
Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding
Author(s) -
Nsugbe Ejay,
William Samuel Oluwarotimi,
Asogbon Mojisola Grace,
Li Guanglin
Publication year - 2021
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12039
Subject(s) - computer science , artificial intelligence , signal (programming language) , signal processing , pattern recognition (psychology) , decoding methods , redundancy (engineering) , wavelet , speech recognition , computer vision , algorithm , digital signal processing , computer hardware , programming language , operating system
In signal processing, multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels, where the optimal level within the signal minimises redundancy, uncertainties, and contains the information required for the characterisation of the sensed phenomena. In the area of physiological signal processing for prosthesis control, scenarios where a signal decomposition analysis are required: the wavelet decomposition (WD) has been seen to be the favoured time‐frequency approach for the decomposition of non‐stationary signals. From a research perspective, the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification. Despite this, there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity. Here, for neuromuscular (electromyography) and brainwave (electroencephalography) signals acquired from a transhumeral amputee, a computationally efficient time domain signal decomposition method based on a series of heuristics was applied to process the acquired signals before feature extraction. The results showed an improvement in motion intent decoding prowess for the proposed time‐domain‐based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.