Effects of Task Complexity on Motor Imagery-Based Brain–Computer Interface

Mashat, M. Ebrahim M.; Lin, Chin‐Teng; Zhang, Dingguo

doi:10.1109/tnsre.2019.2936987

Cited by 24 publications

(15 citation statements)

References 40 publications

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“…Similarly, a previous study also suggested that motor imagery of a complex task resulted in better motor learning in healthy adults (Allami et al, 2008). More recently, (Mashat et al, 2019) examined the effects of task complexity on motor imagery function using the brain-computer interface.…”

Section: Discussionmentioning

confidence: 81%

“…Similarly, a previous study also suggested that motor imagery of a complex task resulted in better motor learning in healthy adults (Allami et al., 2008 ). More recently, (Mashat et al., 2019 ) examined the effects of task complexity on motor imagery function using the brain–computer interface. They concluded that the classification accuracy of the brain–computer interfaces for a complex motor imagery task was 7.3% higher than the simple task.…”

Section: Discussionmentioning

confidence: 99%

“…They concluded that the classification accuracy of the brain–computer interfaces for a complex motor imagery task was 7.3% higher than the simple task. Researchers believed that a complex task needs a specific control of the limb muscles and proper motor planning, which require the participation of a large number of peripheral nerves (Mashat et al., 2019 ). Consequently, this might affect the location and the intensity of activation in the brain (Alahmadi et al., 2016 ).…”

Section: Discussionmentioning

confidence: 99%

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Effects of task complexity or rate of motor imagery on motor learning in healthy young adults

Heena

Zia²,

Sehgal

et al. 2021

Brain and Behavior

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Background: A growing body of evidence suggests the benefit of motor imagery in motor learning. While some studies tried to look at the effect of isolated mental practice, others evaluated the combined effect of motor imagery and physical practice in clinical rehabilitation. This study aimed to investigate the effects of task complexity or rates of motor imagery on motor learning in health young adults.Methods: Eighty-eight healthy individuals participated in this study. Participants were randomly allocated to either Group A (50% complex, N = 22), Group B (75% complex, N = 22), Group C (50% simple, N = 22), or Group D (75% simple, N = 22).Participants in the complex groups performed their task with nondominant hand and those in simple groups with a dominant hand. All participants performed a task that involved reach, grasp, and release tasks. The performance of the four groups was examined in the acquisition and retention phase. The main outcome measure was the movement time.Results: There were significant differences between immediate (i.e., acquisition) and late (i.e., retention) movement times at all three stages of task (i.e., MT 1 [reaching time], MT 2 [target transport time], and TMT [reaching time plus object transport time]) when individuals performed complex task with 75% imagery rate (p < .05).Similarly, there were significant differences between immediate and late movement times at all stages of task except the MT 2 when individuals performed simple task with 75% imagery rate (p < .05). There were significant effects of task complexity (simple vs. complex tasks) on immediate movement time at the first stage of task (i.e., MT 1 ) and late movement times of all three stages of task (p < .05). There were significant effects of the rate of imagery (50% vs. 75%) on late movement times at all three stages of tasks (p > .05). Additionally, there were no interaction effects of either task complexity or rate of imagery on both immediate and late movement times at all three stages of tasks (p > .05). Conclusion:This study supports the use of higher rates (75%) of motor imagery to improve motor learning.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of task complexity or rate of motor imagery on motor learning in healthy young adults

Heena

Zia²,

Sehgal

et al. 2021

Brain and Behavior

View full text Add to dashboard Cite

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“…The latency in the MRCPs of the stand-to-sit transition reflected the more difficult nature of this transition (i.e., the lack of visual feedback towards the back as a person moved from standing to sitting) in comparison to the sit-to-stand transition, making it easier for the classifier to distinguish between AO and MI in this former transition. Indeed, previous studies have shown the effects of task complexity on ERD/S rhythms [49], [50]. To overcome the limitations of the current study, higher number of participants as well as number of trials are required.…”

Section: B Decoding Algorithms For Standing and Sitting Tasksmentioning

confidence: 99%

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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“…The highest energy is observed in the rest task without stimulation, which indicates that vibration stimulation will induce the activation of the contralateral sensorimotor cortex no matter imagined or not. Among them, FPS makes the most prominent energy difference because an increase of task complexity or attention results in an increased magnitude of ERD (Boiten et al, 1992;Mashat et al, 2019).…”

Section: Enhancement Of Closed-loop Vibration Stimulation On MImentioning

confidence: 99%

Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance

et al. 2021

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The motor imagery (MI) paradigm has been wildly used in brain-computer interface (BCI), but the difficulties in performing imagery tasks limit its application. Mechanical vibration stimulus has been increasingly used to enhance the MI performance, but its improvement consistence is still under debate. To develop more effective vibration stimulus methods for consistently enhancing MI, this study proposes an EEG phase-dependent closed-loop mechanical vibration stimulation method. The subject’s index finger of the non-dominant hand was given 4 different vibration stimulation conditions (i.e., continuous open-loop vibration stimulus, two different phase-dependent closed-loop vibration stimuli and no stimulus) when performing two tasks of imagining movement and rest of the index finger from his/her dominant hand. We compared MI performance and brain oscillatory patterns under different conditions to verify the effectiveness of this method. The subjects performed 80 trials of each type in a random order, and the average phase-lock value of closed-loop stimulus conditions was 0.71. It was found that the closed-loop vibration stimulus applied in the falling phase helped the subjects to produce stronger event-related desynchronization (ERD) and sustain longer. Moreover, the classification accuracy was improved by about 9% compared with MI without any vibration stimulation (p = 0.012, paired t-test). This method helps to modulate the mu rhythm and make subjects more concentrated on the imagery and without negative enhancement during rest tasks, ultimately improves MI-based BCI performance. Participants reported that the tactile fatigue under closed-loop stimulation conditions was significantly less than continuous stimulation. This novel method is an improvement to the traditional vibration stimulation enhancement research and helps to make stimulation more precise and efficient.

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Effects of Task Complexity on Motor Imagery-Based Brain–Computer Interface

Cited by 24 publications

References 40 publications

Effects of task complexity or rate of motor imagery on motor learning in healthy young adults

Effects of task complexity or rate of motor imagery on motor learning in healthy young adults

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

Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance

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