Introduction
People
with spinal cord injury (SCI) face significant lifestyle challenges. Loss of
independence due to the SCI can often be associated with depressive illness,
pain, high levels of anxiety, frequent hospitalisation, drug dependency and so
on (Craig & Hancock, 1998; Craig et al., 2002). They face severe
restrictions on their ability to perform basic activities of daily living such
as eating and drinking, and being wheelchair-bound restricts the user to
limited terrains. Functional Electrical Stimulation (FES) involves developing
technology that has the potential to help overcome some of these problems. For
example,
All
lower-body
The
status of development of lower extremity neuroprostheses
has been reviewed by Triolo & Bogie (1999) and
includes systems for standing, transfers, stepping, stair climbing/descending and
bladder control. There are many degrees of freedom involved in the control of
lower extremity
Brain-computer interfaces (BCIs)
BCIs
offer “hands-free” control by allowing the user to employ their brain signals
as a switch. The principle of BCI control is that a person can voluntarily
change, or learn to change, neuronal activity in their brain. The brain
activity generally used is the electroencephalogram (EEG) that is measured
using surface electrodes. Detection of a specific change is
then used to control some device. This general criterion has resulted in a wide
variety of different strategies for BCIs (Wolpaw et al., 2000). BCI development has mainly focused on
applications for people with little or no voluntary movement, including devices
for moving a cursor on a computer screen, selecting characters for writing, or
using an Environmental Control System (ECS; Craig et al., 2002).
When
choosing a BCI command system one must ensure that activation of the neuroprosthesis and movement of the limb do not adversely
affect command signals used by the BCI. However, signals employed by BCIs often originate from motor areas of the cortex, and
are affected by natural and passive movement of the extremities. Many BCIs use Mu (8-13 Hz) and Beta rhythms (13 Hz and above)
located over sensorimotor areas. The signals used by these BCIs
are reactive to movement preparation, execution, imagination and passive
movement of the limbs, and form a spatial pattern of reactivity corresponding
to the particular movement involved. Movement of the right hand for
example, whether executed, passive, or imagined, principally results in reduced
mu and beta-band power over the hand area of cortex on the left side of the
head, prior to and during movement. It is difficult, therefore, to
discriminate between mu and beta signals arising from movement and those
arising from motor imagery of the same movement. This means that motor imagery
of a particular limb cannot currently be used effectively as a command to
activate a neuroprosthesis fitted to the same limb. Toe and
feet movement and motor imagery have a predominant association with
sensorimotor rhythms near the vertex. These signals are more difficult
to detect on the scalp, probably due to the location of the cortical foot motor
area in the mesial cortex. These signals have potential application for
command of lower extremity neuroprostheses, provided
that movement of the paralysed limb does not affect mu and beta rhythms at the
vertex, which may be true for individuals with paraplegia involving complete
lesions.
Mind Switch BCI interface
The
Mind Switch BCI uses 8-13 Hz signals from the part of the cortex where
reactivity is greatest to eye-closure. When a person closes their eyes,
electrical activity within the 8-13 Hz frequency band is enhanced over the
cortex, predominantly over visual areas. This increase in alpha amplitude
measured is consistent with the hypothesis that alpha band activity is a
correlate of a cortical area at rest. Heasman et al.
(2002) recently reported a person with C5 tetraplegia using the Mind Switch to
control a hand grasp neuroprosthesis. The subject
toggled the open and closed state of the neuroprosthesis
by closing their eyes for an average of 2.1 seconds. The main limitation of the
Mind Switch is the necessity of closing the eyes to issue a command. However,
as discussed below, the Mind Switch is the only BCI demonstrated to operate in
an asynchronous control environment, which is an important requirement for neuroprosthetic applications.
