DSSV2022_np_eeg_toolbox
•
0 likes•1 view
This document introduces the NP-EEG Toolbox, an open-source MATLAB toolbox for analyzing EEG data collected using naturalistic paradigms. The toolbox was developed by researchers at the Institute of Statistical Science, Academia Sinica to streamline their analysis of EEG signals recorded during real-world activities. The toolbox architecture includes modules for EEG preprocessing, connectivity analysis, statistics, and visualization. Future work will involve improving documentation, including sample datasets, developing a graphical user interface, and refactoring code. The goal is to provide a useful tool that saves researchers time and effort in analyzing naturalistic EEG data.
Report
Share
Report
Share
Download to read offline
Recommended
Advances in automating analysis of neural time series
This document describes advances in automating the analysis of neural time series data, specifically EEG and MEG data. It discusses challenges with manual analysis methods, including lack of reproducibility and scalability. It then summarizes the author's contributions to improving automation and reproducibility through tools like a Brain Imaging Data Structure validator, a tutorial on group analysis methods, and a method called Autoreject for automatically rejecting artifact-contaminated trials in EEG/MEG data. The goal of this work is to develop fully automated and reproducible analysis methods that can handle large datasets.
Lecture artificial neural networks and pattern recognition
This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
- The basic anatomy and function of artificial neurons and how they are modeled after biological neurons.
- Different types of neural networks including feedforward networks, recurrent networks, self-organizing maps, and Hopfield networks.
- Popular supervised and unsupervised learning algorithms like backpropagation and self-organizing feature maps.
- Examples of applications like handwritten character recognition, stock price prediction, and memory recall in Hopfield networks.
The document serves as an introduction for students to understand the basic concepts and applications of artificial neural networks.
Lecture artificial neural networks and pattern recognition
This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
- The basic anatomy and function of artificial neurons and how they are modeled after biological neurons.
- Different types of neural networks including feedforward networks, recurrent networks, self-organizing maps, and Hopfield networks.
- Popular supervised and unsupervised learning algorithms like backpropagation and self-organizing feature maps.
- Examples of applications like handwritten character recognition, stock price prediction, and memory recall in Hopfield networks.
The document serves as an introduction for students to understand the basic concepts and applications of artificial neural networks.
Reservoir computing fast deep learning for sequences
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
Automated_EEG_artifact_elimination_by_applying_mac.pdf
This document summarizes an approach for automated EEG artifact elimination using machine learning algorithms applied to features extracted from independent component analysis (ICA). The method involves preprocessing the EEG data with filtering and ICA, then generating features from the ICA results like topographic maps and power spectra. Machine learning classifiers like support vector machines and artificial neural networks are trained on the features to classify components as artifacts or EEG. The best performance was 95% accuracy using a neural network classifier trained on features from topographic map filtering and power spectra. This automated approach avoids time-consuming manual classification and enables real-time artifact removal.
EEG course.pptx
This document discusses preparing and analyzing EEG data. It covers the history of EEG, how to record EEG data by applying electrodes to standardized scalp locations and synchronizing recordings with stimulus presentations. It also discusses preprocessing steps like downsampling, filtering, re-referencing, artifact removal through ICA and visual inspection, and epoching data to create ERPs. Examples of ERP components like the N170 face processing component and error-related negativity are also mentioned.
Brain Control Club progress meeting Project: Introduction and Projects
This document summarizes the first progress meeting of the Brain Control Club. It discusses the club's goals of understanding brain-computer interface theory and applications through hands-on projects. Four active projects are summarized: Project Nirvana aims to develop tools for detecting mental states using physiological signals; Project Morpheus investigates targeted memory reactivation during sleep; a brain-based music composition project uses steady-state responses to modulate music; and the EEG-synth project applies brain signals to interactive art installations. The document reviews progress and next steps for each project.
