IpsiHand: An EEG-based Brain Computer Interface for Rehabilitation and Restoration of Hand
Control following Stroke and Traumatic Brain Injury Using Ipsilateral Cortical Physiology
Sam Fok, Raphael Schwartz, Mark Wronkiewicz, Charles Holmes, Jessica Zhang, Thane Somers, David Bundy, Dr. Eric Leuthardt
Washington University in St. Louis
Abstract
Methods
Results
Stroke and traumatic brain injury (TBI) cause long-term, unilateral
loss of motor control due to brain damage on the opposing
(contralateral) side of the body. Conventional therapies are
ineffective at restoring function in about half affected. Brain
computer interfaces (BCIs) show promise for rehabilitation but
remain
primarily
restricted
to
the
research
stage.
Furthermore, traditional BCIs cannot work if areas such as M1 are
damaged. We present a novel BCI, IpsiHand, which circumvents
signal sourcing issues in an injured brain as well as risk associated
with invasive recordings. IpsiHand uses electroencephalography
(EEG) to record novel motor intent signals and control a powered
hand orthosis, which allows the undamaged hemisphere to control
both hands. Through sensory and proprioceptive feedback and
neural plasticity, IpsiHand can strengthen ipsilateral neural pathways.
Synchronous neuronal firing over large areas of the cortex are
recorded from the scalp and processed onboard a laptop. The signal
is filtered to generate a control signal, which is then sent to a linear
actuator fitted to an orthosis controlling the patient’s finger closure.
IpsiHand was tested with three healthy subjects to verify the ability
to use non-conventional signals from cortex on one side of the
brain to control a hand on the same side of the body. We found that:
1. Hand movement correlates with ipsilateral signals.
2. IpsiHand can use EEG signals to move the hand.
Introduction
Stroke and TBI combined are the leading cause of disability in the
US, with around a million cases annually. Half report trouble with
hand movement, and conventional physical therapy produces little
improvement after 3 months post injury [1]. Therapies requiring the
patient to actively control their impaired limb are most likely to
induce reorganization of neural pathways and improving control but
require intensive interaction between the patient and practitioner [2].
BCIs promise new, more effective motor therapies. They are
traditionally applied in cases where central nervous signals are cut
off from their destination by injury. Electrical signals are recorded
from the brain to circumvent injuries and control devices to actuate
a target limb, which recouples intent to move and movement [3].
Despite promise, conventional BCIs cannot be applied to cases of
brain injury where damaged primary motor cortex contralateral to
the affected limb produces no signals. However, recent study found
distinct cortical physiology associated with ipsilateral, contralesional
hand and limb movement in regions distinct and separable from the
primary motor cortex [4]. These signals exist in cortex anterior to
ipsilateral primary motor cortex at frequencies below 40Hz [5]. We
used a non-invasive EEG consumer headset to record from cortex
and control an orthosis that opens and closes a subject’s hand. As
the least invasive recording technique, EEG is most practical for
immediate application in the clinic.
IpsiHand demonstrates the synthesis of neurophysiology,
consumer electronics, and signal processing to develop new devices
for more effective therapies. Implementation of this design
constitutes a fundamentally new approach to restoring function in
stroke and TBI survivors.
Online Performance Results
An Emotiv EPOCTM EEG headset records EEG signals from the
scalp with 14 channels. The headset aligns, bandpass filters, and
digitizes the signal at 128 Hz and transmits wirelessly to a laptop.
Signal Processing and Control
A Becker Oregon TalonTM prefabricated orthosis, designed to
couple wrist motion to hand closure, was fitted with a powered
linear actuator (Firgelli Miniature Linear Motion Series L16),
controlled through our LabView algorithm.
The orthosis size is adjustable, which is key in a clinical setting with a
variety of patients. To mechanically prevent hyperextension or
hyperflexion of the hand, the range of actuator motion is mapped
only onto the natural range of finger joint rotation.
96.2%
92.3%
86.5%
80.8%
Discussion
Signal Acquisition
Mechanical Orthosis
2.60
2.0
1.0
0.5
Classification accuracy using best threshold as determined by ROC
analysis per window length. In actual trials, through 10 sets of
trials with non-impaired individuals we achieved an 81.3% success
rate for the 1D cursor task and orthosis using a 0.5s window.
