Deep learning based adaptive filtering technique for spectral–temporally modulated channeled spectropolarimetry

2021 ◽  
Author(s):  
Qiwei Li ◽  
Jiawei Song ◽  
Andrey S. Alenin ◽  
J. Scott Tyo
Keyword(s):  
Deep Learning ◽  
Adaptive Filtering ◽  
Filtering Technique

scholarly journals Active proportional electromyogram controlled functional electrical stimulation system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bethel A. C. Osuagwu ◽  
Emily Whicher ◽  
Rebecca Shirley
Keyword(s):  
Electrical Stimulation ◽  
Adaptive Filtering ◽  
Functional Electrical Stimulation ◽  
Comb Filter ◽  
Online Application ◽  
Present Technique ◽  
Filtering Technique ◽  
Stimulation System ◽  
Filter Approach ◽  
Intentional Movement

AbstractNeurophysiological theories and past studies suggest that intention driven functional electrical stimulation (FES) could be effective in motor neurorehabilitation. Proportional control of FES using voluntary EMG may be used for this purpose. Electrical artefact contamination of voluntary electromyogram (EMG) during FES application makes the technique difficult to implement. Previous attempts to date either poorly extract the voluntary EMG from the artefacts, require a special hardware or are unsuitable for online application. Here we show an implementation of an entirely software-based solution that resolves the current problems in real-time using an adaptive filtering technique with an optional comb filter to extract voluntary EMG from muscles under FES. We demonstrated that unlike the classic comb filter approach, the signal extracted with the present technique was coherent with its noise-free version. Active FES, the resulting EMG-FES system was validated in a typical use case among fifteen patients with tetraplegia. Results showed that FES intensity modulated by the Active FES system was proportional to intentional movement. The Active FES system may inspire further research in neurorehabilitation and assistive technology.

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