Abstract:
Stroke is the main cause of motor disabilities in the human beings regardless of their ages. For
the rehabilitation of the stroke patients a method must be developed so that they can live an
independent life. Previous studies have shown that by using different classifiers some methods
are developed which can detect the movement intention of the patient. Previous studies on
analyzing the detection of movement intention of different classifiers, such as Linear
Discriminant Analysis (LDA), Support Vector Machines (SVM), employed detection based on
decomposed data set. In thus study, we detected the movement intention using the entire data set.
We used raw EEG signals of different subjects, and they were asked to imagine movement after
some specified time. This movement generated the brain response, called movement-related
brain potentials (MRCPs), which had a specific pattern. The recorded brain response was noisy
and hidden within the EEG signal of the subjects. We developed a method in which we used
matched filter combined with Neyman-Pearson detector to detect the movement intention. The
matched filter was applied both in time and frequency domain. The True positive rate of 73%
and 68% for local and global detector was achieved, respectively. This study addresses the
problem of detecting the movement intention accurately, with limited latency. It was also
concluded that matched filtering in time domain was faster as the impulse response of the filter
was small.
