EEG Imaginary Body Kinematics Regression. Justin Kilmarx, David Saffo, and Lucien Ng
|
|
|
- Betty Ellis
- 5 years ago
- Views:
Transcription
1 EEG Imaginary Body Kinematics Regression Justin Kilmarx, David Saffo, and Lucien Ng
2 Introduction Brain-Computer Interface (BCI) Applications: Manipulation of external devices (e.g. wheelchairs) For communication in disabled people Rehabilitation robotics Diagnosis and prediction of diseases (e.g. Parkinson s disease, Seizure, Epilepsy) Games Invasive vs Noninvasive Electrocorticography Fifer et al. (2012) Electroencephalography Mcfarland & Wolpaw (2011)
3 Background Invasive Noninvasive Sensorimotor Rhythms (SMR) Steady-State Visual Evoked Potential (SSVEP) Imagined Body Kinematics Continuous decoding the kinematic parameters during imaginary movements of one body part Short time of training Natural imaginary movement Smoother controller system Possibility of developing a generalized decoder Eliminating Subject dependency
4 Research Objective and Setup Objective: Improve the training model accuracy of a noninvasive BCI system based on extracted information from EEG signals and through imagined body kinematics Setup Emotiv EPOC for recording EEG signals BCI2000 for cursor visualization and data collectection Matlab/Python for processing
5 Training Automated cursor movement on computer monitor in 1D Subject imagines following movement with dominant hand 10 trials 5 horizontal 5 vertical 1 minute each Cross validation between trials
6 Data 35 Subjects 10 trials each (5 vertical/5 horizontal) 14 channels each 12 million rows total
7 Clean Up Raw signal contains a lot of noise Low pass filter + ICA to filter signal Band pass filter to isolate frequency ranges of interest
8 Features 13 points in memory Power spectral density in 3 frequency ranges Alpha Beta Mu Coefficients Generated by a Classification Model Predicts if velocity will be positive or negative
9 Training, Testing, and Results Models are trained on linear and nonlinear algorithms Linear Regression, Kernel Ridge, Adaboost Test with trial wise cross-validation 1 trial left out as test 4 used for training, rotate Scored using the average correlation of the two curves over five windows
10 1-D Movement Classification
11 Formalized Problem Input EEG data (time series) with 128 Hz and 14 channels Predict The cursor movement direction at any given time point Vertical: Left / Right / No Horizontal: Up / Down / No
12 Overview of Model Raw Data: Current and Past Sample Preprocessing Filter Bank Basic Model Logistic Regression Refinement Gradient Boosting Prediction Up/Right Event-related Potential Logistic Regression Neural Network Down/Left Neural Oscillation Neural Network No movement
13 Overview: Results AUC of Horizontal Movement prediction: 92% AUC of Vertical Movement prediction: 74%
14 Workflow Generate massive models setting Train and test each models on Bridges (16 Cores + GPU) Observe & Refine cross-validation Pick the best combination of models to do run-time prediction
15 Preprocessing: Event-related potential ERP = The brain response correspond to the event The EEG reflects tons of ongoing brain processes Any processes other than we want are noise To maximize the signal-to-noise ratio (SNR) Assume Find the where X = recorded EEG, A = ERP, D is related to event and N = Noise
16 Preprocessing: Filter Bank
17 Classifier: Logistic Regression Reference:
18 Classifier: Neural Network Represented by matrices (multiplication) GPU! Reference: CUHK IERG4160 (2017 Spring)
19 Another NN: Recurrent Neural Network Refrence:
20 Gradient Boosting
21 Experimental Setup 12 Subjects data were used, each of them has 5 trials about horizontal / vertical movements 1st, 2nd, 3rd trials 4th trial 5th trials Basic Models Train Data Validation Validation Refinement - 2-fold validation 2-fold validation The computation were run on XSEDE-Bridges 16 Cores + GPU (P100)