ct_meeting_final_jcy (1).pdf
This document provides an overview of a refresher course on deep learning for CT reconstruction. The course covers an introduction to CNNs, the biological and mathematical origins of deep learning, and various applications of deep learning to CT reconstruction problems like low-dose CT, sparse-view CT, and interior tomography. Successful demonstrations of deep learning approaches for these image reconstruction problems are highlighted from recent works. The role of convolutional and pooling layers in CNNs is discussed. Reasons for why deep learning works well for reconstruction problems are explored, including the mathematical understanding of CNNs in terms of deep convolutional framelets and the lifting of signals to higher dimensional spaces using Hankel matrices. The document concludes by discussing some open problems and providing information on datasets
Recommended
Advances in automating analysis of neural time series
This document describes advances in automating the analysis of neural time series data, specifically EEG and MEG data. It discusses challenges with manual analysis methods, including lack of reproducibility and scalability. It then summarizes the author's contributions to improving automation and reproducibility through tools like a Brain Imaging Data Structure validator, a tutorial on group analysis methods, and a method called Autoreject for automatically rejecting artifact-contaminated trials in EEG/MEG data. The goal of this work is to develop fully automated and reproducible analysis methods that can handle large datasets.
Lecture artificial neural networks and pattern recognition
This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
- The basic anatomy and function of artificial neurons and how they are modeled after biological neurons.
- Different types of neural networks including feedforward networks, recurrent networks, self-organizing maps, and Hopfield networks.
- Popular supervised and unsupervised learning algorithms like backpropagation and self-organizing feature maps.
- Examples of applications like handwritten character recognition, stock price prediction, and memory recall in Hopfield networks.
The document serves as an introduction for students to understand the basic concepts and applications of artificial neural networks.
Lecture artificial neural networks and pattern recognition
This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
- The basic anatomy and function of artificial neurons and how they are modeled after biological neurons.
- Different types of neural networks including feedforward networks, recurrent networks, self-organizing maps, and Hopfield networks.
- Popular supervised and unsupervised learning algorithms like backpropagation and self-organizing feature maps.
- Examples of applications like handwritten character recognition, stock price prediction, and memory recall in Hopfield networks.
The document serves as an introduction for students to understand the basic concepts and applications of artificial neural networks.
Reservoir computing fast deep learning for sequences
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
Automated_EEG_artifact_elimination_by_applying_mac.pdf
This document summarizes an approach for automated EEG artifact elimination using machine learning algorithms applied to features extracted from independent component analysis (ICA). The method involves preprocessing the EEG data with filtering and ICA, then generating features from the ICA results like topographic maps and power spectra. Machine learning classifiers like support vector machines and artificial neural networks are trained on the features to classify components as artifacts or EEG. The best performance was 95% accuracy using a neural network classifier trained on features from topographic map filtering and power spectra. This automated approach avoids time-consuming manual classification and enables real-time artifact removal.
EEG course.pptx
This document discusses preparing and analyzing EEG data. It covers the history of EEG, how to record EEG data by applying electrodes to standardized scalp locations and synchronizing recordings with stimulus presentations. It also discusses preprocessing steps like downsampling, filtering, re-referencing, artifact removal through ICA and visual inspection, and epoching data to create ERPs. Examples of ERP components like the N170 face processing component and error-related negativity are also mentioned.
Brain Control Club progress meeting Project: Introduction and Projects
This document summarizes the first progress meeting of the Brain Control Club. It discusses the club's goals of understanding brain-computer interface theory and applications through hands-on projects. Four active projects are summarized: Project Nirvana aims to develop tools for detecting mental states using physiological signals; Project Morpheus investigates targeted memory reactivation during sleep; a brain-based music composition project uses steady-state responses to modulate music; and the EEG-synth project applies brain signals to interactive art installations. The document reviews progress and next steps for each project.
