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This document summarizes research on developing a unified framework for learning representations from EEG data during working memory tasks. Fifteen participants completed a Sternberg working memory task while EEG was recorded. The EEG data was preprocessed, transformed into images representing spectral properties over time, and analyzed using convolutional neural networks and recurrent neural networks. Classification results showed that incorporating temporal information, such as with LSTMs or 1D convolutions, improved accuracy over max pooling across time. Learned representations revealed frequency selectivity and links to known electrophysiological markers of cognitive load. Future work will explore applications to brain-computer interfaces.
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A neural network is composed of a number of nodes, or units[1], connected by links. Each link has a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
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It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
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Four parts of a typical nerve cell : • DENDRITES: Accepts the inputs • SOMA : Process the inputs • AXON : Turns the processed inputs into outputs. • SYNAPSES : The electrochemical contact between the neurons.
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Inputs to the network are represented by the x1 mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
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performance and the learning environment. The learning
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components. The memory is playing vital role in the
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instant, responsive, process, recollect, reference,
instruction and action memory are involved in the
process of learning. The influencing factors on these
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observed set of data represented using cubical dataset
format for the mining approach. The mining process is
carried out using neural network based back propagation
network model to decide the influencing cognitive load
for the different learning challenges. The learners’
difficulties are identified through the experimental
results.
ANN Based POS Tagging For Nepali Text
The article presents Part of Speech Tagging for Nepali Text using three techniques of Artificial Neural networks. The novel algorithm for POS tagging is introduced .Features are extracted from the marginal probability of Hidden Markov Model. The extracted features are supplied to 3 different ANN architectures viz. Radial Basis Function (RBF) network, General Regression Neural Networks (GRNN) and Feed forward Neural network as an input vector for each word. Two different Annotated Tagged sets are constructed for training and testing purpose. Results are compared using all the 3 techniques and applied on both the sets. GRNN based POS tagging technique is found better as it produces 100% and 98.32% accuracies for both training and testing sets respectively.
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This document summarizes key aspects of neural networks and training algorithms. It introduces threshold logic units (TLUs) as basic units for neural networks and different methods for training single TLUs, including gradient descent, the Widrow-Hoff rule, and the generalized delta procedure. It then discusses feedforward neural networks and the backpropagation method for training multiple layers of neurons. Finally, it covers topics like generalization, accuracy, overfitting, and additional readings in the field of neural networks.
[Research] Detection of MCI using EEG Relative Power + DNN
* This is a summarized presentation material for the conference paper:
Donghyeon Kim, and Kiseon Kim "Detection of Early Stage Alzheimer's Disease using EEG Relative Power with Deep Neural Network," IEEE EMBC 2018
* It was addressed in A-GIST group, study team for Artificial Intelligence in Gwangju Institute of Science and Technology (GIST)
* Youtube (in Korean): https://youtu.be/2maphOXkB6k
Artificial neural networks and its application
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
Presentation, navid khoob
This paper presents a novel approach called LOcal Rule Extraction (LORE) to extract rules from neural networks. LORE transforms a trained multilayer perceptron network into an equivalent decision diagram form to extract logic rules that generalize the network's output for inputs similar to the training set, while relaxing this condition for other inputs. It works by deriving a partial rule for each training sample, merging these rules, and then generalizing the merged rule set over the entire input space. The extracted rules are assessed based on their accuracy, fidelity to the original network, consistency, comprehensibility, and the computational complexity of the extraction process.
Online Coreset Selection for Rehearsal-based Continual Learning
A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual learning, where we store a subset of the training examples (coreset) to be replayed later to alleviate catastrophic forgetting. In continual learning, the quality of the samples stored in the coreset directly affects the model's effectiveness and efficiency. The coreset selection problem becomes even more important under realistic settings, such as imbalanced continual learning or noisy data scenarios. To tackle this problem, we propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration and trains them in an online manner. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting. We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.