22 Results: Horizontal (Basic Models)
23 Results: Horizontal (Refine)
24 Result: Vertical
25 Next Step: Next Step Convert everything to C to accelerate computation Goal Predict the cursor direction in real-time
26 Reference Github: alexandrebarachant/grasp-and-lift-eeg-challenge
Task Level Hierarchical System for BCI-enabled Shared Autonomy Iretiayo Akinola, Boyuan Chen, Jonathan Koss, Aalhad Patankar, Jake Varley & Peter
Task Level Hierarchical System for BCI-enabled Shared Autonomy Iretiayo Akinola, Boyuan Chen, Jonathan Koss, Aalhad Patankar, Jake Varley & Peter Allen Columbia University Shared Autonomy Agent 1 Agent
Performance Analysis of SVM, k-nn and BPNN Classifiers for Motor Imagery
Performance Analysis of SVM, k-nn and BPNN Classifiers for Motor Imagery Indu Dokare #1, Naveeta Kant *2 #1 Assistant Professor, *2 Associate Professor #1, *2 Department of Electronics & Telecommunication
Estimating Noise and Dimensionality in BCI Data Sets: Towards Illiteracy Comprehension
Estimating Noise and Dimensionality in BCI Data Sets: Towards Illiteracy Comprehension Claudia Sannelli, Mikio Braun, Michael Tangermann, Klaus-Robert Müller, Machine Learning Laboratory, Dept. Computer
Brain-Computer Interface for Virtual Reality Control
Brain-Computer Interface for Virtual Reality Control C. Guger 1, C. Holzner 1, C. Grönegress 2, G. Edlinger 1, M. Slater 2 1 g.tec medical engineering GmbH, Guger Technologies OEG, Herbersteinstrasse 60,
Recognition of finger movements using EEG signals for control of upper limb prosthesis using logistic regression.
Biomedical Research 2017; 28 (17): 7361-7369 ISSN 0970-938X www.biomedres.info Recognition of finger movements using EEG signals for control of upper limb prosthesis using logistic regression. Amna Javed,
i-eeg: A Software Tool for EEG Feature Extraction, Feature Selection and Classification
i-eeg: A Software Tool for EEG Feature Extraction, Feature Selection and Classification Baha ŞEN Computer Engineering Department, Yıldırım Beyazıt University, Ulus, Ankara, TURKEY Musa PEKER Computer Engineering
Weka ( )
Weka ( http://www.cs.waikato.ac.nz/ml/weka/ ) The phases in which classifier s design can be divided are reflected in WEKA s Explorer structure: Data pre-processing (filtering) and representation Supervised
ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto Canada Paper with same name to appear in NIPS 2012 Main idea Architecture
CS 179 Lecture 16. Logistic Regression & Parallel SGD
CS 179 Lecture 16 Logistic Regression & Parallel SGD 1 Outline logistic regression (stochastic) gradient descent parallelizing SGD for neural nets (with emphasis on Google s distributed neural net implementation)
Multi-voxel pattern analysis: Decoding Mental States from fmri Activity Patterns
Multi-voxel pattern analysis: Decoding Mental States from fmri Activity Patterns Artwork by Leon Zernitsky Jesse Rissman NITP Summer Program 2012 Part 1 of 2 Goals of Multi-voxel Pattern Analysis Decoding
CME 213 SPRING Eric Darve
CME 213 SPRING 2017 Eric Darve Final project Final project is about implementing a neural network in order to recognize hand-written digits. Logistics: Preliminary report: Friday June 2 nd Final report
Model Assessment and Selection. Reference: The Elements of Statistical Learning, by T. Hastie, R. Tibshirani, J. Friedman, Springer
Model Assessment and Selection Reference: The Elements of Statistical Learning, by T. Hastie, R. Tibshirani, J. Friedman, Springer 1 Model Training data Testing data Model Testing error rate Training error
CS6375: Machine Learning Gautam Kunapuli. Mid-Term Review
Gautam Kunapuli Machine Learning Data is identically and independently distributed Goal is to learn a function that maps to Data is generated using an unknown function Learn a hypothesis that minimizes
