Brain-computer interfaces: classifying imaginary movements and effects of tDCS

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Brain-computer interfaces classifying imaginary
movements and effects of tDCS

• Iulia Comsa
• MRes Computational Neuroscience and Cognitive
  Robotics

Supervisors Dr Saber Sami Dr Dietmar Heinke
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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)

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Brain-computer interfaces (BCIs)

• What is a BCI?
• A communication system that does not depend on
  the brains normal output pathways of peripheral
  nerves and muscles (Wolpaw et al., 2000)
• In this project BCIs based on motor imagery

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The structure of a BCI

• Wolpaw et al. (2002)

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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/
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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
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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
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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)
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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

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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

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Investigating the effects of tDCS

• ERP grand averages (ERPLAB Toolbox)

Anode
Sham
Cathode
Real taps
Imagined taps
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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
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Investigating the effects of tDCS

• Significant differences for real taps

Anode-Cathode
Anode-Sham
Cathode-Sham
85 ms
230 ms
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Investigating the effects of tDCS

• Differences for imagined taps

Anode-Cathode
Anode-Sham
Cathode-Sham
80 ms
700 ms
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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

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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

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Building a BCI with robotic feedback
BCI2000 a general-purpose system for BCI research
consisting of configurable modules
Stimulus Presentation
Signal Acquisition
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

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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)

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Imagined movements pilot study

• r2 (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

Channel
Frequency (1-70 Hz)
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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

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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

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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

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Thank you.