Note that this is slightly higher than predicted by ROC analysis.
Screening Results
EEG signals tend to be small and spatially diffuse compared to
invasively recorded signals, so maximizing the signal-to-noise ratio is
imperative to device performance. We used a large bi-polar reference
spatial filter to attenuate noise from wide areas of the scalp and
detect signals specific to a particular brain area. This also makes
IpsiHand resilient to electrode placement variations [6].
Signal processing was carried out in the BCI2000 framework, a
development platform that allows for rapid recording, filtering, and
feature selection of brain signals [2]. Initial screenings determine the
EEG features that our algorithm will use to contrast movement
from rest. During screening, the user alternates between periods of
attempted hand movement and periods of rest. With screening data,
we identified the specific electrode channels and frequency bins with
consistent changes in power spectrum between hand movement and
rest conditions. The power of the selected channel is normalized to
0 mean and unit variance using a buffer of previous trial data and
sent to LabView software for conversion into control signal.
In LabView, the signal is compared to a user defined threshold and
mapped to an actuator position command according to
𝑥𝑖 = 𝑥𝑖−1 + 𝑔𝑠 ′
where xi is the commanded position of the ith program iteration, g is
a user defined gain, and s’ is the thresholded signal.
Window Length (Seconds) Accuracy of Classification
Left: Correlation colormap between left hand movement and rest per
electrode and per frequency bin. Electrodes named according to the
10-20 EEG system(Right) [7]. Bins with high correlation are good
candidates for control signals. Clusters (dotted red circles) of high
correlation in ipsilateral cortex noted around the 12Hz bins in F3
through P7 and also in channel F3 around the 22Hz bin.
Window Length
— 2.6 Seconds
— 2.0 Seconds
— 1.0 Seconds
— 0.5 Seconds
---
Random Guess
Top: Left: Spatial map of L.hand vs. rest correlations per channel at
12Hz. Note correlations present across frontal cortex.
Top: Right: Spatial map of L.hand vs. rest correlations per channel at
22Hz. Note correlation only present unilaterally in channel F3.
Bottom: Left: Raw spectrum of channel F3 shows difference in power
around 12 and 22Hz bins during L.hand and rest conditions.
Bottom: Right: ROC curves shows classification performance using
varying window lengths and thresholds. Note that longer window
have higher performance but also increase the system latency.
The features identified during screening were used to modulate
orthosis closure and 1D cursor movement. The subject was tasked
with moving the cursor to a target randomly located at either the left
or right side of the screen (below).
Combining the discovery of signals in contralesional hemisphere,
electronics, and advances in rehabilitation, IpsiHand offers a new
rehabilitation option for stroke and TBI survivors. IpsiHand was
able to process EEG signals for real-time hand control with
accuracy consistent with previous studies [8] and theoretical ROC
analysis. Combining BCIs and orthotic devices induces neural
plasticity and improves motor function [8]. Furthermore, IpsiHand
therapy is unhampered by the severity of neural pathway injury by
circumventing the injury. Compared to other devices, IpsiHand
facilitates plasticity most directly, is cheaper and more portable.
Future Directions:
We plan improvements in portability and signal processing.
Portability Goals:
Currently, a laptop processes the EEG signals used for orthosis
movement. We plan to miniaturize the processing to a microcomputer for portability, allowing patients to even use the device
as a replacement for normal hand function in daily life.
Signal Processing Goals:
• Expand the system’s ability to adapt to spatially non-stationary
EEG signals. Possibilities include adaptive feature selection and
adaptive spatial reference selection.
• Remove muscular and other artifacts with spatial and temporal
filtering. This is essential for device performance outside of a
research setting. Artifact sources include eye blinks, EMG,
ECG, and breathing.
• Build real-time feature selection to eliminate need for screening
procedure and improve user friendliness.
• Improve control signal normalization and adaptation through
least mean squares algorithm and linear regression techniques.
Acknowledgements
The undergraduate authors would like to thank D. Bundy for providing technical support and
manuscript comments, Dr. E. Leuthardt for advice throughout the work, and Dr. A. Nehorai
for his support as well. This project supported in part by The National Collegiate Inventors
and Innovators Alliance, The Washington University School of Engineering, and Emotiv
Systems.
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