An empirical study to remove noise from single-trial MRCP for movement intention detection

Abstract

In recent years, movement related cortical potentials (MRCP), a type of slow cortical potentials, have been used for motor intention detection for triggering external devices in close loop rehabilitation paradigm. One of the main issues with these slow frequency MRCP signals is to separate them from the background brain activity or their poor signal to noise ratio. For this reason, different filter bank for denoising are reported in the literature. In this paper, we use a signal adaptive, time-frequency based empirical method known as empirical mode decomposition to decompose the MRCP signal into its constituent frequency components called intrinsic mode function (IMF). By doing so, we can separate the slow frequency MRCP signal occupying the last few IMFs, from the high frequency noise present in the first few IMFs. By removing the first few IMFs, we obtain a cleaned MRCP signal that can be used for the detection of movement intention with much higher accuracies and less latency. Template matching approach of matched filters was used to detect the presence of signal and the results show that four different types of movement intentions can be detected using this denoising approach with an average latency of 0.70 s±0.1 before the actual movement onset. These results are much better than average latency of 0.07 s ± 0.1 reported previously in the literature on the same dataset.