Preface to the Second Edition. Preface to the First Edition. 1 Introduction 1
Preface to the Second Edition Preface to the First Edition vii xi 1 Introduction 1 2 Overview of Supervised Learning 9 2.1 Introduction... 9 2.2 Variable Types and Terminology... 9 2.3 Two Simple Approaches
Applying Supervised Learning
Applying Supervised Learning When to Consider Supervised Learning A supervised learning algorithm takes a known set of input data (the training set) and known responses to the data (output), and trains
Classification of Protein Crystallization Imagery
Classification of Protein Crystallization Imagery Xiaoqing Zhu, Shaohua Sun, Samuel Cheng Stanford University Marshall Bern Palo Alto Research Center September 2004, EMBC 04 Outline Background X-ray crystallography
Outrun Your Competition With SAS In-Memory Analytics Sascha Schubert Global Technology Practice, SAS
Outrun Your Competition With SAS In-Memory Analytics Sascha Schubert Global Technology Practice, SAS Topics AGENDA Challenges with Big Data Analytics How SAS can help you to minimize time to value with
DD143X Degree Project in Computer Science, First Level. Classification of Electroencephalographic Signals for Brain-Computer Interface
DD143X Degree Project in Computer Science, First Level Classification of Electroencephalographic Signals for Brain-Computer Interface Fredrick Chahine 900505-2098 fchahine@kth.se Supervisor: Pawel Herman
ICA+OPCA FOR ARTIFACT-ROBUST CLASSIFICATION OF EEG DATA
ICA+OPCA FOR ARTIFACT-ROBUST CLASSIFICATION OF EEG DATA Sungcheol Park, Hyekyoung Lee and Seungjin Choi Department of Computer Science Pohang University of Science and Technology San 31 Hyoja-dong, Nam-gu
Louis Fourrier Fabien Gaie Thomas Rolf
CS 229 Stay Alert! The Ford Challenge Louis Fourrier Fabien Gaie Thomas Rolf Louis Fourrier Fabien Gaie Thomas Rolf 1. Problem description a. Goal Our final project is a recent Kaggle competition submitted
Classification of Mental Task for Brain Computer Interface Using Artificial Neural Network
Classification of Mental Task for Brain Computer Interface Using Artificial Neural Network Mohammad Naushad 1, Mohammad Waseem Khanooni 2, Nicky Ballani 3 1,2,3 Department of Electronics and Telecommunication
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features Xu SUN ( 孙栩 ) Peking University xusun@pku.edu.cn Motivation Neural networks -> Good Performance CNN, RNN, LSTM
Tutorial on Machine Learning Tools
Tutorial on Machine Learning Tools Yanbing Xue Milos Hauskrecht Why do we need these tools? Widely deployed classical models No need to code from scratch Easy-to-use GUI Outline Matlab Apps Weka 3 UI TensorFlow
Brain enabled mechanized speech synthesizer using Brain Mobile Interface
Brain enabled mechanized speech synthesizer using Brain Mobile Interface G.N.Jayabhavani, N.R.Raajan School of Electrical and Electronics Engineering, SASTRA University, Thanjavur, TamilNadu, India Email:
Evaluation Metrics. (Classifiers) CS229 Section Anand Avati
Evaluation Metrics (Classifiers) CS Section Anand Avati Topics Why? Binary classifiers Metrics Rank view Thresholding Confusion Matrix Point metrics: Accuracy, Precision, Recall / Sensitivity, Specificity,
Topics in AI (CPSC 532L): Multimodal Learning with Vision, Language and Sound. Lecture 12: Deep Reinforcement Learning
Topics in AI (CPSC 532L): Multimodal Learning with Vision, Language and Sound Lecture 12: Deep Reinforcement Learning Types of Learning Supervised training Learning from the teacher Training data includes
EEG-Controlling Robotic Car and Alphabetic Display by Support Vector Machine for Aiding Amyotrophic Lateral Sclerosis Patients
University of Arkansas, Fayetteville ScholarWorks@UARK Electrical Engineering Undergraduate Honors Theses Electrical Engineering 12-2015 EEG-Controlling Robotic Car and Alphabetic Display by Support Vector
