applied to the EEG signals, and the components responsible for the eye movements and blinks were rejected. The time-frequency analysis, which is based on the wavelet transform, can synchronously provide variations of the EEG signals in both the time and frequency domains [31]. The statistical analyses were conducted using SPSS 19 (SPSS, Chicago, IL, USA).…”

Section: Data Processing and Statistical Analysismentioning

confidence: 99%

BrainKilter: A Real-Time EEG Analysis Platform for Neurofeedback Design and Training

et al. 2020

Self Cite

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Neurofeedback targets self-regularized brain activity to normalized brain function based on brain-computer interface (BCI) technology. Although BCI software or platforms have continued to mature in other fields, little effort has been expended on neurofeedback applications. Hence, we present BrainKilter, a real-time electroencephalogram (EEG) analysis platform based on a ''4-tier layered model''. The purposes of BrainKilter are to improve portability and accessibility, allowing different users to choose various options to perform EEG processing, target stimulation-induction through a pipeline, and analyze data online, essentially, to design a protocol paradigm and applicable BCI technology for neurofeedback experiments. The data processing effectiveness and application value of BrainKilter were tested using multiple-parameter neurofeedback training, in which BrainKilter regulated the amplitude of mismatch negative (MMN) signals for healthy individuals. The proposed platform consists of a set of software modules for online protocol design and signal decoding that can be conveniently and efficiently integrated for neurofeedback design and training. The BrainKilter platform provides a truly easy-to-use environment for customizing the experimental paradigm and for optimizing the parameters of neurofeedback experiments for research and clinical neurofeedback applications using BCI technology. INDEX TERMS BrainKilter, BCI, MMN, neurofeedback, platform, real-time.

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Disordered high-frequency oscillation in face processing in schizophrenia patients

Abstract: Supplemental Digital Content is available in the text

Cited by 7 publications

References 52 publications

Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment

BrainKilter: A Real-Time EEG Analysis Platform for Neurofeedback Design and Training

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