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Gumpy: a python toolbox suitable for hybrid brain-computer interfaces

Tayeb, Zied and Waniek, Nicolai and Fedjaev, Juri and Ghaboosi, Nejla and Rychly, Leonard and Widderich, Christian and Richter, Christoph and Braun, Jonas and Saveriano, Matteo and Cheng, Gordon and Conradt, Jorg (2018) Gumpy: a python toolbox suitable for hybrid brain-computer interfaces. Journal of Neural Engineering. Institute of Physics (IOP) Publishing. doi: 10.1088/1741-2552/aae186. ISSN 1741-2560.

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Official URL: https://doi.org/10.1088/1741-2552/aae186

Abstract

Objective. The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain-computer interface (BCI). Approach. Gumpy provides state-of-the-art algorithms and includes a rich selection of signal processing methods that have been employed by the BCI community over the last 20 years. In addition, a wide range of classification methods that span from classical machine learning algorithms to deep neural network models are provided. Gumpy can be used for both EEG and EMG biosignal analysis, visualization, real-time streaming and decoding. Results. The usage of the toolbox was demonstrated through two different offline example studies, namely movement prediction from EEG motor imagery, and the decoding of natural grasp movements with the applied finger forces from surface EMG (sEMG) signals. Additionally, gumpy was used for real-time control of a robot arm using steady-state visually evoked potentials (SSVEP) as well as for real-time prosthetic hand control using sEMG. Overall, obtained results with the gumpy toolbox are comparable or better than previously reported results on the same datasets. Significance. Gumpy is a free and open source software, which allows end-users to perform online hybrid BCIs and provides different techniques for processing and decoding of EEG and EMG signals. More importantly, the achieved results reveal that gumpy’s deep learning toolbox can match or outperform the state-of-the-art in terms of accuracy. This can therefore enable BCI researchers to develop more robust decoding algorithms using novel techniques and hence chart a route ahead for new BCI improvements.

Item URL in elib:https://elib.dlr.de/121742/
Document Type:Article
Title:Gumpy: a python toolbox suitable for hybrid brain-computer interfaces
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Tayeb, ZiedUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Waniek, NicolaiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fedjaev, JuriUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ghaboosi, NejlaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rychly, LeonardUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Widderich, ChristianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Richter, ChristophUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Braun, JonasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Saveriano, MatteoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Cheng, GordonUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Conradt, JorgUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2018
Journal or Publication Title:Journal of Neural Engineering
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1088/1741-2552/aae186
Publisher:Institute of Physics (IOP) Publishing
ISSN:1741-2560
Status:Published
Keywords:Hybrid Brain-Computer Interfaces, Python toolbox, Deep Learning, EEG, EMG
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Terrestrial Assistance Robotics (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Saveriano, Matteo
Deposited On:29 Nov 2018 15:50
Last Modified:13 Jun 2023 14:38

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