Minimum Mean Distance and k-Nearest Neighbor Classifiers for Signal Processing

1. Minimum Mean Distance and k-Nearest Neighbor Classifiers for Signal Processing Kun Yi Li, Young Scholar Student, Quincy High School Eric Lehman, Young Scholar Student, Belmont High School Graduate research mentors:Matt Higger, Fernando Quiviria, PhD Candidate, Northeastern University Professor DenizErdogmus, Associate Professor, NorthesternUniversity College of Computer Engineering, Cognitive Systems Laboratory

2. Help a targeted group of individuals with severe speech and motor impairments who are unable to perform simple tasks or communicate with everyday individuals Why use brain interfaces? Image Source: http://i2.cdn.turner.com/cnn/dam/assets/121016060125-orig-ideas-brainwave-wheelchair-00013909-story-top.jpg

3. Brain Interface • EEG • User • Stimulus • Decision • Classifier

4. SSVEP Brain Interface Video

5. Definitions • SSVEP: Stands for “Steady State Visually Evoked Potential”. This type of brain signal is a response to looking at repeated intensities of light from 0 to 60 Hz. • EEG: Stands for “electroencephalography”. EEG data is the measurement of the brain’s electrical activity voltages on the surface of the scalp over a certain period of time. • Iris Dataset: A dataset that contains 3 different types for flowers, 50 samples each, and 4 different features (sepal length in cm, sepal width in cm, petal length in cm, petal width in cm).   • Classifier: An algorithm that divides data into different group based on their similarities.

6. An algorithm that classifies multiple types of data. • When given a test point, the program: • calculates the distance from the new data point to the average of training data points. • selects the training data point with the shortest distance • identifies the new data point in the same group as the closest training point. Minimum Mean Distance Classifier

7. Minimum Mean Distance Classifier

8. Minimum Mean Distance Classifier

9. An algorithm that classifies and divides multiple types of data. • When given a new test data point, the KNN classifier: • 1. Calculates the distance from the test data to all training data points • 2. Selects the k number of training data points that are the closest to the test data point • 3. Identifies the test data point as the same as the most common class among the k nearest training data points k-Nearest Neighbor Classifier

10. k-Nearest Neighbor Classifier

11. K-Nearest Neighbor Classifier

12. Separates the training set from the test set by segmenting the data into k number of sections • The classifier will test on one section and train the remaining sections • Prevents overfitting K Fold Cross Validation Image Source: http://classes.engr.oregonstate.edu/eecs/winter2011/cs434/notes/knn-4.pdf

13. Applications • Image Source: http://www3.ece.neu.edu/~purwar/research/photo_gallery.htm, http://www3.ece.neu.edu/~orhan/

14. Applications Can classify not just EEG data, but many other types of data! Iris Flower Dataset Image source: http://en.wikipedia.org/wiki/Iris_flower_data_set

15. Future Work

16. Acknowledgements • Graduate Research Mentors: Matt Higger, Fernando Quivira, PhD Candidates, Northeastern University • Professor DenizErdogmus, Department of Electrical and Computer Engineering, Cognitive Systems Lab, Northeastern University • OrkanSezer, Summer intern, Northeastern University • Center for STEM Education • Young Scholars Program & Team • Claire Duggan - Director • Kassi Stein, Jake Holstein, Chi Tse - YSP Coordinators

17. Questions?