ct_meeting_final_jcy (1).pdf
This document provides an overview of a refresher course on deep learning for CT reconstruction. The course covers an introduction to CNNs, the biological and mathematical origins of deep learning, and various applications of deep learning to CT reconstruction problems like low-dose CT, sparse-view CT, and interior tomography. Successful demonstrations of deep learning approaches for these image reconstruction problems are highlighted from recent works. The role of convolutional and pooling layers in CNNs is discussed. Reasons for why deep learning works well for reconstruction problems are explored, including the mathematical understanding of CNNs in terms of deep convolutional framelets and the lifting of signals to higher dimensional spaces using Hankel matrices. The document concludes by discussing some open problems and providing information on datasets
ICC2017 UUUI sessions 6205-1
ICC2017 Washington - http://icc2017.org/
6205.1
Exploring the possibilities of eye tracking and EEG integration for cartographic context
Merve Keskin
Istanbul Technical University
Kristien Ooms
Ghent University
A. Ozgur Dogru
Istanbul Technical University
Philippe De Maeyer
Universiteit Gent
FrB18_2_Krishnamoorthy_Venkatasubramanian.pptx
This document summarizes a study that used convolutional neural networks (CNNs) to detect microsleep states from EEG data. The researchers compared using oversampling and cost-based learning techniques to address class imbalance when training the CNN models. They also compared using raw EEG data versus 2D maps of log power spectral features as the CNN input. The CNN models achieved high performance in detecting microsleep states, outperforming other machine learning methods. Using raw EEG data and cost-based learning provided the best combination of high performance and reduced training time. Future work could explore ensemble and parallel CNN architectures.
Visual Analytics in Omics - why, what, how?
This document discusses visual analytics in omics data. It begins by noting the shift from hypothesis-driven to data-driven research due to large datasets. Visual analytics can help explore these data by opening the "black box" of algorithms and enabling researchers to develop hypotheses. Effective visualization leverages human perception through techniques like preattentive vision and Gestalt laws. Challenges to visual analytics include scalability issues for large datasets and identifying interesting patterns for further analysis. Examples demonstrate data exploration, filtering, and user-guided analysis in genomic applications.
AI and neurological disorder- Epilepsy.,work with brain and omputers
This presentation discusses using artificial intelligence techniques to help detect and diagnose epilepsy based on EEG signals. The objectives are to pre-process EEG data using techniques like noise reduction and filtering, extract features using CNN, detect epileptic features in the waves, and detect if a patient is epileptic. The system will collect EEG data from individuals with epilepsy, preprocess the data, develop an AI model using deep learning to recognize seizure patterns, and train the model on the preprocessed EEG data. Potential applications include early seizure detection for prompt medical intervention, personalized treatment plans tailored to individual seizure patterns, and reducing the healthcare burden by ensuring critical cases receive attention.
Artificial Neural Network for hand Gesture recognition
This document discusses using artificial neural networks for hand gesture recognition. It introduces gesture recognition and ANNs, describing how ANNs can be used for gesture recognition by being adaptive systems that change structure based on information flow. The document outlines training ANNs using feedforward and backpropagation algorithms in MATLAB for gesture recognition. It also provides steps of the recognition process and discusses advantages like learning without reprogramming and disadvantages like needing training.
BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO
This document summarizes a student project titled "Design and Implementation of Patient Monitoring System Using Steady State Visual Evoked Potential Signal(SSVEP) Based on Labview". The project involved:
1. Designing and implementing a brain-computer interface using SSVEP signals to allow communication for disabled individuals. Electrodes were placed on the visual cortex and subjects were shown visual stimuli at different frequencies.
2. Signal processing circuits including amplifiers and filters were designed and tested to extract the SSVEP frequencies from EEG signals.
3. A visual stimulation system was developed using flashing lights at different frequencies.
4. LabVIEW was used to analyze the SSVEP signals and detect the frequency the
Brain-Inspired Computation based on Spiking Neural Networks ...
