Empirical mode decomposition use in electroencephalography signal analysis for detection of starting and stopping intentions during gait cycle

Electroencephalography signals can be used to detect start and stop times of gait. This is useful for people who have lost or present serial low limb motor difficulties in order to work in conjunction with an exoskeleton. Normally, the frequency bands that are used to detect the gait or stop intentions are related to mu and beta frequency bands. However, in order to enhance the electroencephalography signal quality, it is necessary to increase the signal-to-noise ratio. In the paper, a former research is complemented with the use of different types of frequency and spatial filters. A multi resolution analysis tool based on Hilbert-Huang transform is also introduced as a new processing tool and its results discussed with the help of a recent developed comparison index.