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1MPI for Biological Cybernetics

2Stanford University3Werner Reichardt Centre for Integrative Neuroscience

Eberhard Karls University Tuebingen

Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

M. Gomez-Rodriguez1,2 M. Grosse-Wentrup1

J. Peters1 G. Naros3 J. Hill1 B. Schölkopf1 A. Gharabaghi3

ICPR Workshop in Brain Decoding, August 2010

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BCI for Neurorehabilitation

A Brain Computer Interface (BCI) for neurorehabilitation of:Hemiparetic syndromes due to cerebrovascular, traumatic or tumor-related brain damage.

may outperform traditional therapy → Hebbian rule-based [1]

1. Instantaneous feedback:Synchronize subject’s attempt and feedback

2. High accuracy:User’s control of the BCI

3. High specificity:To focus on specific areas of the cortex

4. Limited invasiveness

Challenges

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EECoG for Neurorehabilitation

BCI with Epidural Electrocorticography (EECoG) may be a promising tool for neurorehabilitation.

Instantaneous feedback: Delays in the order of ms.

Why EECoG for neurorehabilitation?

High accuracy: On-line decoding of arm movement intention.High specificity: Greater spatial resolution over motor cortex.

Limited invasiveness: Safer alternative to intraparenchymal electrodes or subdural devices.

Previous studies [2, 3]: Off-line real movement and motor imagery classification and real movement direction decoding using subdural ECoG with epileptic patients.

In our work:On-line movement attempt classification using epidural ECoG with a stroke patient.

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Outline

We present a Case Study (one patient) of a BCI with Epidural Electrocorticography (EECoG):

1. Experimental Setup:Human subject, task and recording

2. Methods:Signal processing and on-line decoding techniques

3. Results:Spatial and frequency features and classification performance

4. Conclusions

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Experimental Setup

Recording: 96 epidural ECoG platinum electrodes: somato-sensory, motor and pre-motor cortex.

8 x 12-electrode grid, 4-mm diameter pad (2.3 mm exposed), 5-mm interelectrode distance.

Human subject: 65-year old male, right-sided hemiparesis (hemorrhagic stroke in left thalamus).

Subject’s task: attempt to move the right arm forward (extension) or backward (flexion).

Training & test phase in each run, 30 trials per run, 5-s movement + 3-s rest per trial

Limited invasiveness!

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Methods (Signal Processing)

Common-Average Reference (CAR), band-pass filtering (2-115Hz) & notch filtering (50 Hz power-line).

Average power spectral densities over 2Hz frequency bins for each electrode are used as features. Welch’s method over overlapping incrementally bigger time

segments each 5-s movement or 3-s resting periods.

Larger segments → Less noise and more reliable estimates. Shorter segments → Necessary for on-line feedback.

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Methods (On-line Decoding)

On-line classification (every 300ms) between movement and resting using spectral features:

Visual on-line feedback is provided

A linear support vector machine (SVM) is generated on-line after a training period (15 seconds of each condition)

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Results (Spatial and Spectral Features)

Discriminative power of each electrode & frequency bin:

Operating Characteristic Curve (AUC), 96 electrodes, (2-115) Hz Classifier weights, 35 electrodes, (2-80)

Hz

Low freqs (<40 Hz): Power decreases for movement attempt

High freqs (>40 Hz): Power increases for movement attempt

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Results (Spatial and Spectral Features)

To see discriminative power of each spatial location better:Average AUC for each electrode over:

Low freqs (2 - 40 Hz) High freqs (40 - 80 Hz)

High Specificity!

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Results (Performance)

On-line decoding of movement intention based on different frequency bands: (2, 40), (40, 80) and (2, 80) Hz

High Accuracy!

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Conclusions

We showed the feasibility of epidural ECoG for BCI-based rehabilitation devices for hemiparetic patients

Future work (in progress): EECoG on-line decoding + haptic feedback provided by a robot arm guiding

patient’s arm: See already workshop paper at SMC’10 [4] on robot-based haptic feedback using EEG on-line

decoding on healthy subjects.

Instantaneous feedback: delay of 300 ms.

High accuracy: > 85 % in arm movement intention decoding.High specificity: Cortical reorganization caused by the stroke.

Limited invasiveness

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References

[1] T. H. Murphy, and D. Corbett. Plasticity during stroke recovery: from synapse to behaviour. Nature Review Neuroscience 2009, 10-12, 861-872.

[2] T. Pistohl, T. Ball, A. Schulze-Bonhage, A. Aertsen, and C. Mehring. Prediction of arm movement trajectories from ECoG-recordings in humans. Journal of Neuroscience Methods 2008, Vol. 167-1, 105-114

[3] G. Schalk, J. Kubanek, K.J. Miller, N.R. Anderson, E.C. Leuthardt, J.G. Ojemann, D. Limbrick, D. Moran, L.A. Gerhardt, and J.R. Wolpaw. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. Journal of Neural Engineering 2007, 4-264.

[4] M. Gomez-Rodriguez, J. Peters, J. Hill, B. Schölkopf, A. Gharabaghi, and M. Grosse-Wentrup. Closing the Sensorimotor Loop: Haptic Feedback Facilitates Decoding of Arm Movement Imagery. SMC Workshop in Shared-Control for BMI, 2010.