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Classification of EEG signals between walking and non-walking states

Classification of EEG signals between walking and non-walking states. Matt Beall Kyle Crandall Babak Hejrati Dara Scher. Problem statement. How can we detect when a user wants to start or stop walking?. Emotive epoc device. Head mounted EEG

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Classification of EEG signals between walking and non-walking states

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  1. Classification of EEG signals between walking and non-walking states Matt Beall Kyle Crandall BabakHejrati DaraScher

  2. Problem statement • How can we detect when a user wants to start or stop walking?

  3. Emotive epoc device • Head mounted EEG • Built for hands free machine-human interaction • 14 EEG signals • 2 Gyro signals • EEG and Gyro signals filtered and recorded

  4. Experimental Setup • 4 subjects (ourselves) • 20 static trials (10 standing, 10 walking) • 5 dynamic trials (standwalkstand) • First: using only static trials • 80% used as training • 20% for testing • Second: using static and dynamic trials • Static trials used as training • Dynamic trials used for testing

  5. Raw Data Classification • SVM with polynomial kernel of varying degree • Pros and Cons

  6. Fast Fourier Transform: Preprocessing • FFT performed on data over time • Trials of raw data segmented into time section • Take mean of all points in FFT set and all sensors • Higher mean walking • Lower mean standing • Feature vector simplified to R1

  7. Classification • Results • Pros and Cons

  8. Readiness classification: Data Manipulation • Used FFT method on a linear combination of signals • Readiness = AF4 + F4 + FC6 + AF3 + F3 + FC5 – (O1 + O2) / 2 • On going work • Advantages and Drawbacks

  9. Conclusion Results Future Work • Make work in real time • More testing and tuning • Develop simple training method for new users • Implement on TPAWT • Determine how results translate from day to day and user to user • Explore other combinations of signals • Developed an algorithm that can classify EEG data into walking and not walking with • 98% accuracyfor static trials • 79% accuracy for dynamic trials • Determined that feature in frequency domain provides better accuracy

  10. Questions? Thank you for your time.

  11. Bibliography • “Emotive Epoc” in Emotiv EPOC user Manual • “Figure 1 Scalp locations covered by Emotiv EPO,” in Research Use of Emotiv EPOC[online]Available: http://neurofeedback.visaduma.info/emotivresearch.htm • “Treadport,” in Realism in a Virtual World: Graphics, Movement, Touch & Smell Combine for a Lifelike Virtual Environment [online]. Available: http://eae.utah.edu/research/treadport-and-rehabilitation/

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