Brain-computer interfaces: classifying imaginary movements and effects of tDCS Iulia Comşa MRes Computational Neuroscience and Cognitive Robotics Supervisors:

Brain-computer interfaces:

classifying imaginary movements

and effects of tDCS

Iulia Comşa

MRes Computational Neuroscience and Cognitive Robotics

Supervisors: Dr Saber SamiDr Dietmar Heinke

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)

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

The structure of a BCI

Wolpaw et al. (2002)

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/

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

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

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)

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

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

Investigating the effects of tDCS

Real taps

Anode

Cathode

Sham

Imagined taps

ERP grand averages (ERPLAB Toolbox)

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]

Investigating the effects of tDCS Significant differences for real taps

Anode-Cathode

Anode-Sham

Cathode-Sham

~ 85 ms

~ 230 ms

Differences for imagined taps

Investigating the effects of tDCS

Anode-Cathode

Anode-Sham

Cathode-Sham

~ 80 ms

~ 700 ms

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

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

Building a BCI with robotic feedback

BCI2000a general-purpose system for BCI research consisting of

configurable modules

Signal Acquisition

StimulusPresentation

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

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)

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

Participant 1 Participant 2 Participant 3

Channel

Frequency (1-70 Hz)

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

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

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

Thank you.