7 Techniques for Data Dimensionality Reduction
7 Techniques for Data Dimensionality Reduction Rosaria Silipo KNIME.com The 2009 KDD Challenge Prediction Targets: Churn (contract renewals), Appetency (likelihood to buy specific product), Upselling (likelihood
Data Mining. Neural Networks
Data Mining Neural Networks Goals for this Unit Basic understanding of Neural Networks and how they work Ability to use Neural Networks to solve real problems Understand when neural networks may be most
Tap Position Inference on Smart Phones
Tap Position Inference on Smart Phones Ankush Chauhan 11-29-2017 Outline Introduction Application Architecture Sensor Event Data Sensor Data Collection App Demonstration Scalable Data Collection Pipeline
3D Face and Hand Tracking for American Sign Language Recognition
3D Face and Hand Tracking for American Sign Language Recognition NSF-ITR (2004-2008) D. Metaxas, A. Elgammal, V. Pavlovic (Rutgers Univ.) C. Neidle (Boston Univ.) C. Vogler (Gallaudet) The need for automated
Analyzing Electroencephalograms Using Cloud Computing Techniques
Analyzing Electroencephalograms Using Cloud Computing Techniques Kathleen Ericson Shrideep Pallickara Charles W. Anderson Colorado State University December 1, 2010 Outline Background 1 Background BCI
A P300-speller based on event-related spectral perturbation (ERSP) Ming, D; An, X; Wan, B; Qi, H; Zhang, Z; Hu, Y
Title A P300-speller based on event-related spectral perturbation (ERSP) Author(s) Ming, D; An, X; Wan, B; Qi, H; Zhang, Z; Hu, Y Citation The 01 IEEE International Conference on Signal Processing, Communication
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models DB Tsai Steven Hillion Outline Introduction Linear / Nonlinear Classification Feature Engineering - Polynomial Expansion Big-data
Interpretable Machine Learning with Applications to Banking
Interpretable Machine Learning with Applications to Banking Linwei Hu Advanced Technologies for Modeling, Corporate Model Risk Wells Fargo October 26, 2018 2018 Wells Fargo Bank, N.A. All rights reserved.
Design of classification methods for Brain Computer Interfaces: An application using the OpenViBE software
Informatics Engineering Degree Software Engineering Degree s Final Project Design of classification methods for Brain Computer Interfaces: An application using the OpenViBE software Author Ibai Díez Oronoz
Brain-Machine Interface Based on EEG Mapping to Control an Assistive Robotic Arm
The Fourth IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics Roma, Italy. June 24-27, 2012 Brain-Machine Interface Based on EEG Mapping to Control an Assistive Robotic Arm
Feature extraction feature selection and dimensionality reduction techniques for brain computer interface
Oregon Health & Science University OHSU Digital Commons Scholar Archive July 2011 Feature extraction feature selection and dimensionality reduction techniques for brain computer interface Tian Lan Follow
The Fly & Anti-Fly Missile
The Fly & Anti-Fly Missile Rick Tilley Florida State University (USA) rt05c@my.fsu.edu Abstract Linear Regression with Gradient Descent are used in many machine learning applications. The algorithms are
A BRAIN CONTROLLED WHEELCHAIR TO NAVIGATE IN FAMILIAR ENVIRONMENTS BRICE REBSAMEN A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
A BRAIN CONTROLLED WHEELCHAIR TO NAVIGATE IN FAMILIAR ENVIRONMENTS BRICE REBSAMEN (M.Sc., ENSEIRB, France) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF MECHANICAL ENGINEERING
Using Machine Learning to Optimize Storage Systems
Using Machine Learning to Optimize Storage Systems Dr. Kiran Gunnam 1 Outline 1. Overview 2. Building Flash Models using Logistic Regression. 3. Storage Object classification 4. Storage Allocation recommendation
EEG P300 wave detection using Emotiv EPOC+: Effects of matrix size, flash duration, and colors
