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Brain-Computer Interface (BCI) in a Motor Imagery Paradigm

Brain-Computer Interface (BCI) in a Motor Imagery Paradigm. Carlos Carreiras Adviser: Prof. João Sanches Co-Adviser: Prof. Luís Borges de Almeida. Motivation. A BCI attempts to provide an additional channel of communication for its users;

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Brain-Computer Interface (BCI) in a Motor Imagery Paradigm

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  1. Brain-Computer Interface (BCI) in a Motor Imagery Paradigm Carlos Carreiras Adviser: Prof. João Sanches Co-Adviser: Prof. Luís Borges de Almeida

  2. Carlos Carreiras December 2011 Motivation • A BCI attempts to provide an additional channel of communication for its users; • A user’s intent is directly extracted from the brain and translated into commands by the BCI; • Development of BCIs has been a very active research field in recent years; • Important for patients that are “locked in”, as they have limited motor function.

  3. Carlos Carreiras December 2011 Contributions • To develop an Electroencephalogram (EEG) BCI; • The BCI is controlled through the imagination of motor tasks; • A total of 6 different motor tasks are considered; • The motor tasks are identified by analysing the pattern of Event-Related Desynchronization (ERD) and Synchronization (ERS) in the EEG; • A new method to identify ERD/ERS, from the field of synchronization quantification, is propopsed.

  4. Carlos Carreiras December 2011 Outline • BCI Definition and Structure • Neurophysiology of Motor Tasks • Methods • Experimental Setup • Band Power Features • PLF Features • Classification • Experimental Results • Conclusions

  5. Carlos Carreiras December 2011 BCI Definition and Structure

  6. Carlos Carreiras December 2011 BCI Definition • A BCI is a system that measures brain signals and converts them into outputs; • These outputs do not depend on the normal pathways of peripheral nerves and muscles. • A user controls the BCI: • By perceiving a set of stimuli and concentrating on a certain stimulus that accomplishes the user’s intent; • By concentrating on a specific mental task.

  7. Carlos Carreiras December 2011 BCI Structure

  8. Carlos Carreiras December 2011 Signal Acquisition • Measures of brain activity: • Electrophysiological signals: • EEG; • ECoG; • Intracortical devices. • Magnetic systems: • MEG. • Metabolic measures: • fMRI; • NIRS.

  9. Carlos Carreiras December 2011 Feature Extraction • The feature extraction method depends on the type of mental task. • Time-Domain Features: • Filtering; • Wavelet transform; • AR models. • Frequency-Domain Features: • Fourier analysis; • Morlet wavelets; • AR models. Source: Bashashati et al., 2007

  10. Carlos Carreiras December 2011 Feature Classification • Classify features according to the experimental paradigm. • Typical algorithms: • Neural Networks; • Linear Discriminant Analysis; • Fisher’s Discriminant Analysis; • Support Vector Machine (SVM).

  11. Carlos Carreiras December 2011 Applications • Control of assistive technologies: • Communication; • Environment control; • Locomotion; • Gaming and virtual reality. • Neurorehabilitation.

  12. Carlos Carreiras December 2011 Neurophysiology of Motor Tasks

  13. Carlos Carreiras December 2011 The Motor Cortex • The Primary Motor Cortex (PMC) is responsible for planning and executing movements; • There is a correspondence between areas of the PMC and the various muscle groups; • Movement tasks induce changes to brain activity visible in the EEG. Source: Guyton and Hall, 2005

  14. Carlos Carreiras December 2011 ERD and ERS • Certain events change the oscillating dynamics of brain waves; • The changes are frequency-specific, and can be: • Decreases in power – Event-Related Desynchronization (ERD); • Increases in power – Event-Related Synchronization (ERS). • Neuronal networks become asynchronous during mental activity; • ERD – correlated with mental activity; • ERS – correlated with mental inactivity.

  15. Carlos Carreiras December 2011 ERD and ERS • During a motor task: • ERD in the corresponding region of the PMC (10 – 20 Hz); • ERS over unrelated cortical areas; • Post-movement ERS in the corresponding region of the PMC (13 – 25 Hz). Source: Piotr J. Durka, 2001 Imagination and observation of motor tasks produce similar changes in the brain as actual movement.

  16. Carlos Carreiras December 2011 Methods

  17. Carlos Carreiras December 2011 Experimental Setup • EEG signals acquired from 6 voluntary subjects; • Subjects imagined various motor tasks: • No Movement (CC) • Right Foot (RF); • Left Foot (LF); • Right Leg (RL); • Left Leg (LL); • Right Hand (RH); • Left Hand (LH); • Procedure:

  18. Carlos Carreiras December 2011 Band Power Features EEG Channel • ERD is traditionally measured by computing the EEG power in specific frequency bands: • Fourier Transform; • Auto-Regressive Models; • Continuous Wavelet Transform. • Disadvantages: • Necessary to select frequency band (changes with subject); • Indirect measure of the phenomenon. Windowing (256 ms, 50% overlap) Compute FFT Average Power (8 – 15 Hz)

  19. Carlos Carreiras December 2011 Analytical Signals • An analytical signal is a signal that has no negative-frequency components; • Obtained by adding a quarter-cycle time shift (Hilbert Transform Filter); • Phase obtained by: d/dt = 10 Hz d/dt = 10 Hz d/dt = 30 Hz

  20. Carlos Carreiras December 2011 Phase-Locking Factor Features • During ERD, certain neuronal networks become out of sync; • The Phase-Locking Factor (PLF) measures the synchronization between 2 signals: • PLF = 1 – perfect synchrony; • PLF = 0 – no synchrony. Windowing (256 ms, 50% overlap) Analytical Signals Compute PLF

  21. Carlos Carreiras December 2011 Classification • Support Vector Machines (SVMs) used in a hierarchical structure; • SVMs trained with a gaussian kernel; • Results evaluated with Leave-One-Out Cross Validation;

  22. Carlos Carreiras December 2011 Experimental Results

  23. Carlos Carreiras December 2011 EEG Signal • Imagination of right hand movement:

  24. Carlos Carreiras December 2011 Comparison of Features Band Power Features (BPF) Imagination of right hand movement. PLF Features (PLFF)

  25. Carlos Carreiras December 2011 Classification Results Actual Movement Imagined Movement Average BPF: 68.67 % PLFF: 86.58 % Average BPF: 71.86 % PLFF: 86.34 %

  26. Carlos Carreiras December 2011 Conclusions

  27. Carlos Carreiras December 2011 Summary • A BCI can be controlled through the imagination of motor tasks; • Detection of motor tasks is done by identifying ERD in the EEG: • Band Power Features; • PLF Features; • Classification is made with a hierarchical SVM classifier.

  28. Carlos Carreiras December 2011 Summary • The system is capable of distinguishing between 6 motor tasks. • PLF features provide better results than the traditional band power features: • Increase in average accuracy; • Decreased subject variability; • Less susceptible to noise.

  29. Carlos Carreiras December 2011 Future Work • Improve the experimental setup: • More subjects; • Better session procedure. • Better understanding of PLF features: • Mapping; • Combination with other features. • Feature selection; • Real-Time BCI: • Computational efficiency; • Continuous adaptation; • Good feedback system.

  30. Carlos Carreiras December 2011 Thank You! Acknowledgements The 6 subjects who volunteered their brains CENC and Prof. Teresa Paiva David Belo

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