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 Deniz Erdogmus, Associate Professor, Northestern University College of Computer Engineering, Cognitive Systems Laboratory

2. Why use brain interfaces?
Help a targeted group of individuals with severe speech and motor impairments who are unable to perform simple tasks or communicate with everyday individuals
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3. Brain Interface
Stimulus
User
EEG
Classifier
Decision

4. SSVEP Brain Interface Video

5. SSVEP: Stands for “Steady State Visually Evoked Potential”
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.
Definitions

6. Minimum Mean Distance Classifier
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.

7. Minimum Mean Distance Classifier
Classification of Iris Flower Dataset Using
Minimum Mean Distance Classifier
Ground Truth Class
Estimated Class
I. setosa
I. 
versicolor I.
 virginica 1
I. versicolor
0.92
0.14
I. virginica
0.08
0.86

8. Minimum Mean Distance Classifier
Classification of EEG Data Using
Minimum Mean Distance Classifier
Ground Truth Class
Estimated Class
20 Hz
15 Hz
12 Hz 1

9. k-Nearest Neighbor 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

10. k-Nearest Neighbor Classifier
Classification of Iris Flower Dataset Using
Minimum Mean Distance Classifier
Ground Truth Class
Estimated Class
I. setosa
I. 
versicolor I.
 virginica 1
I. versicolor
0.94
0.04
I. virginica
0.06
0.96

11. K-Nearest Neighbor Classifier
Classification of EEG Data Using
K-Nearest Neighbor Classifier
Ground Truth Class
Estimated Class
20 Hz
15 Hz
12 Hz 1

12. K Fold Cross Validation
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
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13. Applications RSVP Typing system
Uses P300 brain signal to determine which letter is the acquired target
SSVEP brain interface
Control robot motions through looking at a screen
Applications
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14. Applications
Can classify not just EEG data, but many other types of data!
Iris Flower Dataset
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15. Future Work Perform K-Fold Cross Validation
Classify unprocessed EEG data using more advanced concepts to determine the most likely decision
Classify EEG data obtained from other types of stimuli such as tactile sensors
Help individuals with Locked-in Syndrome to communicate and with others through brain interfaces
Future Work

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

17. Questions?