EEG P300 wave detection using Emotiv EPOC+: Effects of matrix size, flash duration, and colors Saleh Alzahrani Corresp., 1, Charles W Anderson 2 1 Department of Biomedical Engineering, Imam Abdulrahman
CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS
130 CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS A mass is defined as a space-occupying lesion seen in more than one projection and it is described by its shapes and margin
Machine Learning 13. week
Machine Learning 13. week Deep Learning Convolutional Neural Network Recurrent Neural Network 1 Why Deep Learning is so Popular? 1. Increase in the amount of data Thanks to the Internet, huge amount of
Brain-Computer Interface Binary Classification using Regularized CSP and Stacked Concept
Brain-Computer Interface Binary Classification using Regularized CSP and B. Bijitha #1, Nandakumar Paramparambath *2 # M.Tech Student, * Professor Dept. of Electronics and Communication NSS College of
Network Traffic Measurements and Analysis
DEIB - Politecnico di Milano Fall, 2017 Sources Hastie, Tibshirani, Friedman: The Elements of Statistical Learning James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning Andrew Ng:
A Combined Method for On-Line Signature Verification
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 14, No 2 Sofia 2014 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2014-0022 A Combined Method for On-Line
Ensemble methods in machine learning. Example. Neural networks. Neural networks
Ensemble methods in machine learning Bootstrap aggregating (bagging) train an ensemble of models based on randomly resampled versions of the training set, then take a majority vote Example What if you
Feature Selection for fmri Classification
Feature Selection for fmri Classification Chuang Wu Program of Computational Biology Carnegie Mellon University Pittsburgh, PA 15213 chuangw@andrew.cmu.edu Abstract The functional Magnetic Resonance Imaging
Detecting Limb Movements by Reading Minds
Detecting Limb Movements by Reading Minds Lars Hiller Eidnes Master i informatikk Oppgaven levert: Desember 2011 Hovedveileder: Pinar Öztürk, IDI Norges teknisk-naturvitenskapelige universitet Problem
Large Scale Data Analysis Using Deep Learning
Large Scale Data Analysis Using Deep Learning Machine Learning Basics - 1 U Kang Seoul National University U Kang 1 In This Lecture Overview of Machine Learning Capacity, overfitting, and underfitting
Natural Language Processing CS 6320 Lecture 6 Neural Language Models. Instructor: Sanda Harabagiu
Natural Language Processing CS 6320 Lecture 6 Neural Language Models Instructor: Sanda Harabagiu In this lecture We shall cover: Deep Neural Models for Natural Language Processing Introduce Feed Forward
5 Learning hypothesis classes (16 points)
5 Learning hypothesis classes (16 points) Consider a classification problem with two real valued inputs. For each of the following algorithms, specify all of the separators below that it could have generated
Statistical Analysis of Metabolomics Data. Xiuxia Du Department of Bioinformatics & Genomics University of North Carolina at Charlotte
Statistical Analysis of Metabolomics Data Xiuxia Du Department of Bioinformatics & Genomics University of North Carolina at Charlotte Outline Introduction Data pre-treatment 1. Normalization 2. Centering,
Class 6 Large-Scale Image Classification
Class 6 Large-Scale Image Classification Liangliang Cao, March 7, 2013 EECS 6890 Topics in Information Processing Spring 2013, Columbia University http://rogerioferis.com/visualrecognitionandsearch Visual
Statistical Analysis of MRI Data
Statistical Analysis of MRI Data Shelby Cummings August 1, 2012 Abstract Every day, numerous people around the country go under medical testing with the use of MRI technology. Developed in the late twentieth
Learning algorithms for physical systems: challenges and solutions