Artificial neural networks in hydrology
1) Artificial neural networks are made up of nodes that pass signals through connection links. Each node applies an activation function to determine its output signal.
2) Neural networks can be classified based on number of layers (single, bi-layer, multi-layer) or direction of information flow (feed forward, recurrent).
3) Backpropagation is commonly used for training, which involves passing inputs forward and propagating errors backward to adjust weights. Other algorithms like conjugate gradient and radial basis function training also exist.
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Efficiency of Neural Networks Study in the Design of Trusses
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On the cross domain reusability of neural modules for general video game playing
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Analysis of Influences of memory on Cognitive load Using Neural Network Back ...
[Research] Detection of MCI using EEG Relative Power + DNN
[Research] Detection of MCI using EEG Relative Power + DNN
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Online Coreset Selection for Rehearsal-based Continual Learning
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Unified Framework for Learning Representation from EEG Data
This document summarizes research on developing a unified framework for learning representations from EEG data. Researchers recorded EEG data from 15 participants performing a Sternberg working memory task. The EEG data was preprocessed, transformed into images representing spectral properties, and fed into a convolutional neural network. This innovative approach preserved spatial and temporal relationships in the data. Classification results showed that incorporating temporal information, especially with LSTMs, improved accuracy over traditional methods. Learned representations revealed frequency selectivity and links to cognitive load markers. Future work involves using these techniques for brain-computer interfaces.
A Marketing Research Tool for Destination Management Organisations’ Logo Design,
A Marketing Research Tool for Destination Management Organisations’ Logo Design,University of Winchester (UK)
This document discusses the development of a marketing research tool to help Destination Marketing Organizations (DMOs) design effective logos. It introduces the challenges facing Haiti's tourism industry and its DMO's rebranding efforts through a new logo. The conceptual framework examines how logos can portray a destination's image and essence through shape, color, and representing its context, identity and history. While Haiti's new logo modernized its image, the original colors more effectively conveyed the destination's meaning through their representation of its history, traditions and identity. The tool being developed will help DMOs select appropriate colors and visuals that best market their destination while maintaining its core characteristics.Viewers also liked (7)
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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.
⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
✅ Recent research shows the possibility of using electromyography #EMG electrical signals to control devices or prosthesis.
✅ The EMG signals are measured in muscles, such as the forearm. These signals can lead to determine the intentionality of the patient when performing some motor tasks, however the signals are susceptible to noise due to the voltage levels in microvolts that are acquired.
✅ In this work, the preprocessing of the EMG signals includes the design and test of a filter.
✅ Our designed filter allows eliminating any signal components from the electrical network or any other sources that are not EMG signals. Then, analyzing the frequency components and the distribution of the filtered EMG signals validated the preprocessing efficiency.
✅ Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms.
✅ The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, while the DBSCAN and Hierarchical algorithms were not able to perform the nine clusters, otherwise they match the performance of clustering two groups combining gestures.
Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017
Irina Rish is a researcher at the AI Foundations department of the IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems (“autonomic computing”) to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 60 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds 24 patents and several IBM awards. Irina currently serves on the editorial board of the Artificial Intelligence Journal (AIJ). As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling.
Abstract Summary:
Learning About the Brain and Brain-Inspired Learning:
Quantifying mental states and identifying statistical biomarkers of mental disorders from neuroimaging data is an exciting and rapidly growing research area at the intersection of neuroscience and machine learning, with the particular focus on interpretability and reproducibility of learned models. We will discuss promises and limitations of machine-learning methods in such applications, focusing on recent applications of deep learning methods such as recurrent convnets to the analysis of “brain movies” (EEG) data. On the other hand, besides the above “AI to Brain” direction, we will also discuss the “Brain to AI”, namely, borrowing ideas from neuroscience to improve machine learning, with specific focus on adult neurogenesis and online model adaptation in representation learning.
