Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle

Abstract

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The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability. Methods are compared with the Weighted Discriminator, a recently developed comparison index. The tool developed can improve the rehabilitation process associated with lower limb exoskeletons with the help of a Brain-Machine Interface.

Keywords

• brain-machine interface
• EEG analysis
• fast fourier transform
• gait intention
• Hilbert-Huang transform
• Stockwell transform