Learning algorithms for physical systems: challenges and solutions Ion Matei Palo Alto Research Center 2018 PARC 1 All Rights Reserved System analytics: how things are done Use of models (physics) to inform
Modelling of EEG Data for Right and Left Hand Movement
Modelling of EEG Data for Right and Left Hand Movement Priya Varshney, Rakesh Narvey Abstract Brain machine Interface (BMI) improves the approach to life of traditional individuals by enhancing their performance
Feature Selection. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani
Feature Selection CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Dimensionality reduction Feature selection vs. feature extraction Filter univariate
Introduction to Automated Text Analysis. bit.ly/poir599
Introduction to Automated Text Analysis Pablo Barberá School of International Relations University of Southern California pablobarbera.com Lecture materials: bit.ly/poir599 Today 1. Solutions for last
Accelerated Machine Learning Algorithms in Python
Accelerated Machine Learning Algorithms in Python Patrick Reilly, Leiming Yu, David Kaeli reilly.pa@husky.neu.edu Northeastern University Computer Architecture Research Lab Outline Motivation and Goals
Apprenticeship Learning for Reinforcement Learning. with application to RC helicopter flight Ritwik Anand, Nick Haliday, Audrey Huang
Apprenticeship Learning for Reinforcement Learning with application to RC helicopter flight Ritwik Anand, Nick Haliday, Audrey Huang Table of Contents Introduction Theory Autonomous helicopter control
Journal of Articles in Support of The Null Hypothesis
Data Preprocessing Martin M. Monti, PhD UCLA Psychology NITP 2016 Typical (task-based) fmri analysis sequence Image Pre-processing Single Subject Analysis Group Analysis Journal of Articles in Support
Neurocomputing 108 (2013) Contents lists available at SciVerse ScienceDirect. Neurocomputing. journal homepage:
Neurocomputing 108 (2013) 69 78 Contents lists available at SciVerse ScienceDirect Neurocomputing journal homepage: www.elsevier.com/locate/neucom Greedy solutions for the construction of sparse spatial
Tutorials Case studies
1. Subject Three curves for the evaluation of supervised learning methods. Evaluation of classifiers is an important step of the supervised learning process. We want to measure the performance of the classifier.
CS145: INTRODUCTION TO DATA MINING
CS145: INTRODUCTION TO DATA MINING 08: Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu October 24, 2017 Learnt Prediction and Classification Methods Vector Data
CS229 Project: Classification of Motor Tasks Based on Functional Neuroimaging
CS229 Project: Classification of Motor Tasks Based on Functional Neuroimaging Gerald Brantner Mechanical Engineering, Stanford University, Stanford, CA 9435, USA. geraldb@stanford.edu Georg Schorpp Management
Tutorials (M. Biehl)
Tutorials 09-11-2018 (M. Biehl) Suggestions: - work in groups (as formed for the other tutorials) - all this should work in the python environments that you have been using; but you may also switch to
Classification of objects from Video Data (Group 30)
Classification of objects from Video Data (Group 30) Sheallika Singh 12665 Vibhuti Mahajan 12792 Aahitagni Mukherjee 12001 M Arvind 12385 1 Motivation Video surveillance has been employed for a long time
QstatLab: software for statistical process control and robust engineering
QstatLab: software for statistical process control and robust engineering I.N.Vuchkov Iniversity of Chemical Technology and Metallurgy 1756 Sofia, Bulgaria qstat@dir.bg Abstract A software for quality
BCI Based Analysis Of Hand Movement Trajectories For Rehabilitate Patients
BCI Based Analysis Of Hand Movement Trajectories For Rehabilitate Patients Ms.Ninu.J.S. PG Scholar, Dept. of ECE Younus College of Engineering and Technology, Kollam, Kerala ninujs@gmail.com Ms. Sreeja.M.S.
