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

• Published in: Journal of neural engineering Vol. 15; no. 6; p. 65003
• Main Authors: Tayeb, Zied, Waniek, Nicolai, Fedjaev, Juri, Ghaboosi, Nejla, Rychly, Leonard, Widderich, Christian, Richter, Christoph, Braun, Jonas, Saveriano, Matteo, Cheng, Gordon, Conradt, Jörg
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.12.2018
• Subjects: deep learning; EEG; EMG; hybrid brain-computer interfaces; Python; Hybrid Brain-Computer Interfaces; EEG; Deep Learning; EMG; Python
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2552/aae186

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.