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The document summarizes research using a transformer model to classify biomedical signals, specifically EEG signals. Key points:
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- The model was tested on the Bonn and CHB-MIT EEG datasets, achieving accuracy above 97% on binary, tertiary and 5-class problems from the Bonn data and above 95% accuracy for individual patients in the CHB-MIT data.
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This document discusses spatial filtering in EEG signal processing. It begins with an introduction that describes EEG and spatial filtering algorithms that combine multiple channels into a single channel using weighted linear combinations. The document then covers the history of EEG and spatial filtering, including its use in detecting epileptiform discharges and brain-computer interfaces. It presents the mathematical model of spatial filtering and different categories of spatial filters. The document also discusses results from different spatial filters and classifiers. It concludes that spatial filters use weighted combinations of electrode activity to identify patterns and isolate sources of variance in multichannel EEG data.
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This document summarizes an improved algorithm for brain signal analysis using blind source separation techniques. It compares the proposed algorithm to other existing BSS methods like STFT-ICA, wavelet ICA, EEMD, and IVA. The proposed algorithm uses an ensemble of EEMD and IVA (EEMD-IVA) to separate both uni-dimensional and multi-dimensional EEG data. Experimental results on synthetic and real EEG data show that the proposed EEMD-IVA algorithm has a faster convergence rate and higher signal-to-noise ratio and average correlation coefficient values compared to other BSS techniques.
Paper memo: persistent homology on biological problems
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This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
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- 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.
Developmental Mega Sample: Exploring Inter-Individual Variation
Slides from my talk at the 4th Whistler Scientific Workshop on Brain Functional Organization, Connectivity, and Behavior.
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This document presents an improved algorithm for analyzing brain signals (EEGs) using blind source separation techniques. It compares the proposed algorithm to other existing BSS methods like STFT-ICA, wavelet ICA, EEMD, and IVA. The proposed algorithm uses an ensemble EEMD-IVA approach to separate both univariate and multivariate data sources from EEG recordings. Experimental results showed the proposed algorithm had higher SNR and ACC values than other techniques, indicating better separation performance. It also had a faster convergence rate than the normal EEMD-IVA method.
chapter10
This document proposes an emotion recognition system using EEG signals and time domain analysis. Five different machine learning algorithms (RVM, MLP, DT, SVM, Bayesian) are used to classify three emotions (happy, relaxed, sad) felt by subjects viewing different videos. EEG data is collected from subjects using a headset and amplifier. Six features are extracted from the time domain signals of each EEG channel. The document describes the data collection process, feature extraction methodology, and compares the performance of different algorithms at classifying emotions based on the EEG data.
Jennie Si: "Computing with Neural Spikes"
This document discusses neural coding and how neurons encode information during cognitive tasks. It summarizes research using single-unit neural recordings in rats performing a directional control task. Three key findings are presented:
1) Neural firing rates in areas like PM and M1 encoded information about trial outcomes and the rat's learning progress over multiple sessions.
2) Precise spike timing provided information about the rat's cognitive state, with more synchronized activity observed early in learning.
3) Network modeling revealed changes in functional connectivity between neurons associated with synaptic plasticity, with more optimized patterns emerging as the rat became proficient at the task.
⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BC...
Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
A Novel Single-Trial Analysis Scheme for Characterizing the Presaccadic Brain...
The document presents a novel single-trial analysis scheme to characterize presaccadic brain activity based on self-organizing neural networks. EEG data from eye movement experiments was used to group saccades by velocity. Functional connectivity graphs identified variations in information exchange between brain regions for different velocity groups. Fast saccades showed earlier peaks and higher efficiency in specific brain regions compared to slower saccades. The approach provides a way to model brain self-organization during cognitive tasks using single-trial variability analyzed with network methods.
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This document provides an overview of recurrent neural networks for sequence learning. It discusses different types of sequence labeling tasks and architectures of neural networks commonly used for sequence learning, including recurrent neural networks, long short-term memory networks, and bidirectional recurrent neural networks. It also summarizes a research paper on using temporal deep neural networks for mobile biometric authentication using inertial sensor data.
