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

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

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

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**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**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**Brain Interface**• EEG • User • Stimulus • Decision • Classifier**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.**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**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**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**Applications**• Image Source: http://www3.ece.neu.edu/~purwar/research/photo_gallery.htm, http://www3.ece.neu.edu/~orhan/**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**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