Steps toward EEG-based prosthetic control
Scott Makeig, Tzyy-Ping Jung and Terrence J Sejnowski
To build a neuroprosthetic based on voluntary control of EEG signals,
several steps are necessary. First, the spatiotemporal information
in spontaneous and/or evoked EEG data must be separated from the
data. For this purpose, independent component analysis (ICA) is
very promising. The event-related spectral perturbations (ERSPs) of
independent components of the EEG show clear linkages between
cognitive and motor events and changes in the amplitudes and phase
dynamics of synchronous brain oscillations at multiple EEG frequencies.
For example, we have found that the suppression of the mu rhythm
generated in and near the motor cortex prior to an anticipated hand
movement (currently used by some groups to effect prosthetic control)
is 4-6 dB larger after ICA separation than in any single channel
derivation
(Makeig et al.,
IEEE Trans Rehab Eng 8:208-11, 2000. (.pdf, 111k)).
Next, the
degrees of learned voluntary control over each of the independent
EEG components must be evaluated as well as the degrees of freedom
this control can achieve. With this knowledge, suitable robotic
prostheses may be designed to take advantage of the learned control
for useful purposes by paralyzed individuals. Other applications
include predicting the responsiveness of operators of complex systems
to imperative stimuli (Jung et al., 1996).
Should we expect convergence between concurrent EEG and BOLD data?
Scott Makeig
I will consider the basis for expecting conjunction or disjunction
between the results of concurrent fMRI and EEG neuroimaging, and will
illustrate the application of ICA to continuous EEG data decomposition.
While much effort has been expended to find common areas of activation
for (a) particular EEG features of event-related potential (ERP)
averages and for (b) areas active in fMRI experiments, recording concurrent
EEG and fMRI data provide a much richer basis for exploration of what kind
of "neural" activity is correlated with fMRI BOLD signal changes. I will
give a simple argument from first principles suggesting there need be
no linkage at all between brain sources of ERP and EEG features,
on one hand, and fMRI BOLD signal changes on the other. Concurrent EEG
and fMRI data collection and analysis presents several challenges.
I will illustrate the usefulness of ICA combined with single-trial
visualization methods to recover event-related EEG brain
dynamics during continuous high-rate fMRI data acquisition.
"Algorithms, technologies and neural representations
for neuroprosthetics and neurorobotics."
Institute for Neural Computation,
University of California San Diego, La Jolla CA.
and The Salk Institute, La Jolla CA 92037
scott@salk.edu
"Explorative analysis and data modelling in functional neuroimaging:
Arriving at, not starting with a hypothesis!"
Computational Neurobiology Lab, Salk Institute, La Jolla, CA
Institute for Neural Computation, University of California San Diego, La Jolla CA
scott@salk.edu