Transcript Brain-computer interfaces: classifying real and imaginary

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
Presentation structure
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An overview of brain-computer interfaces
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Experiment 1: effects of tDCS on the EEG
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Implementing a brain-computer interface with
robotic feedback
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Experiment 2: imagined movements (pilot study)
Brain-computer interfaces (BCIs)
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What is a BCI?
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“A communication system that does not depend on
the brain’s normal output pathways of peripheral
nerves and muscles” (Wolpaw et al., 2000)
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In this project: BCIs based on motor imagery
The structure of a BCI
Wolpaw et al. (2002)
Brain imaging techniques for BCIs
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Electroencephalography (EEG)
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Records electric potentials from the scalp
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Advantages:
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Very good temporal resolution
Comfortable and cost-efficient
http://www.biosemi.com/
Already on the market for home entertainment
Brain imaging techniques for BCIs
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Transcranial direct current stimulation (tDCS)
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Direct current applied to the brain
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Induces changes in cortical excitability
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Anodal: increases excitability
Cathodal: decreases excitability
http://www.neuroconn.de
Brain imaging techniques for BCIs
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Transcranial direct current stimulation (tDCS)
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Influences TMS-induced motor evoked
responses in real or imagined movements
(Lang et al. 2004, Quartarone et al. 2004)
http://www.neuroconn.de
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Potential benefit for classification
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No study in literature about its effect on the EEG in the
motor area
Investigating the effects of tDCS
Question: Does tDCS produce significant changes in
event-related potentials in the motor area?
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Event-related potential (ERP):
brief change in electric
potential that follows a
motor, sensory or cognitive
event
Luck et al. (2007)
Investigating the effects of tDCS
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Previously collected data available
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Three groups of participants (9 participants each)
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Task
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Anodal tDCS
Cathodal tDCS
Sham
250 real finger taps
250 imaginary finger taps
Two sessions: before and after tDCS
Data collection
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128 EEG channels using a Biosemi ActiveTwo system
Investigating the effects of tDCS
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Data pre-processing (EEGLAB Toolbox)
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Filtering
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Between 1 and 100 Hz
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Epochs (segments of data) were extracted
between 0 and 1 second following the
stimulus
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Artefact rejection
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Removing data contaminated by noise (e.g.
blinks)
By amplitude threshold (55-125 mV) and
manually
Investigating the effects of tDCS
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ERP grand averages (ERPLAB Toolbox)
Anode
Sham
Cathode
Real taps
Imagined taps
Investigating the effects of tDCS
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Permutation t-tests (Mass Univariate ERP Toolbox)
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Family-wise alpha level: 0.05
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2500 permutations
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Tmax statistic
(Blair & Karniski, 1993)
Anode-Cathode t-scores, real finger taps
after tDCS [video]
Investigating the effects of tDCS
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Significant differences for real taps
Anode-Cathode
~ 85 ms
~ 230 ms
Anode-Sham
Cathode-Sham
Investigating the effects of tDCS
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Differences for imagined taps
Anode-Cathode
Anode-Sham
~ 80 ms
~ 700 ms
Cathode-Sham
Effects of tDCS on ERPs: Summary
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Significant effects found for anodal tDCS in the
motor area around 85 and 230 ms during real
movements
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Significant effects found for cathodal tDCS
around 700 ms in the parietal area during
imaginary movements
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Although not always significant, differences in the
motor area are visible in all conditions
Oscillatory EEG processes
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ERPs: phase-locked activity
What if the response is not phase-locked?
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Induced responses: EEG frequency bands
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Mu rhythms: 8-13 Hz
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Recorded from the sensorimotor cortex while it is idle
Briefly suppressed when an action is performed or
imagined
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Beta rhythms: 13-30 Hz
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Gamma rhythms: 30-40 Hz, 60-90 Hz
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:
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Signal preprocessing (filtering, cleaning)
Feature extraction: Common Spatial Patterns
Machine learning algorithms for classification
RWTH Aachen MINDSTORMS NXT Toolbox
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Robot arm control
Imagined movements pilot study
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3 healthy participants
Imagined left and right hand clenching
(100 trials each)
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Data collection: 32 electrodes
covering the motor-premotor area
(using a Biosemi ActiveTwo system)
Imagined movements pilot study
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r2 (coefficient of determination): the amount of
variance that is accounted for by the task condition
Participant 1
Participant 2
Participant 3
Channel
Frequency (1-70 Hz)
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Strongest activity: 10-30 Hz in lateral electrodes
Some activity above 60 Hz
Imagined movements pilot study
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Best results – 10 fold cross-validation:
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Epochs between 1 and 2 seconds after stimulus
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Classifier: linear discriminant analysis
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Participant 2: 88,5% accuracy
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Common Spatial Patterns
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FIR Filter: 10-30 Hz bandpass
Participant 3: 85,5% accuracy
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Filter-Bank Common Spatial Patterns
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Frequency windows: 8-30 Hz and 8-15 Hz
No model with accuracy better than 65% could be trained
for Participant 1
Further work: Improving the results
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More trials
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Adding online feedback
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Problem: we would already need a good classifier
Incorporating purpose in the motor imagery
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Problem: subjects may get bored
“Clenching a fist” versus “grabbing a pen”
Using tDCS
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99% accuracy for the tDCS data from Experiment 1
Project summary
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We showed that tDCS has significant effects on
event-related potentials
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We implemented a brain-computer interface
with robotic feedback
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We performed a pilot study and explored
classification of left and right imaginary
movements
Thank you.