System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation

Delisle-Rodríguez, Denis; Cardoso, Vivianne; Gurve, Dharmendra; Loterio, Flávia Aparecida; Romero-Laiseca, Maria Alejandra; Krishnan, Sridhar; Bastos, Teodiano

doi:10.1088/1741-2552/ab08c8

Cited by 40 publications

(35 citation statements)

References 86 publications

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“…The experimental protocol from the present study will be suitable for BCI-based exoskeleton systems for rehabilitating patients with motor disorders of the lower limbs in future studies. This has been supported by the previous studies as the signals during MI have been found to facilitate the operation of lower limb exoskeleton when the classification accuracy of MI patterns, and even the motor intention, was high [54], [55].…”

Section: Feasibility Study and Applicationssupporting

confidence: 76%

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

et al. 2020

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Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain's intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for the actions of standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the transitioning actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and classifier testing analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73±2.54% in the stand-to-sit transition. By applying the classifier testing analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.

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Section: Feasibility Study and Applicationssupporting

confidence: 76%

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

et al. 2020

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“…The feasibility of inducing neurological recovery in paraplegic patients by long term training with a BCI-based gait protocol was shown in [5]. In addition, BCI-based control of virtual object [6], robotic arm [7][8][9], robotic prosthetic [10,11], wheelchair [12], and various rehabilitation devices [13][14][15][16] were also reported in previous research.…”

Section: Introductionmentioning

confidence: 81%

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

Bao-guo

et al. 2020

Electronics

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Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform teleoperation for reach and grasp task still has many difficulties, especially in continuous multidimensional control of robot and tactile feedback. In this research, a motor imagery EEG-based continuous teleoperation robot control system with tactile feedback was proposed. Firstly, mental imagination of different hand movements was translated into continuous command to control the remote robotic arm to reach the hover area of the target through a wireless local area network (LAN). Then, the robotic arm automatically completed the task of grasping the target. Meanwhile, the tactile information of remote robotic gripper was detected and converted to the feedback command. Finally, the vibrotactile stimulus was supplied to users to improve their telepresence. Experimental results demonstrate the feasibility of using the motor imagery EEG acquired by wireless portable equipment to realize the continuous teleoperation robot control system to finish the reach and grasp task. The average two-dimensional continuous control success rates for online Task 1 and Task 2 of the six subjects were 78.0% ± 6.1% and 66.2% ± 6.0%, respectively. Furthermore, compared with the traditional EEG triggered robot control using the predefined trajectory, the continuous fully two-dimensional control can not only improve the teleoperation robot system’s efficiency but also give the subject a more natural control which is critical to human–machine interaction (HMI). In addition, vibrotactile stimulus can improve the operator’s telepresence and task performance.

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“…The analysis of single events in the EEG, in turn, is relatively rarely used, especially at short time intervals [25,51]. Comparative analysis has shown [52][53][54] that by single implementations of short (up to 500 ms) EEG segments, it is possible to identify the type of visual stimulus that indicates, for example, the direction of movement and the areas of the brain involved in its processing. In addition, even shorter EEG epochs (up to 200 ms) may be sufficient in identifying the readiness potential, indicating the lateralization of the upcoming motor act.…”

Section: Discussionmentioning

confidence: 99%

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

et al. 2022

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A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.

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System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation

Cited by 40 publications

References 86 publications

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

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