On this live, prof. Kasabov gives us a gentle overview of Spiking Neural Networks, and their current applications
Full live here, with discussion: https://www.youtube.com/watch?v=niAannUB3pc&t=232s
Have fun 😎😂😁😀
Bci
This document summarizes a project on developing a brain-computer interface system to help people with disabilities control external devices. It lists the supervisors and team members working on the project. It then outlines the agenda which includes defining the problem, objectives, motivations, system architecture, implementation, and future work. It notes disability statistics in Egypt and the objective is to help people overcome disabilities. The motivations include the technology now being more successful and dealing with new techniques. The system architecture involves acquiring brain signals, preprocessing, feature extraction, classification, and decision steps. The implementation uses an EMOTIV headset and explores preprocessing, feature extraction using wavelets, Fourier transforms and PCA, and classification using neural networks. Future work involves adding more
Bci
controlling arm using brain signal
using emotive headseat and worked on 3 classes (open arm ,closed arm and closed hand)
Information processing with artificial spiking neural networks
Brains rely on spiking neural networks for ultra-low-power information processing. Building artificial intelligence with similar efficiency requires learning algorithms to instantiate complex spiking neural networks and brain-inspired neuromorphic hardware to emulate them efficiently. Toward this end, I will briefly introduce surrogate gradients as a general framework for training spiking neural networks and showcase their robustness and self-calibration capabilities on analog neuromorphic hardware. Drawing further inspiration from biology, I will discuss the impact of homeostatic plasticity and network initialization in the excitatory-inhibitory balanced regime on deep spiking neural network training. Finally, I will show how approximations relate surrogate gradients to biologically plausible online learning rules with a minor impact on their effectiveness.
The second seminar
This document discusses developing a generic EEG classification system using brain signals for brain-computer interface applications. The system architecture includes EEG signal acquisition, preprocessing to remove noise and artifacts, feature extraction using independent component analysis and power spectral density, dimensionality reduction, classification using convolutional neural networks, and postprocessing. The goals are to extract spatial and temporal information from EEG signals to classify different brain states and movements like hand movement, tongue movement, walking, eye blinks, and more. This will help build a robust EEG classification system to be used in various BCI applications.
Brain computer interface based
1) The document presents research on using EEG signals to predict reaching targets during two experiments.
2) It describes the components of a BCI system and the challenges of using EEG data, which can be contaminated by artifacts from eye and muscle movements.
3) The researchers used independent component analysis and other techniques to remove artifacts from the EEG data before extracting features and using classification algorithms to decode reaching targets.
Presentation on neural network
Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.
Brain Computer Interface for reconstructing sensory experiences
Brain imaging using electroencephalography
Scientific data management (v2)
Covers basic concepts of data and data management and a few examples of data services provided by universities.
Scaling Unsupervised Ciliary Motion Analysis for Actionable Biomedical Insigh...
This document summarizes research on using unsupervised machine learning techniques with PySpark to analyze high-speed videos of ciliary motion and identify subtypes of ciliary dysfunction. The researcher processes video frames with OpenCV to extract motion features, models the features as autoregressive processes, clusters the processes with eigendecomposition in Spark to identify multiple motion subtypes, and aims to develop this into a clinical tool to assist diagnosis of ciliopathies and other disorders related to ciliary dysfunction. The large datasets required to identify subtypes will need to be collected from collaborators and processed in parallel using Spark.
Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotat...
Scientific Workflows have become the workhorse of BigData analytics for scientists. As well as being repeatable and optimizable pipelines that bring together datasets and analysis tools, workflows make-up an important part of the provenance of data generated from their execution. By faithfully capturing all stages in the analysis, workflows play a critical part in building up the audit-trail (a.k.a. provenance) meta- data for derived datasets and contributes to the veracity of results. Provenance is essential for reporting results, reporting the method followed, and adapting to changes in the datasets or tools. These functions, however, are hampered by the complexity of workflows and consequently the complexity of data-trails generated from their instrumented execution. In this paper we propose the generation of workflow description summaries in order to tackle workflow complexity. We elaborate reduction primitives for summarizing workflows, and show how prim- itives, as building blocks, can be used in conjunction with semantic workflow annotations to encode different summariza- tion strategies. We report on the effectiveness of the method through experimental evaluation using real-world workflows from the Taverna system.
[台灣人工智慧學校] 主題演講 - 張智威總經理 (President of HTC DeepQ)
The document discusses various topics related to artificial intelligence including:
1) It defines AI and machine learning, discusses the recent hype around AI, and some applications and requirements of AI systems.
2) It covers different types of neural networks including deep learning, recurrent neural networks, and how neural networks can be used for tasks like machine translation.
