Brain State Modeling from EEG data through Machine Learning

Slides:



Advertisements
Similar presentations
Rerun of machine learning Clustering and pattern recognition.
Advertisements

CSCI 5582 Fall 2006 CSCI 5582 Artificial Intelligence Lecture 20 Jim Martin.
|| Dmitry Laptev, Joachim M. Buhmann Machine Learning Lab, ETH Zurich 05/09/14Dmitry Laptev1 Convolutional Decision Trees.
UCI KDD Archive University of California at Irvine –
Class Discussion Chapter 2 Neural Networks. Top Down vs Bottom Up What are the differences between the approaches to AI in chapter one and chapter two?
Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)
Three kinds of learning
Support Vector Machines
CS 732: Advance Machine Learning Usman Roshan Department of Computer Science NJIT.
Learning Programs Danielle and Joseph Bennett (and Lorelei) 4 December 2007.
Machine Learning: Final Presentation James Dalphond James McCauley Andrew Wilkinson Phil Kovac Data Set: Yeast GOLD TEAM.
Artificial Intelligence Techniques
Parallelization: Conway’s Game of Life. Cellular automata: Important for science Biology – Mapping brain tumor growth Ecology – Interactions of species.
Machine Learning CS 165B Spring 2012
Artificial Intelligence Lecture No. 28 Dr. Asad Ali Safi ​ Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.
Machine Learning1 Machine Learning: Summary Greg Grudic CSCI-4830.
LOGO Ensemble Learning Lecturer: Dr. Bo Yuan
Special topics on text mining [ Part I: text classification ] Hugo Jair Escalante, Aurelio Lopez, Manuel Montes and Luis Villaseñor.
CS Fall 2015 (© Jude Shavlik), Lecture 7, Week 3
Today Ensemble Methods. Recap of the course. Classifier Fusion
Machine Learning Methods Maximum entropy –Maxent is an example Boosting: –Boosted Regression Trees Neural Networks.
Feature (Gene) Selection MethodsSample Classification Methods Gene filtering: Variance (SD/Mean) Principal Component Analysis Regression using variable.
COP5992 – DATA MINING TERM PROJECT RANDOM SUBSPACE METHOD + CO-TRAINING by SELIM KALAYCI.
Prerequisites: -Emotiv headset (aprox. 300 $) - Head (you should have one)
COMP24111: Machine Learning Ensemble Models Gavin Brown
CS 189 Brian Chu Slides at: brianchu.com/ml/ brianchu.com/ml/ Office Hours: Cory 246, 6-7p Mon. (hackerspace lounge)
CS 732: Advance Machine Learning
Data Mining By: Johan Johansson. Mining Techniques Association Rules Association Rules Decision Trees Decision Trees Clustering Clustering Nearest Neighbor.
MIND CONTROLLED ROBOT BY ADITHYA KUMAR EIGHTH GRADE.
Introduction to Azure Machine Learning and Data Mining algorithms Oleksandr Krakovetskyi CEO, DevRain Solutions PhD, Microsoft Regional
Neural networks (2) Reminder Avoiding overfitting Deep neural network Brief summary of supervised learning methods.
How do you get here?
Accelerating K-Means Clustering with Parallel Implementations and GPU Computing Janki Bhimani Miriam Leeser Ningfang Mi
Position Benedict R. Gaster AMD. The world of programs.
BNFO 615 Fall 2016 Usman Roshan NJIT. Outline Machine learning for bioinformatics – Basic machine learning algorithms – Applications to bioinformatics.
Combining Models Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya.
Panel: Beyond Exascale Computing
Introduction to Machine Learning
Data-intensive Computing Algorithms: Classification
G. Suarez, J. Soares, S. Lopez, I. Obeid and J. Picone
Estimating Link Signatures with Machine Learning Algorithms
COMP61011 : Machine Learning Ensemble Models
Hire Toyota Innova in Delhi for Outstation Tour
© 2013 ExcelR Solutions. All Rights Reserved Examples of Random Forest.
© 2013 ExcelR Solutions. All Rights Reserved Data Mining - Supervised Decision Tree & Random Forest.
AV Autonomous Vehicles.
Motivation Computers are good at some things… Calculating 
Type Topic in here! Created by Educational Technology Network
Artificial Intelligence
CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 12: Combining models Geoffrey Hinton.
إعداد المشرفة التربوية نجلاء الجارد
Training a Neural Network
Logistic Regression & Parallel SGD
The Combination of Supervised and Unsupervised Approach
Deep Forest: Towards an Alternative to Deep Neural Networks
Using decision trees and their ensembles for analysis of NIR spectroscopic data WSC-11, Saint Petersburg, 2018 In the light of morning session on superresolution.
Artificial Intelligence Lecture No. 28
Discovering Activities of Daily Life Using RFID’s
Pattern Recognition & Machine Learning
Machine learning Empirical Performance Analysis
Ensemble learning.
Data Science in Industry
Тархи ба оюун \Brain and Mind\
Microarray Data Set The microarray data set we are dealing with is represented as a 2d numerical array.
Machine Learning with Clinical Data
Playback control using mind
Algorithms Lecture # 26 Dr. Sohail Aslam.
Machine Learning.
Advisor: Dr.vahidipour Zahra salimian Shaghayegh jalali Dec 2017
Lecturer: Geoff Hulten TAs: Alon Milchgrub, Andrew Wei
Presentation transcript:

Brain State Modeling from EEG data through Machine Learning Caleb Ji EEG headsets measure the electrical activity in your brain.

Applications of EEG Detection/diagnosis of disease Control of prosthetics/exoskeletons Meditation Mind control/world domination

So here we have… We’re trying to map different combinations of these waves to different brain states

Machine Learning Algorithms, Parallel R Calls C for OpenMP Rmpi – MPI Wrapper CUDA Boosting Random Forests Neural Networks Supervised algorithms Boosting: combining learning algorithms Random forests: ton of decision trees Neural networks: literally brain modeling