Application of Correlation in Detecting
Movement-related Potentials
Mehrdad Fatourechi
Department of Electrical and Computer Engineering, University of British Columbia,
Vancouver, BC, Canada V6T 1Z4, Email: mehrdadf@ece.ubc.ca
Abstract- Detecting movement-related potentials (MRPs) is an important research field in the analysis of brain
signals with applications in clinical neurophysiology as well as in developing brain computer interface (BCI)
systems. One of the challenges in detecting MRPs is that they are usually obscured in single trial analysis. On the
other hand, correlation is a powerful signal detection tool that enables us to detect a pattern embedded in noise,
given a template. In this project, we investigate the application of correlation with a template to detect MRPs.
1. Introduction
Many physiological disorders such as Amyotrophic Lateral Sclerosis (ALS) or injuries such
as high-level spinal cord injury can disrupt the communication path between the brain and the
body. People with severe motor disabilities may lose all voluntary muscle control, including eye
movements. These people are forced to accept a reduced quality of life, resulting in dependence
on caretakers and escalating social costs. Most of the existing assistive technology devices for
these patients are not possible because these devices are dependant on motor activities from
specific parts of the body. Over the last three decades, BCI has emerged as a new frontier in
assistive technology since it could provide an alternative communication channel between a
user’s brain and the outside world. A successful BCI design would enable people to control
objects in their environment (such as a light switch in their room or television, wheelchairs,
neural prosthesis and computers) by thought only. This could be accomplished by measuring
specific features of the user’s brain activity (EEG) that relate to his/her intent to perform the
control.
Movement- related Potentials (MRPs) are components of EEG signals that are time-locked to
a motor process. They have a characteristic pattern that is more or less reproducible under similar
experimental conditions. For this reason, MRPs are of great interest to many researchers seeking
knowledge about the functions of the brain. In fact, in the literature, two significant applications
make use of MRPs: diagnosing neurological disorders and development of BCI systems. Since
MRPs are time-locked to the onset of a movement attempt, they are known to be excellent
candidates for developing BCI systems [FAT06].
The main problem of MRP detection is that the amplitude of an MRP is much smaller than
that of the background EEG signal. This makes the detection process very hard in single trial
analysis. In the literature, several feature extraction have been implemented for efficient
detection of MRPs including parametric modeling, wavelet transform, and spectral parameters
(see [BAS07] for review).
Although correlation is an efficient technique to detect a known waveform embedded in
noise, it has not been explored widely in the BCI literature [BAS07]. In this project, we will
explore such application and examine how suitable correlation is in detecting MRPs.
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2. Method
Matched filtering (correlation) is the optimal method to detect a waveform of a known shape
that is embedded in white Gaussian noise. Since MRPs are time-locked to the movement onset,
several research papers have shown that averaging many single trials results in a less noisy
version of an MRP, i.e., a template. We postulate that by cross-correlating this template with
EEG signals, we can detect the presence of MRPs, as shown in Figure 1. The correlation will be
computed for two types of test EEG signals: one when an MRP is present and the other when no
movement has occurred (i.e., an MRP is not present). In order to show that correlation is an
efficient way for detecting MRPs, we use metrics such as Fisher’s discriminant ratio to show that
the distribution of MRP features is different from the distribution of the features of EEG signals
when MRP is not present.
Figure 1. How correlation with a template can result in the detection of MRPs
We also investigate the effect of two factors in the performance of the correlation machine:
First, we analyze the effect of low-pass filtering of EEG signals. Removing high-frequency noise
results in less noisy EEG signals and perhaps it improves the performance of our detection
algorithm. We also investigate the effect of number of single-trial EEG signals that are added
together to form the template. We will investigate how the number of trials affects the shape of
the template and ultimately the performance of our detection algorithm.
References
[BAS07] Bashashati, A., Fatourechi, M., Ward, R. K., and Birch, G. E., “Signal Processing Algorithms in
Brain Interfaces”, Journal of Neural Engineering, Vol. 4, No. 2, Jun 2007, pp. R35-57.
[FAT 06] Fatourechi, M., Bashashati, A., Birch, G.E. and Ward, R.K. “Automatic User Customization
for Improving the Performance of an Asynchronous Brain Interface System”, Journal of Medical &
Biological Engineering and Computing, Vol.44, No.12, Dec 2006, pp.1093-1104.
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