3) It discusses challenges in developing AI systems like acquiring large datasets, interpreting data to select the right machine learning model, building scalable infrastructure, and training skilled personnel.
20141003.journal club
- Researchers used a hierarchical convolutional neural network (CNN) optimized for object categorization performance to predict neural responses in higher visual cortex.
- The top layer of the CNN accurately predicted responses in inferior temporal (IT) cortex, and intermediate layers predicted responses in V4 cortex.
- This suggests that biological performance optimization directly shaped neural mechanisms in visual processing areas, as the CNN was not explicitly trained on neural data but emerged as predictive of responses in IT and V4.
Visual Analytics in Omics: why, what, how?
Visual Analytics in omics can help address several challenges in analyzing complex biological data:
- It allows researchers to explore large datasets in an interactive way to generate hypotheses, as the initial analysis is often exploratory rather than driven by a specific hypothesis.
- It opens the "black box" of automated analysis by making the analysis process transparent and understandable to domain experts.
- Effective visualization techniques leverage human visual perception and cognition to facilitate reasoning about the data.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Nucleic Acid-its structural and functional complexity.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
More Related Content
Similar to DSSV2022_np_eeg_toolbox
ICC2017 UUUI sessions 6205-1
ICC2017 Washington - http://icc2017.org/
6205.1
Exploring the possibilities of eye tracking and EEG integration for cartographic context
Merve Keskin
Istanbul Technical University
Kristien Ooms
Ghent University
A. Ozgur Dogru
Istanbul Technical University
Philippe De Maeyer
Universiteit Gent
FrB18_2_Krishnamoorthy_Venkatasubramanian.pptx
This document summarizes a study that used convolutional neural networks (CNNs) to detect microsleep states from EEG data. The researchers compared using oversampling and cost-based learning techniques to address class imbalance when training the CNN models. They also compared using raw EEG data versus 2D maps of log power spectral features as the CNN input. The CNN models achieved high performance in detecting microsleep states, outperforming other machine learning methods. Using raw EEG data and cost-based learning provided the best combination of high performance and reduced training time. Future work could explore ensemble and parallel CNN architectures.
Visual Analytics in Omics - why, what, how?
This document discusses visual analytics in omics data. It begins by noting the shift from hypothesis-driven to data-driven research due to large datasets. Visual analytics can help explore these data by opening the "black box" of algorithms and enabling researchers to develop hypotheses. Effective visualization leverages human perception through techniques like preattentive vision and Gestalt laws. Challenges to visual analytics include scalability issues for large datasets and identifying interesting patterns for further analysis. Examples demonstrate data exploration, filtering, and user-guided analysis in genomic applications.
AI and neurological disorder- Epilepsy.,work with brain and omputers
This presentation discusses using artificial intelligence techniques to help detect and diagnose epilepsy based on EEG signals. The objectives are to pre-process EEG data using techniques like noise reduction and filtering, extract features using CNN, detect epileptic features in the waves, and detect if a patient is epileptic. The system will collect EEG data from individuals with epilepsy, preprocess the data, develop an AI model using deep learning to recognize seizure patterns, and train the model on the preprocessed EEG data. Potential applications include early seizure detection for prompt medical intervention, personalized treatment plans tailored to individual seizure patterns, and reducing the healthcare burden by ensuring critical cases receive attention.
Artificial Neural Network for hand Gesture recognition
This document discusses using artificial neural networks for hand gesture recognition. It introduces gesture recognition and ANNs, describing how ANNs can be used for gesture recognition by being adaptive systems that change structure based on information flow. The document outlines training ANNs using feedforward and backpropagation algorithms in MATLAB for gesture recognition. It also provides steps of the recognition process and discusses advantages like learning without reprogramming and disadvantages like needing training.
BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO
This document summarizes a student project titled "Design and Implementation of Patient Monitoring System Using Steady State Visual Evoked Potential Signal(SSVEP) Based on Labview". The project involved:
1. Designing and implementing a brain-computer interface using SSVEP signals to allow communication for disabled individuals. Electrodes were placed on the visual cortex and subjects were shown visual stimuli at different frequencies.
