This slide will provide a tutorial for preprocessing of fMRI data. The step-by-step process will be provided.
visit my website for more information:
http:/skyeong.net
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
Lec5: Pre-Processing Medical Images (III) (MRI Intensity Standardization)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
Lec5: Pre-Processing Medical Images (III) (MRI Intensity Standardization)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
This presentation is a starter for folks interested in the implementation and application of compressed sensing (CS) MRI. It includes a Matlab demo and list of well-known resources for CS MRI.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
In this study,
We propose a EEG analysis model using a nonlinear oscillator with one degree of freedom.
It doesn’t have a random term.
our study method identifies six model parameters experimentally.
Here is the detail: https://kenyu-life.com/2018/11/03/modeling_of_eeg/
Created by Kenyu Uehara
MagnetoenCephaloGraphy (MEG) is a technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
This slide includes various neuroimaging methods. Firstly, brief backgrounds of positron emission tomography (PET), diffusion tensor MRI, voxel-based morphometry will be introduced. Secondly, a theoretical explanation of BOLD fMRI and preprocessing will be introduced.
http://skyeong.net
This presentation is a starter for folks interested in the implementation and application of compressed sensing (CS) MRI. It includes a Matlab demo and list of well-known resources for CS MRI.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
In this study,
We propose a EEG analysis model using a nonlinear oscillator with one degree of freedom.
It doesn’t have a random term.
our study method identifies six model parameters experimentally.
Here is the detail: https://kenyu-life.com/2018/11/03/modeling_of_eeg/
Created by Kenyu Uehara
MagnetoenCephaloGraphy (MEG) is a technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
This slide includes various neuroimaging methods. Firstly, brief backgrounds of positron emission tomography (PET), diffusion tensor MRI, voxel-based morphometry will be introduced. Secondly, a theoretical explanation of BOLD fMRI and preprocessing will be introduced.
http://skyeong.net
A simple introduction to fMRI study design for social science and other researchers outside the field who might want to design a study using fMRI brain scanning technology
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. using Statistical Parametric Mapping (SPM8)
Preprocessing of fMRI data
Sunghyon Kyeong
sunghyon.kyeong@gmail.com
Institute of Behavioural Science in Medicine,
Yonsei University College of Medicine
2. Steps in the spatial preprocessing of
event related and resting state fMRI data
are the same.
3. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Summary of Preprocess
3
Input Output
EPI1.nii
EPI2.nii
…
aEPI1.nii
aEPI2.nii
…
aEPI1.nii
aEPI2.nii
…
meanaEPI.nii
aEPI1.nii (realigned)
aEPI2.nii (realigned)
rp_EPI.txt
…
meanaEPI.nii
anat.nii
meanaEPI.nii
anat.nii (coregistered)
anat.nii
aEPI1.nii
aEPI2.nii
…
wanat.nii
waEPI1.nii
waEPI1.nii
…
waEPI1.nii
waEPI2.nii
…
Slice Timing
Realignment
Coregistration
T1 → meanEPI
Normalisation
Smoothing
Event related fMRI analysis
Resting state fMRI analysis
Preprocessing
• Specify 1st-level in SPM
Individual GLM with Stimulus onset
and rp_EPI.txt as regressors
• Specify 2nd-level in SPM
Group-wise analysis
one sample, two sample, factorial
design, flexible design
• Linear detrending of EPI time series
at each voxel.
• bandpass filtering (0.009~0.08Hz) to
capture Low-frequency fluctuation
• regression nuisance parameters
such as head motion, white matter,
ventricle, and global signal
• Functional connectivity analysis and
Complex network analysis
swaEPI1.nii
swaEPI1.nii
…
5. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
DICOM Import
5
(1)
(2)
(3)
select .dcm files
select output directory
6. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Slice timing correction
(1) Data
select image files (*.nii or *.img)
Number of Slices
specify number of slices
TR
repetition time in second
TA
TA=TR-(TR/nslices)
Slice order
ex) 1:nslices (ascending)
ex) 1:2:nslices 2:2:nslices (interleaved)
Reference Slice
usually a middle slice in space
6
7. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Realignment
(1) Realign (Estimate & Reslice)
Data
slice timing corrected images (a*.nii)
Num Passes
Register to first
Resliced images
Mean Image Only
^a.*
(2)
(3)
select directory
(4)(5)
첫번째 volume에 모든 EPI
영상을 정합
meana~~.nii 가 생성되고,
coregistration에 사용됨.
7
8. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Co-registration
(1) Coregistration (Estimate)
Reference Image
select a mean EPI image which was
created after realignment process
Source Image
select an anatomical (T1) image
※ fMRI 과제가 여러개인 경우에 T1 영상을 각 fMRI 과에별로 복사해서 사용할 것을 권장함.
8
9. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Check Registration!
9
(1) restanat.nii
meanrest.nii
Select T1 and meanEPI images
※ T1과 meanEPI 영상이 잘 정합 되었는지 반드시 확인!!
10. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Normalisation
(1)
Normalise (Estimate&Write)
10
Source Image
select the coregistered T1 image
Images to Write
select the coregistered T1 & realigned
EPI images
Template Image
select T1 template file (T1.nii)
which is located at SPM8template
11. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Smoothing
11
(1)
(2)
(3)
^wa.*
12. Sunghyon Kyeong (Yonsei Univ) | Preprocessing and Analysis of fMRI data | p
Now, we have
12
swaEPI1.nii
swaEPI1.nii
…
Event Related fMRI analysis
- Task-related brain activation
- PPI analysis (connectivity)
Resting State fMRI analysis
- functional connectivity
- graph theoretical analysis
preprocessed fMRI data