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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
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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 brain’s 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 Real taps Anode Cathode Sham Imagined taps ERP grand averages (ERPLAB Toolbox)
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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-CathodeAnode-ShamCathode-Sham ~ 85 ms ~ 230 ms
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Differences for imagined taps Investigating the effects of tDCS Anode-CathodeAnode-ShamCathode-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 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
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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 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)
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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.
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