The Sensitivity of Single-Trial Mu-Suppression Detection for Motor Imagery Performance as Compared to Motor Execution and Motor Observation Performance.

Kunyu Xu,Jeng-Ren Duann,Yu-Yu Huang

doi:10.3389/fnhum.2019.00302

Abstract

Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.

Highlights

A single independent component analysis (ICA) decomposition was applied to individual data with all the three motor conditions combined, the common right-mu and left-mu-components for all motor conditions could be identified for each participant as shown in Figures 2, 3, respectively
The results from a non-parametric Kruskal–Wallis test showed that there was no significant difference in success rate across different window sizes in the Motor imagery (MI) (H(6) = 3.116, p = 0.794) and the motor observation (MO) condition (H(6) = 4.888, p = 0.558) condition
We compared the success rate under the individualized latency reflecting the best performance of each participant, the success rate under the generalized latency at 635 ms after the motor cue onset that was determined by the maximum mu-suppression in the grand average time-frequency plot from all motor conditions and all participants, and the average success rate across the 150-ms windows at all latencies along the alpha power time course derived from the contralateral mu-components

Summary

INTRODUCTION

Motor imagery (MI), rehearsing a motor action without actual movement, has been reported to involve similar brain networks, such as the motor cortex, premotor cortex, supplementary motor area, and prefrontal cortex, as reflected in motor execution (ME; Guillot and Collet, 2005; Lotze and Halsband, 2006; Guillot et al, 2007; Munzert et al, 2009; Collet et al, 2011; Westlake and Nagarajan, 2011; Hétu et al, 2013; Bajaj et al, 2014, 2015; Gallivan and Culham, 2015; Jiang et al, 2015; Ridderinkhof and Brass, 2015; Saiote et al, 2016; O’Shea and Moran, 2017). Duann and Chiou (2016) applied ICA to extract the common motor-related independent EEG components for the MI, ME, and MO conditions across participants. Significant improvement could be achieved by individualizing the window latency for mu-suppression detection

Participants

RESULTS

DISCUSSION

ETHICS STATEMENT