A study of action difference on motor imagery based on delayed matching posture task

Abstract

Objective. Motor imagery (MI)-based brain-computer interfaces (BCI) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively. Approach. To explore action differences, this study proposes a novel paradigm named as Action Observation based Delayed Matching Posture Task (AO-DMPT). Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference. Main results. Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p<0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28μV of grasping, 5.64μV and 5.25μV higher than the cutting and the writing, respectively (p<0.05). Significance. The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further

Personalized motor imagery prediction model based on individual difference of ERP

“…Halder et al showed a commonality in the neural mechanisms of training between the ERP and MI BCI paradigms [38]. Similarly, our previous study also showed a positive correlation between the ability of the supplement motor area (SMA) region to induce ERPs and that of MI [17,39]. The proposed AO-Multi-DMPT paradigm utilizes this commonality to establish a correlation between ERP and MI.…”

“…This is due to the overlapping activation in the supplementary motor area and putamen between the ERP task and the MI task [38]. Similarly, our previous study also showed a positive correlation between the ability of the SMA region to induce ERPs and that of MI [17,39]. Based on this correlation demonstrated in our previous study, the current study uses a machine learning model (LR) to learn the correlation between ERP and MI-ACC to build a personalized MI prediction model.…”

“…A correlation between event-related potential (ERP) and MI has been demonstrated in our previous study by an action observation-based delayed matching posture task (AO-DMPT) paradigm [17]. That is, ERP and MI have a positive correlation, and both are improved under a suitable action.…”

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights

“…The maximum of the beta ERS coincides with the reduced excitability of motor cortex neurons [59]. Results in [60] demonstrated the ERD/ERS difference was related to action difference. Common ERD/ERS visualization methods include the band power method [61] and event-related spectral perturbation [62].…”

Preparatory movement state enhances premovement EEG representations for brain–computer interfaces

“…On the other hand, to address the issue of BCI illiteracy, many researchers have conducted studies on paradigm designs. For example, they have studied the inclusion of corresponding videos during the MI process to enhance users' ability to imagine movements [21,22]. While MI-BCI effectively conveys users' motor intentions, the generation of such signals requires a considerable amount of time (3 s-4 s), which can result in a reduction of system efficiency.…”