Machine Learning / Jan 27, 2010
Revisiting Logistic Regression & Naïve Bayes Aarti Singh Machine Learning 10-701/15-781 Jan 27, 2010 Generative and Discriminative Classifiers Training classifiers involves learning a mapping f: X -> Y,
Performing real-time BCI experiments
Performing real-time BCI experiments g.tec medical engineering GmbH Herbersteinstr. 60 8020 Graz, Austria www.gtec.at guger@gtec.at g.usbamp is a biosignal acquisition system for EEG, ECG, EMG, EOG and
Classification using Weka (Brain, Computation, and Neural Learning)
LOGO Classification using Weka (Brain, Computation, and Neural Learning) Jung-Woo Ha Agenda Classification General Concept Terminology Introduction to Weka Classification practice with Weka Problems: Pima
Motion illusion, rotating snakes
Motion illusion, rotating snakes Local features: main components 1) Detection: Find a set of distinctive key points. 2) Description: Extract feature descriptor around each interest point as vector. x 1
1 Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals.
1 Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals. N. Jeremy Hill, 1 Thomas Navin Lal, 1 Michael Schröder, 2 Thilo Hinterberger, 3 Guido Widman, 4 Christian E. Elger, 4 Bernhard
3D RECONSTRUCTION OF BRAIN TISSUE
3D RECONSTRUCTION OF BRAIN TISSUE Hylke Buisman, Manuel Gomez-Rodriguez, Saeed Hassanpour {hbuisman, manuelgr, saeedhp}@stanford.edu Department of Computer Science, Department of Electrical Engineering
A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based
Home Search Collections Journals About Contact us My IOPscience A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain computer interface This content has been downloaded
A Simple Generative Model for Single-Trial EEG Classification
A Simple Generative Model for Single-Trial EEG Classification Jens Kohlmorgen and Benjamin Blankertz Fraunhofer FIRST.IDA Kekuléstr. 7, 12489 Berlin, Germany {jek, blanker}@first.fraunhofer.de http://ida.first.fraunhofer.de
Fast or furious? - User analysis of SF Express Inc
CS 229 PROJECT, DEC. 2017 1 Fast or furious? - User analysis of SF Express Inc Gege Wen@gegewen, Yiyuan Zhang@yiyuan12, Kezhen Zhao@zkz I. MOTIVATION The motivation of this project is to predict the likelihood
DIGITS DEEP LEARNING GPU TRAINING SYSTEM
DIGITS DEEP LEARNING GPU TRAINING SYSTEM AGENDA 1 Introduction to Deep Learning 2 What is DIGITS 3 How to use DIGITS Practical DEEP LEARNING Examples Image Classification, Object Detection, Localization,
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIENCE, VOL.32, NO.9, SEPTEMBER 2010 Hae Jong Seo, Student Member,
1 Introduction. 3 Data Preprocessing. 2 Literature Review
Rock or not? This sure does. [Category] Audio & Music CS 229 Project Report Anand Venkatesan(anand95), Arjun Parthipan(arjun777), Lakshmi Manoharan(mlakshmi) 1 Introduction Music Genre Classification continues
PREPROCESSING FOR ADVANCED DATA ANALYSIS
PREPROCESSING FOR ADVANCED DATA ANALYSIS PSYC696B CHAP 7 JL SANGUINETTI Preprocessing Reorganization Transformation Data analysis Collecting EEG Organize Extracting epochs Adjusting event codes Removing
Data Mining: STATISTICA
Outline Data Mining: STATISTICA Prepare the data Classification and regression (C & R, ANN) Clustering Association rules Graphic user interface Prepare the Data Statistica can read from Excel,.txt and
Contents Machine Learning concepts 4 Learning Algorithm 4 Predictive Model (Model) 4 Model, Classification 4 Model, Regression 4 Representation
Contents Machine Learning concepts 4 Learning Algorithm 4 Predictive Model (Model) 4 Model, Classification 4 Model, Regression 4 Representation Learning 4 Supervised Learning 4 Unsupervised Learning 4
Lecture 7: Neural network acoustic models in speech recognition
CS 224S / LINGUIST 285 Spoken Language Processing Andrew Maas Stanford University Spring 2017 Lecture 7: Neural network acoustic models in speech recognition Outline Hybrid acoustic modeling overview Basic
Otto Group Product Classification Challenge
Otto Group Product Classification Challenge Hoang Duong May 19, 2015 1 Introduction The Otto Group Product Classification Challenge is the biggest Kaggle competition to date with 3590 participating teams.
