Characterizing Motor System to Improve Training Protocols Used in Brain-Machine Interfaces Based on Motor Imagery

Motor imagery (MI)-based brain-machine interface (BMI) is a technology under development that actively modifies users’ perception and cognition through mental tasks, so as to decode their intentions from their neural oscillations, and thereby bringing some kind of activation. So far, MI as control task in BMIs has been seen as a skill that must be acquired, but neither user conditions nor controlled learning conditions have been taken into account. As motor system is a complex mechanism trained along lifetime, and MI-based BMI attempts to decode motor intentions from neural oscillations in order to put a device into action, motor mechanisms should be considered when prototyping BMI systems. It is hypothesized that the best way to acquire MI skills is following the same rules humans obey to move around the world. On this basis, new training paradigms consisting of ecological environments, identification of control tasks according to the ecological environment, transparent mapping, and multisensory feedback are proposed in this chapter. These new MI training paradigms take advantages of previous knowledge of users and facilitate the generation of mental image due to the automatic development of sensory predictions and motor behavior patterns in the brain. Furthermore, the effectuation of MI as an actual movement would make users feel that their mental images are being executed, and the resulting sensory feedback may allow forward model readjusting the imaginary movement in course

Implementation of a Motor Imagery Based BCI System Using Python Programming Language

At present, there is a wide variety of free open-source brain-computer interface (BCI) software. Even though the available software is very complete, it often runs under a Matlab environment. Matlab is a high performance language for scientific computing, but its limitations concerning the license cost, the restricted access to the algorithm code, and the portability difficulties complicates its use. Therefore, we proposed to implement a motor imagery (MI) based BCI system using Python programming language. This system was called miBCI software, was designed to discriminate up to three control tasks and was structured on the basis of online and offline data analyses. The functionality and efficiency of the software were firstly assessed in a pilot study, and then, its applicability and utility were demonstrated in two subsequent studies associated with the external and internal influences on MI-related control tasks. Results of the pilot study and preliminary outcomes of the subsequent studies are herein presented. This work contributes by promoting the utilization of tools which facilitate the advance of BCI research. The advantage of using Python instead of Matlab, which is the widely used programming language at the moment, is the opportunity to develop BCI software in a public and collaborative way, without property license restrictions

Development of a simulated living-environment platform: design of BCI assistive software and modeling of a virtual dwelling place

In brain-computer interfaces (BCIs), the user mental constrains and the cognitive workload involved are frequently overlooked. These factors are aggravated by neuromuscular dysfunction, collateral complications, and side effects of medication in motor-impaired people. We therefore proposed to develop a simulated living-environment platform (SLEP) that was tailored to severely paralyzed people and also allowed the progressive user-system adaptation through increasingly demanding scenarios. This platform consisted of a synchronous motor imagery based BCI system, an everyday assistive computer program, and a virtual dwelling place. The SLEP was tested in 11 healthy users, where the user-system adaptation was evaluated according to the BCI accuracy for classifying the user control tasks. The user heart rate was also incorporated in the evaluation in order to verify the progressiveness of such adaptation. The results of this study showed that user performance tended to increase from the least to the most challenging scenario in learning situations. The results also showed that nine of the eleven users controlled the BCI system in cue-driven mode, completing over half of the tasks. Two of the eleven users controlled the BCI system in target-driven mode, completing two tasks. Taken together, these results suggest that the progressive adaptation in BCI systems can enhance the performance, the persistence and the confidence of the users, even when they are immersed in simulated daily-living situations. © 2013 Elsevier Ltd. All rights reserved