2. Signal processing circuits including amplifiers and filters were designed and tested to extract the SSVEP frequencies from EEG signals.
3. A visual stimulation system was developed using flashing lights at different frequencies.
4. LabVIEW was used to analyze the SSVEP signals and detect the frequency the
Brain-Inspired Computation based on Spiking Neural Networks ...
On this live, prof. Kasabov gives us a gentle overview of Spiking Neural Networks, and their current applications
Full live here, with discussion: https://www.youtube.com/watch?v=niAannUB3pc&t=232s
Have fun 😎😂😁😀
Bci
This document summarizes a project on developing a brain-computer interface system to help people with disabilities control external devices. It lists the supervisors and team members working on the project. It then outlines the agenda which includes defining the problem, objectives, motivations, system architecture, implementation, and future work. It notes disability statistics in Egypt and the objective is to help people overcome disabilities. The motivations include the technology now being more successful and dealing with new techniques. The system architecture involves acquiring brain signals, preprocessing, feature extraction, classification, and decision steps. The implementation uses an EMOTIV headset and explores preprocessing, feature extraction using wavelets, Fourier transforms and PCA, and classification using neural networks. Future work involves adding more
Bci
controlling arm using brain signal
using emotive headseat and worked on 3 classes (open arm ,closed arm and closed hand)
Information processing with artificial spiking neural networks
Brains rely on spiking neural networks for ultra-low-power information processing. Building artificial intelligence with similar efficiency requires learning algorithms to instantiate complex spiking neural networks and brain-inspired neuromorphic hardware to emulate them efficiently. Toward this end, I will briefly introduce surrogate gradients as a general framework for training spiking neural networks and showcase their robustness and self-calibration capabilities on analog neuromorphic hardware. Drawing further inspiration from biology, I will discuss the impact of homeostatic plasticity and network initialization in the excitatory-inhibitory balanced regime on deep spiking neural network training. Finally, I will show how approximations relate surrogate gradients to biologically plausible online learning rules with a minor impact on their effectiveness.
The second seminar
This document discusses developing a generic EEG classification system using brain signals for brain-computer interface applications. The system architecture includes EEG signal acquisition, preprocessing to remove noise and artifacts, feature extraction using independent component analysis and power spectral density, dimensionality reduction, classification using convolutional neural networks, and postprocessing. The goals are to extract spatial and temporal information from EEG signals to classify different brain states and movements like hand movement, tongue movement, walking, eye blinks, and more. This will help build a robust EEG classification system to be used in various BCI applications.
Brain computer interface based
1) The document presents research on using EEG signals to predict reaching targets during two experiments.
2) It describes the components of a BCI system and the challenges of using EEG data, which can be contaminated by artifacts from eye and muscle movements.
3) The researchers used independent component analysis and other techniques to remove artifacts from the EEG data before extracting features and using classification algorithms to decode reaching targets.
Presentation on neural network
Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.
Brain Computer Interface for reconstructing sensory experiences
Brain imaging using electroencephalography
Scientific data management (v2)
Covers basic concepts of data and data management and a few examples of data services provided by universities.
Scaling Unsupervised Ciliary Motion Analysis for Actionable Biomedical Insigh...
This document summarizes research on using unsupervised machine learning techniques with PySpark to analyze high-speed videos of ciliary motion and identify subtypes of ciliary dysfunction. The researcher processes video frames with OpenCV to extract motion features, models the features as autoregressive processes, clusters the processes with eigendecomposition in Spark to identify multiple motion subtypes, and aims to develop this into a clinical tool to assist diagnosis of ciliopathies and other disorders related to ciliary dysfunction. The large datasets required to identify subtypes will need to be collected from collaborators and processed in parallel using Spark.
Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotat...