Research Article A Hybrid Brain-Computer Interface-Based Mail Client
Computational and Mathematical Methods in Medicine Volume 2013, Article ID 750934, 9 pages http://dx.doi.org/10.1155/2013/750934 Research Article A Hybrid Brain-Computer Interface-Based Mail Client Tianyou
CS4670: Computer Vision
CS4670: Computer Vision Noah Snavely Lecture 6: Feature matching and alignment Szeliski: Chapter 6.1 Reading Last time: Corners and blobs Scale-space blob detector: Example Feature descriptors We know
FEATURE EVALUATION FOR EMG-BASED LOAD CLASSIFICATION
FEATURE EVALUATION FOR EMG-BASED LOAD CLASSIFICATION Anne Gu Department of Mechanical Engineering, University of Michigan Ann Arbor, Michigan, USA ABSTRACT Human-machine interfaces (HMIs) often have pattern
Phase-Functioned Neural Networks for Motion Learning
Phase-Functioned Neural Networks for Motion Learning TAMS University of Hamburg 03.01.2018 Sebastian Starke University of Edinburgh School of Informatics Institue of Perception, Action and Behaviour Sebastian.Starke@ed.ac.uk
Extending reservoir computing with random static projections: a hybrid between extreme learning and RC
Extending reservoir computing with random static projections: a hybrid between extreme learning and RC John Butcher 1, David Verstraeten 2, Benjamin Schrauwen 2,CharlesDay 1 and Peter Haycock 1 1- Institute
Janitor Bot - Detecting Light Switches Jiaqi Guo, Haizi Yu December 10, 2010
1. Introduction Janitor Bot - Detecting Light Switches Jiaqi Guo, Haizi Yu December 10, 2010 The demand for janitorial robots has gone up with the rising affluence and increasingly busy lifestyles of people
A Study of recent classification algorithms and a novel approach for biosignal data classification
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 2010 A Study of recent classification algorithms and a novel approach for biosignal data classification Eyup Cinar
CS249: ADVANCED DATA MINING
CS249: ADVANCED DATA MINING Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu April 24, 2017 Homework 2 out Announcements Due May 3 rd (11:59pm) Course project proposal
Genetic algorithm and forward method for feature selection in EEG feature space
Journal of Theoretical and Applied Computer Science Vol. 7, No. 2, 2013, pp. 72-82 ISSN 2299-2634 (printed), 2300-5653 (online) http://www.jtacs.org Genetic algorithm and forward method for feature selection
Decoding the Human Motor Cortex
Computer Science 229 December 14, 2013 Primary authors: Paul I. Quigley 16, Jack L. Zhu 16 Comment to piq93@stanford.edu, jackzhu@stanford.edu Decoding the Human Motor Cortex Abstract: A human being s
Neural Networks. Robot Image Credit: Viktoriya Sukhanova 123RF.com
Neural Networks These slides were assembled by Eric Eaton, with grateful acknowledgement of the many others who made their course materials freely available online. Feel free to reuse or adapt these slides
Multi-label classification using rule-based classifier systems
Multi-label classification using rule-based classifier systems Shabnam Nazmi (PhD candidate) Department of electrical and computer engineering North Carolina A&T state university Advisor: Dr. A. Homaifar