Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI

Abstract

Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in the framework of a so called "Hybrid-BCI" (hBCI) approach. Thereby, subjects could achieve a good control of their hBCI independently of their level of muscular fatigue. Furthermore, although EMG alone yields good performance, it is outperformed by the hybrid fusing of EEG and EMG. Two different fusion techniques are explored showing graceful performance degradation in the case of signal attenuation. Such a system allows a very reliable control and a smooth handover if the subjects get exhausted or fatigued during the day.

Multifunctional Neurodevice for Recognition of Electrophysiological Signals and Data Transmission in an Exoskeleton Construction

“…The merging of multiple modalities or switching between them in order to find those that better embodies the intention of a person not only increases the accuracy but also serves as an evidence of a potentially higher productivity of robotic equipment. The experimental results of the EEG and EMG signals hybridization confirmed the success of various research groups and the feasibility of different physiological signals merging [15,16,21]. A combination of multiple types of biological signals as the source of the control commands should be considered as promising.…”

“…With this in mind, a team of scientists has proposed the hybrid EEG-EMG system, and its effectiveness was assessed in 6 healthy volunteers [15]. The task for the volunteers was the performance of periodic motions by the left or the right hand (the command is executed on the color bar) for 5 s (total of 60 reps).…”

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

“…Physiological Signals Fusion Application ECG EMG EEG [32][33][34] Automatic seizure detection [35] hybrid-BCI [36] Epileptic event detection [37] Human movement intention [38] Automatic sleep stage classification [39] Gait intent detection [40,41] and this research Gender Identification for rehabilitation exercise Therefore, the bimodal physiological systems for automatic gender recognition were carried out in this study to develop the mechanism of physiotherapy for online monitoring. To guarantee the accurate signal of a single modality system, the fusion of two or more signals is significant to improve the performance of the system [39,[41][42][43][44][45][46][47][48].…”

Review of Brain Computer Interface Components and Corresponding Research Topics

“…In order to improve system performance, make robustness of system stronger, and make the classification results more accurate, combining two or more kinds of EEG signals [40] and integrating EMG [41] or EOG [39] which is regarded as interference in the past can be considered as an effective way to use the brain signal.…”