BCIs can operate in continuous, synchronous, or asynchronous control environments. Those that operate in continuous control environments require the BCI to generate commands continuously to update the state of a device. This control has the limitation that the person must constantly attend to the task. In a synchronous control environment, stimuli are presented, say every 4.5 – 8 seconds, to indicate time windows when the user’s response is measured. Synchronous control environments require the user to continuously produce appropriate responses in each time window, and allow command generation only during these periods. These environments are useful for communication applications where the BCI is used repeatedly, for example to choose letters for writing, but are less suitable for neuroprosthetic applications. A neuroprosthesis requires a command interface that can issue commands asynchronously, at a time of the user’s choosing. To accomplish this a BCI must be able to detect specific brain activity at any time a command is intended, and disregard all other brain activity that arises when the user is performing other tasks. This is a much more difficult detection problem than encountered by BCIs in continuous and synchronous control environments. The signal used by the Mind Switch exhibits a much higher signal-to-noise ratio than other BCIs, and allows the interface to be used for periods exceeding 15 minutes without the generation of unintended commands.
Future directions of the Mind Switch
Our current research efforts are seeking to develop asynchronous BCI with multiple switches by combining the Mind Switch with alternative switches. For example, one potential combination is with the control of mu activity at the vertex, reactive to motor imagery of the feet. In this arrangement a user activates the Mind Switch and then immediately imagines relaxing or moving the feet. The Mind Switch acts as the asynchronous switch, and the high or low level of mu activity indicates the BCI output. This BCI could potentially be used to toggle the state of two devices, such as the open/closed states of the left and right hands in a bilateral hand grasp neuroprosthesis.
The impact of artifact
remains a challenge. The performance of BCIs is
usually assessed in an environment where the user remains still and relaxed.
These conditions are unrealistic in neuroprosthetic
applications, and techniques to remove cable noise, muscular artifact, and ocular artifact
from the EEG need to be incorporated to ensure adequate performance. Artifact in the EEG will also arise from
For convenience, BCI development is usually conducted with able-bodied subjects. In order to optimize performance for people with SCI we need to know the impact the injury has on brain signals used by BCIs. We have recently investigated the impact of SCI on the EEG during eye-closure. Sign tests showed that people with SCI, compared to able-bodied individuals, have consistently lower activity in the alpha frequency band recorded from 14 out of 15 electrode sites. Furthermore, the group with tetraplegia had significantly lower (p < 0.5) alpha band magnitudes than the group with paraplegia, at central and posterior sites (74). These results will help to optimize electrode placement in the Mind Switch BCI for people with SCI.
Conclusion
Neuroprostheses require command interfaces where the user can switch the state of a system, or supply graded commands to control the device along one or more degrees of freedom. The user must be able to supply these commands asynchronously, at a time of their choosing, and the operation of the neuroprosthesis must not interfere with the user’s ability to generate commands. Current command interfaces allow neuroprostheses to be operated to perform some activities of daily living, but often limit the potential functionality that can be obtained from the device. BCIs provide a means of generating commands “hands-free”, and therefore offer the potential to overcome some of the limitations of existing command interfaces. Many signals used by BCIs are affected by extremity movement, and are unlikely to be suitable for neuroprostheses. Mind Switch is less affected by extremity movement and is therefore likely to offer suitable command signals. Furthermore, Mind Switch is presently one of the few BCIs that can potentially provide asynchronous commands for neuroprostheses. However, it does require users to momentarily draw their visual attention away from the task at hand.
Unfortunately, neuroprosthetic
applications increase the likelihood of artifact in
the EEG due to movement of the prosthetic limb, and increased muscle and eye
activity. Stimulation currents from the neuroprosthesis
are also likely to contaminate signals used by BCIs.
Studies are needed to quantify these artifacts, so
that appropriate signal processing strategies can be adopted to reduce the
impact of these signals on BCI performance. Lastly, there is little data
available concerning the deafferentation impact of SCI on the EEG. As many BCI
have been developed using able-bodied subjects, it will be important to
understand how SCI affects brain activity in order to optimize BCI –
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Aknowledgements. This paper was supported by an ARC Linkage Grant