Scientific Workflows have become the workhorse of BigData analytics for scientists. As well as being repeatable and optimizable pipelines that bring together datasets and analysis tools, workflows make-up an important part of the provenance of data generated from their execution. By faithfully capturing all stages in the analysis, workflows play a critical part in building up the audit-trail (a.k.a. provenance) meta- data for derived datasets and contributes to the veracity of results. Provenance is essential for reporting results, reporting the method followed, and adapting to changes in the datasets or tools. These functions, however, are hampered by the complexity of workflows and consequently the complexity of data-trails generated from their instrumented execution. In this paper we propose the generation of workflow description summaries in order to tackle workflow complexity. We elaborate reduction primitives for summarizing workflows, and show how prim- itives, as building blocks, can be used in conjunction with semantic workflow annotations to encode different summariza- tion strategies. We report on the effectiveness of the method through experimental evaluation using real-world workflows from the Taverna system.
[台灣人工智慧學校] 主題演講 - 張智威總經理 (President of HTC DeepQ)
The document discusses various topics related to artificial intelligence including:
1) It defines AI and machine learning, discusses the recent hype around AI, and some applications and requirements of AI systems.
2) It covers different types of neural networks including deep learning, recurrent neural networks, and how neural networks can be used for tasks like machine translation.
3) It discusses challenges in developing AI systems like acquiring large datasets, interpreting data to select the right machine learning model, building scalable infrastructure, and training skilled personnel.
20141003.journal club
- Researchers used a hierarchical convolutional neural network (CNN) optimized for object categorization performance to predict neural responses in higher visual cortex.
- The top layer of the CNN accurately predicted responses in inferior temporal (IT) cortex, and intermediate layers predicted responses in V4 cortex.
- This suggests that biological performance optimization directly shaped neural mechanisms in visual processing areas, as the CNN was not explicitly trained on neural data but emerged as predictive of responses in IT and V4.
Visual Analytics in Omics: why, what, how?
Visual Analytics in omics can help address several challenges in analyzing complex biological data:
- It allows researchers to explore large datasets in an interactive way to generate hypotheses, as the initial analysis is often exploratory rather than driven by a specific hypothesis.
- It opens the "black box" of automated analysis by making the analysis process transparent and understandable to domain experts.
- Effective visualization techniques leverage human visual perception and cognition to facilitate reasoning about the data.
Similar to DSSV2022_np_eeg_toolbox (20)
AI and neurological disorder- Epilepsy.,work with brain and omputers
AI and neurological disorder- Epilepsy.,work with brain and omputers
Artificial Neural Network for hand Gesture recognition
Artificial Neural Network for hand Gesture recognition
BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO
BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO
Brain-Inspired Computation based on Spiking Neural Networks ...
Brain-Inspired Computation based on Spiking Neural Networks ...
Information processing with artificial spiking neural networks
Information processing with artificial spiking neural networks
Brain Computer Interface for reconstructing sensory experiences
Brain Computer Interface for reconstructing sensory experiences
Scaling Unsupervised Ciliary Motion Analysis for Actionable Biomedical Insigh...
Scaling Unsupervised Ciliary Motion Analysis for Actionable Biomedical Insigh...
Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotat...
Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotat...
Recently uploaded
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Nucleic Acid-its structural and functional complexity.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
原版纸张【微信：741003700 】【(uvic毕业证书)维多利亚大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样
原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
bordetella pertussis.................................ppt
Bordettela is a gram negative cocobacilli spread by air born drop let
8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
20240520 Planning a Circuit Simulator in JavaScript.pptx
Evaporation step counter work. I have done a physical experiment.
(Work in progress.)
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptx
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Nucleophilic Addition of carbonyl compounds.pptx
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
ESR spectroscopy in liquid food and beverages.pptx
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
SAR of Medicinal Chemistry 1st by dk.pdf
In this presentation include the prototype drug SAR on thus or with their examples .
Syllabus of Second Year B. Pharmacy
2019 PATTERN.
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...Abdul Wali Khan University Mardan,kP,Pakistan
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsUnveiling the Energy Potential of Marshmallow Deposits.pdf
Unveiling the Energy Potential of Marshmallow Deposits: A Revolutionary
Breakthrough in Sustainable Energy Science
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Recently uploaded (20)
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Nucleic Acid-its structural and functional complexity.