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This document describes a method for automatically detecting dopaminergic neurons in light microscopy images of zebrafish embryos using deep learning. Zebrafish are useful for modeling Parkinson's disease. The method first normalizes color across image stacks, then uses a support vector machine to detect rough cell regions. A convolutional neural network is trained on patches to precisely locate cell pixels. It achieves this with over 0.5 million training patches. The method detects cells in test stacks and provides performance metrics like recall and precision compared to manual labels. Overall it presents a high-throughput solution to automatically count neurons in large quantities of zebrafish images.
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A brief presentation about concepts and research in reservoir computing, presented in COSC594 Fall 2016 at the University of Tennessee
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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
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Embry-Riddle Aeronautical University (ERAU) is the world's largest aviation and aerospace university with over 32,000 students. The ERAU-Worldwide Campus offers over 70 degrees through online and face-to-face instruction at over 125 campuses globally. It has offered distance learning since 1971 and has 12 enrollment periods per year for flexibility. ERAU-Worldwide focuses on unmanned systems through academic programs like a UAS minor and masters specialization, as well as a masters in unmanned systems and bachelors in unmanned systems applications. It also conducts UAS research and outreach activities.
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Cody Behles from the University of Memphis introduced altmetrics and the focus of the Altmetrics and Digital Analytics Interest Group. Altmetrics are non-traditional impact measures based on online activity that provide a broader view of impact beyond citations. Examples include social media mentions, blogs, mainstream media, and readership metrics. Potential issues with altmetrics include manipulation of data, difficulty standardizing across sources, and weak correlations with citations. The interest group aims to discuss successful integration of altmetrics into librarian tools and address concerns about their role in scholarly communication.
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The document discusses the emergence and purpose of makerspaces in academic libraries. It defines makerspaces as spaces for creating, learning, and experimenting using tools and technologies like 3D printers. The document argues that makerspaces are well-suited for academic libraries because libraries are repositories of knowledge, community centers, and places that promote information literacy and interdisciplinary collaboration. It provides statistics on the success of the University of Memphis library's makerspace and discusses benefits of makerspace partnerships between libraries. The conclusion is that makerspaces can redefine libraries but require proper planning, support, talented staff, and institutional buy-in to be successful.
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Yeasin
- 1. Unified Framework for Learning Representation from EEG Data Pouya Bashivan, PhD candidate Dr. Mohammed Yeasin The University of Memphis April 9, 2016 1
- 2. Working Memory Significance • What: Shared resource for storage and manipulation of information (e.g. reading comprehension, arithmetic) • Why: Plays a key role in determining individual mental capacity • How: Information is stored and maintained through a network of cortical and sub-cortical areas distributed widely across the brain Meta-analysis result of 189 WM experiments Rottschy et al. Neuroimage (2012) 2 Intro Results FutureMethods
- 3. Participants • 15 participants (8 female) • Graduate level education • 24-33 years (28±3) • Strongly right-handed (Edinburgh handedness inventory) 3 Intro Results FutureMethods
- 4. EEG Recording • Inside an electro-acoustically shielded booth • EEG cap with 64 scalp electrodes, standard 10-10 locations • Electrode impedance < 5kΩ • Sampling freq. 500 Hz • 4 electrodes placed around the eyes for detection of ocular activity 4 Intro Results FutureMethods
