Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity

Abstract

Recently, studies on cycling-based brain–computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

“…The 10-20 international system was used for positioning the EEG electrodes, and 250 Hz was used as the sampling frequency. Considering the primary region where ERD/ERS occurs during lower-limb movement in connection to lower-limb cycling tasks, the following EEG channels were recorded: F 3 , F 4 , C 3 , C Z , C 4 , P 3 , P Z , and P 4 , which are presented in figure 1 [18,19,28]. The minibike WCT fitness was used to execute the pedaling actions, which has been reported in previous studies as a tool that can be used for active pedaling tasks in a comfortable, portable, and effective manner [5].…”

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

“…UAVs present numerous challenges in terms of their many degrees of freedom and difficulty in mastering control, even when using a manual remote controller. Pedaling machines are typically used in stroke rehabilitation studies [ 31 ], which constitute a smaller niche of research. Finally, prosthetics are generally aimed at subjects who have amputations but generally have reasonable muscle signals in the residual (stump) limb, which has led to the development of numerous reliable EMG devices [ 32 ].…”

“…The majority of the papers we reviewed obtained the control signal directly from the output of the BCI classifier [ 2 , 28 , 31 , 33 , 37 , 43 , 46 , 48 , 50 , 56 , 59 , 60 , 61 , 63 , 66 , 72 ]. For asynchronous systems, this generally involved windowing the EEG data and obtaining a classification output at a regular rate [ 2 , 28 , 33 , 37 , 43 , 46 , 48 , 50 , 56 , 59 , 60 , 61 , 63 , 66 , 71 , 72 ].…”

“…Riemannian-geometry-based features are an emerging approach that was applied in two rehabilitative pedaling-machine BCIs [ 31 , 56 ]. They were used due to their low computational cost and strong classification performance [ 56 ].…”

Sensors Technology for Medical Robotics

“…This is shown in the paper written by De la Torre et al [ 9 ], where a literature review of works related to the use of robotics in upper limb rehabilitation is presented. On the other hand, Cardoso et al [ 10 ] show the use of brain–computer interfaces (BCI) as a support to lower limb rehabilitation with a passive pedaling device. The authors show an experiment in which the information provided by the BCI is used when volunteers perform the imaginative movement of pedaling.…”