Nucleic Acid-its structural and functional complexity.
bordetella pertussis.................................ppt
bordetella pertussis.................................ppt
8.Isolation of pure cultures and preservation of cultures.pdf
8.Isolation of pure cultures and preservation of cultures.pdf
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
20240520 Planning a Circuit Simulator in JavaScript.pptx
20240520 Planning a Circuit Simulator in JavaScript.pptx
ESR spectroscopy in liquid food and beverages.pptx
ESR spectroscopy in liquid food and beverages.pptx
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
Unveiling the Energy Potential of Marshmallow Deposits.pdf
Unveiling the Energy Potential of Marshmallow Deposits.pdf
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
The debris of the ‘last major merger’ is dynamically young
The debris of the ‘last major merger’ is dynamically young
DSSV2022_np_eeg_toolbox
- 1. NP-EEG Toolbox: An Open-Source Toolbox for Naturalistic Paradigm EEG Analysis Juin-Der Lee, Michelle Liou, Vincent S. C. Chien, Jiun-Wei Liou Institute of Statistical Science, Academia Sinica, Taipei, Taiwan Data Science, Statistics & Visualisation 2022 This Photo by Unknown author is licensed under CC BY-SA-NC.
- 2. Agenda • EEG • Naturalistic paradigm • Why release NP-EEG Toolbox • Architecture • Flowchart • Future work
- 3. Spatio-temporal EEG signal for studying human brain functions https://i1.wp.com/pic4.zhimg.com/v2-d69f44ef5f25419ed2342d6a15d2f1e5_r.jpg
- 4. Traditional EEG experiment design Mohsenzadeh, Yalda & Qin, Sheng & Cichy, Radoslaw & Pantazis, Dimitrios. (2018). Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway. eLife. 7. 10.7554/eLife.36329. • Repeat simple tasks a lot of times • It is “Unnatural”: humans seldom behaves this way in real life.
- 6. Record EEG signal during shopping Banerjee, Tannista. (2020). A neurobehavioral study on the efficacy of price interventions in promoting healthy food choices among low socioeconomic families. Nature. 10. 10.1038/s41598-020-71082-y.
- 7. • Differences in Physiological Signals Due to Age and Exercise Habits of Subjects during Cycling Exercise, SY Lin, et al., Sensors 21 (21), 7220, 2021 • Brain dynamics and connectivity networks under natural auditory stimulation, PC Kuo, et al., Neuroimage 202, 116042, 2019 • Lingering sound: event-related phase-amplitude coupling and phase-locking in fronto-temporo-parietal functional networks during memory retrieval of music melodies, YL Tseng, et al., Frontiers in Human Neuroscience 13, 150, 2019 Brain Dynamics & Information Synthesis Lab at Institute of Statistical Science, Academia Sinica • Have being working on real-life EEG signal processing for the past few years. • Have developed an SOP of analyzing data. • Have accumulated lots of in-house codes.
- 8. Motivations for releasing NP-EEG Toolbox Save researcher’s time and effort As a reference design Foster collaborations
- 9. Architecture EEG Lab Preprocessing Connectivity Statistics Visualization NP-EEG Toolbox MATLAB Image processing toolbox Signal processing toolbox Statistics toolbox Optimization toolbox
- 10. Preprocessing Brain Connectivity Analysis Statistics Visualization channels_() • add_dummy_events • re-reference • bandpass filtering • extract epochs • reject bad channels with too many bad trials epochs_() • reject bad epochs ica_() • Mark ICA components having large R2 with regression channels clean_() • Clean ICA components whose R2 is too large by regression out artifacts. computePAC() • Compute phase amplitude coupling value computePLV() • Compute phase locking value plv_ttest() • Statistical t-test for PLV pac_ttest() • Statistical t-test for PAC plotPLV(), plotPAC() • Visualize PLV and PAC results
- 11. Future work Current • Alpha version • Command line only • Some messy codes • Not well documented Next • Documentation • Sample dataset • Graphical User Interface • Refactoring Will be available soon on GitHub.
- 12. Thank you for your time!