1. Brain-computer interfaces: classifying imaginary movements and effects of tDCS Iulia Comşa MRes Computational Neuroscience and Cognitive Robotics Supervisors: Dr Saber Sami Dr Dietmar Heinke

2. Presentation structure  An overview of brain-computer interfaces  Experiment 1: effects of tDCS on the EEG  Implementing a brain-computer interface with robotic feedback  Experiment 2: imagined movements (pilot study)

3. Brain-computer interfaces (BCIs)  What is a BCI?  “A communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles” (Wolpaw et al., 2000)  In this project: BCIs based on motor imagery

4. The structure of a BCI Wolpaw et al. (2002)

5. Brain imaging techniques for BCIs  Electroencephalography (EEG)  Records electric potentials from the scalp  Advantages:  Very good temporal resolution  Comfortable and cost-efficient  Already on the market for home entertainment http://www.biosemi.com/

6. Brain imaging techniques for BCIs  Transcranial direct current stimulation (tDCS)  Direct current applied to the brain  Induces changes in cortical excitability  Anodal: increases excitability  Cathodal: decreases excitability http://www.neuroconn.de

7. Brain imaging techniques for BCIs  Transcranial direct current stimulation (tDCS)  Influences TMS-induced motor evoked responses in real or imagined movements (Lang et al. 2004, Quartarone et al. 2004)  Potential benefit for classification  No study in literature about its effect on the EEG in the motor area http://www.neuroconn.de

8. Investigating the effects of tDCS Question: Does tDCS produce significant changes in event-related potentials in the motor area?  Event-related potential (ERP): brief change in electric potential that follows a motor, sensory or cognitive event Luck et al. (2007)

9. Investigating the effects of tDCS  Previously collected data available  Three groups of participants (9 participants each)  Anodal tDCS  Cathodal tDCS  Sham  Task  250 real finger taps  250 imaginary finger taps  Two sessions: before and after tDCS  Data collection  128 EEG channels using a Biosemi ActiveTwo system

10. Investigating the effects of tDCS  Data pre-processing (EEGLAB Toolbox)  Filtering  Between 1 and 100 Hz  Epochs (segments of data) were extracted between 0 and 1 second following the stimulus  Artefact rejection  Removing data contaminated by noise (e.g. blinks)  By amplitude threshold (55-125 mV) and manually

11. Investigating the effects of tDCS Real taps Anode Cathode Sham Imagined taps  ERP grand averages (ERPLAB Toolbox)

12. Investigating the effects of tDCS  Permutation t-tests (Mass Univariate ERP Toolbox)  Family-wise alpha level: 0.05  2500 permutations  Tmax statistic (Blair & Karniski, 1993) Anode-Cathode t-scores, real finger taps after tDCS [video]

13. Investigating the effects of tDCS  Significant differences for real taps Anode-CathodeAnode-ShamCathode-Sham ~ 85 ms ~ 230 ms

14.  Differences for imagined taps Investigating the effects of tDCS Anode-CathodeAnode-ShamCathode-Sham ~ 80 ms ~ 700 ms

15. Effects of tDCS on ERPs: Summary  Significant effects found for anodal tDCS in the motor area around 85 and 230 ms during real movements  Significant effects found for cathodal tDCS around 700 ms in the parietal area during imaginary movements  Although not always significant, differences in the motor area are visible in all conditions

16. Oscillatory EEG processes  ERPs: phase-locked activity  What if the response is not phase-locked?  Induced responses: EEG frequency bands  Mu rhythms: 8-13 Hz  Recorded from the sensorimotor cortex while it is idle  Briefly suppressed when an action is performed or imagined  Beta rhythms: 13-30 Hz  Gamma rhythms: 30-40 Hz, 60-90 Hz

17. Building a BCI with robotic feedback BCI2000 a general-purpose system for BCI research consisting of configurable modules Signal Acquisition Stimulus Presentation Signal Processing BCILAB Toolbox - provides: Signal preprocessing (filtering, cleaning) Feature extraction: Common Spatial Patterns Machine learning algorithms for classification RWTH Aachen MINDSTORMS NXT Toolbox Robot arm control

18. Imagined movements pilot study  3 healthy participants  Imagined left and right hand clenching (100 trials each)  Data collection: 32 electrodes covering the motor-premotor area (using a Biosemi ActiveTwo system)

19. Imagined movements pilot study  r 2 (coefficient of determination): the amount of variance that is accounted for by the task condition  Strongest activity: 10-30 Hz in lateral electrodes  Some activity above 60 Hz Participant 1Participant 2Participant 3 Channel Frequency (1-70 Hz)

20. Imagined movements pilot study  Best results – 10 fold cross-validation:  Epochs between 1 and 2 seconds after stimulus  Classifier: linear discriminant analysis  Participant 2: 88,5% accuracy  Common Spatial Patterns  FIR Filter: 10-30 Hz bandpass  Participant 3: 85,5% accuracy  Filter-Bank Common Spatial Patterns  Frequency windows: 8-30 Hz and 8-15 Hz  No model with accuracy better than 65% could be trained for Participant 1

21. Further work: Improving the results  More trials  Problem: subjects may get bored  Adding online feedback  Problem: we would already need a good classifier  Incorporating purpose in the motor imagery  “Clenching a fist” versus “grabbing a pen”  Using tDCS  99% accuracy for the tDCS data from Experiment 1

22. Project summary  We showed that tDCS has significant effects on event-related potentials  We implemented a brain-computer interface with robotic feedback  We performed a pilot study and explored classification of left and right imaginary movements

23. Thank you.