- 5. Sternberg WM Task • Two variants of Sternberg WM task, differed by modality • English characters • Number of items = {2, 4, 6, 8} • 60 trials per set size • Visual SET presented concurrently, Auditory SET presented sequentially. 5 Visual Auditory Intro Results FutureMethods
- 6. Preprocessing EEG • Down-sampling: Data down-sampled to 250 Hz • Filtering: Band-Pass zero-phase FIR filter, N=500, [1-45] Hz • Epoching: Data was segmented from 2000ms pre-SET till TEST • Artifact Correction: – Threshold on EOG channels – PCA on aggregated artifactual time-series – Discard first 3-components 6 Intro Results FutureMethods
- 7. Spectral properties of EEG • Rhythmic activities of EEG = informative neuro-markers. – Frequency-specific Power – Frequency-specific Phase • Conventional frequency bands: – Delta (δ) [1-4Hz] – Theta (θ) [4-7Hz]* – Alpha (α) [8-13Hz]* – Beta (β) [13-30Hz]* – Gamma (γ) [30+Hz] 7 Intro Results FutureMethods
- 8. Representation Learning • Goal: Find signal representations that are robust to variations. • Sources of variations: 1. Cortical maps 2. Head shapes and caps 8 Intro Results FutureMethods
- 9. How to Learn? • Conventional approach: Aggregate features into a vector – Relationship between features is lost. • Alternative approach: Structure features into its natural form, preserving relationships. – Neighborhood notion is preserved throughout classification. 9 Intro Results FutureMethods
- 10. Transforming Time-series to Images 1. Project electrode locations into 2D surface 2. Compute PSD for each channel times-series 3. Compute power estimates within a specific band 4. Map power values onto the projected electrode locations (polar projection). 5. Interpolate values to construct an image. 10 1 4 5 2, 3 Intro Results FutureMethods
- 11. Innovative Approach 11 Bashivan et al. ICLR (2016) VGG ConvNet with receptive field (3x3) Zisserman et al. ICLR (2015) Intro Results FutureMethods
- 12. ConvNet • Advantage: invariant to partial translation and deformation of input patterns. • Layer types: 1. Conv. Layers (filters or kernels) 2. Pooling Layers (e.g. Max(0, x)) • All parameters learned through back-propagation 12 Conv. Layer Max Pool Intro Results FutureMethods
- 13. Handling Time - RNNs 1. Max-pooling across time 2. Temporal convolution 3. Long Short-Term Memory (LSTM) 13 LSTM Cell Conveyor belt Forget gate Input gate Output gate Ng. et al. CVPR (2015) Bashivan et al. ICLR (2016) Hochreiter et al. Neural Computation (1997) Intro Results FutureMethods
- 14. Behavioral Responses • Tracking behavioral markers: 1. Response times 2. Response Correctness 14 Intro Results FutureMethods
- 15. Classification Results • Adding temporal information considerably decreased average error rates. • Maxpooling over time frames did NOT help with classification. • Mixture of LSTM and 1D-conv worked best. 15 Intro Results FutureMethods
- 16. Learned Representations We used Deconvnet1 approach for back projecting feature maps. • Increasingly sparser feature activation maps for deeper layers. • Frequency selectivity in learned filters. • Noticeable links to electrophysiological markers of cognitive load (e.g. Frontal β, θ, Parietal α)2,3 16 1. Zeiler et al. ECCV (2014) 2. Tallon-Baudry et al., Vis Neuroscience (1999) 3. Jensen et al., Eur J Neuroscience, (2002) Intro Results FutureMethods
- 17. Future Work • Brain-Computer Interface – Real-time recognition of user intention – Network Description Brain Activities 17 Intro Results FutureMethods
- 18. Questions? 18 “The only stupid question is the question that is never asked.” Ramon Bautista
- 19. Acknowledgement • My advisers • My friends at CVPIA lab • 15 anonymous participants in our experiment Bahareh Elahian Iftekhar Anam Shahinour Alam Dr. Mohammed Yeasin Dr. Gavin Bidelman Faruk Ahmed Rakib Al-fahad 19
Editor's Notes
- The way it is planned out
- In order to study WM, we have to give a WM task to our participants.
- Imperfect fitting of the cap on different head shapes.
- First we project the electrode locations onto the 2D surface. Then we compute power (or any other feature you would like) for each electrode and map in onto the electrode location. Interpolate values to construct an image.