Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis, Nikki; Yoon, Sue; Alimardani, Maryam

doi:10.3389/fnhum.2021.732946

Cited by 30 publications

(28 citation statements)

References 86 publications

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“…The dataset used in this study consisted of bipolar derivations, instead of unipolar channel values that are more commonly used in the qEEG analysis [24,29]. The use of bipolar derivations is seen as a more advantageous method compared to unipolar or average referencing methods [33], as it can mitigate the issues associated with common active referencing such as volume conduction [34].…”

Section: Discussionmentioning

confidence: 99%

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qEEG Analysis in the Diagnosis of Alzheimer’s Disease; a Comparison of Functional Connectivity and Spectral Analysis

Frangopoulou

Alimardani

2022

Preprint

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Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

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Section: Discussionmentioning

confidence: 99%

“…For every subject, the PLV was calculated for all possible 253 bipolar channel combinations in five frequency bands as defined above. Next, inspired by [29],…”

Section: Functional Connectivity Analysismentioning

confidence: 99%

qEEG Analysis in the Diagnosis of Alzheimer’s Disease; a Comparison of Functional Connectivity and Spectral Analysis

Frangopoulou

Alimardani

2022

Preprint

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“…One possible reason for these results is that the decrease in functional connectivity may be related to the cognitive load required for MI. Indeed, Leeuwis et al found that functional connectivity during the resting state was greater than that during MI in both BCI illiterate and BCI literate subjects (Leeuwis et al, 2021). Mylonas et al have shown that the decrements of phase synchronization at the alpha and beta bands are associated with the sensorimotor integration that contributes to motor performance (Mylonas et al, 2016).…”

Section: Variation In Functional Connectivitymentioning

confidence: 99%

The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance

et al. 2022

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Motor imagery-based brain-computer interface (MI-BCI) has been largely studied to improve motor learning and promote motor recovery. However, the difficulty in performing MI limits the widespread application of MI-BCI. It has been suggested that the usage of sensory threshold somatosensory electrical stimulation (st-SES) is a promising way to guide participants on MI tasks, but it is still unclear whether st-SES is effective for all users. In the present study, we aimed to examine the effects of st-SES on the MI-BCI performance in two BCI groups (High Performers and Low Performers). Twenty healthy participants were recruited to perform MI and resting tasks with EEG recordings. These tasks were modulated with or without st-SES. We demonstrated that st-SES improved the performance of MI-BCI in the Low Performers, but led to a decrease in the accuracy of MI-BCI in the High Performers. Furthermore, for the Low Performers, the combination of st-SES and MI resulted in significantly greater event-related desynchronization (ERD) and sample entropy of sensorimotor rhythm than MI alone. However, the ERD and sample entropy values of MI did not change significantly during the st-SES intervention in the High Performers. Moreover, we found that st-SES had an effect on the functional connectivity of the fronto-parietal network in the alpha band of Low Performers and the beta band of High Performers, respectively. Our results demonstrated that somatosensory input based on st-SES was only beneficial for sensorimotor cortical activation and MI-BCI performance in the Low Performers, but not in the High Performers. These findings help to optimize guidance strategies to adapt to different categories of users in the practical application of MI-BCI.

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“…In addition, we assumed that poor BCI performers may have greater potential to change their brain activity through learning or external stimulation, for either better or worse, compared to high BCI performers because high BCI performers' brain activity may be stable and optimal already. To divide the subjects into low and high BCI performance groups, a previous study used the median value of the BCI performance [29], as the median value can divide groups with the same size and allow analysis to be performed on sub-groups of the same size. However, the distribution and sample size affect the median value.…”

Section: Bci Performance Evaluationmentioning

confidence: 99%

“…They found that the subjects bene ted from brain network features (functional connectivity), and suggested that low performers may engage in motor imagery differently than high performers, and thus existing features, such as ERD, cannot capture the engagement although the brain network showed the possibility. Similarly, another study divided the subjects recruited into two groups based upon BCI performance, compared their brain networks on multiple network scales [29], and found signi cantly higher phase synchronization values in the right hemisphere during high performers' motor imagery. In addition to brain network measures, a difference between low and high BCI performers' frequency band power has been reported during the rest or pre-stimulus period, showing that subjects with higher alpha band or sensory motor rhythm (SMR) power during those periods are more likely to achieve better BCI performance [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Can Vibrotactile Stimulation and tDCS Help Inefficient BCI Users?

Won

Kim

Gwon

et al. 2022

Preprint

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Brain-computer interface (BCI) has helped people by enabling them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and comparative study of different stimulation modalities has been overlooked. Accordingly, this comparative study was designed to investigate vibrotactile stimulation and transcranial direct current stimulation’s (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the BCI performance of low performers in the vibrotactile stimulation group increased significantly by 9.13% (p=0.0053), and while the tDCS group subjects’ performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking value (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers across all groups showed no significant stimulation effects. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.

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Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Cited by 30 publications

References 86 publications

qEEG Analysis in the Diagnosis of Alzheimer’s Disease; a Comparison of Functional Connectivity and Spectral Analysis

qEEG Analysis in the Diagnosis of Alzheimer’s Disease; a Comparison of Functional Connectivity and Spectral Analysis

The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance

Can Vibrotactile Stimulation and tDCS Help Inefficient BCI Users?

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