Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks

Cattai, Tiziana; Colonnese, Stefania; Corsi, Marie‐Constance; Bassett, Danielle S.; Scarano, Gaetano; Fallani, Fabrizio De Vico

doi:10.1109/tnsre.2021.3088637

Cited by 17 publications

(14 citation statements)

References 72 publications

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“…From the function activity aspect, the work on synchronization between the signal amplitudes and the phase has been discussed (Rosenblum et al, 1996 ; Nolte et al, 2004 ). It hypothesizes that integrating these complementary features will allow a better characterization of the BCI-related mental states and that including them in the feature extraction block, which is helpful to increase the BCI accuracy compared to standard approaches solely based on power spectra (Cattai et al, 2019 ). In this way, the phase relationship between EEG signals on the prefrontal and parietal lobes will be characterized by PLV.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

Chen

et al. 2022

Front. Aging Neurosci.

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BackgroundThe aging of the world population poses a major health challenge, and brain–computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.ObjectivesThis study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.MethodsA total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time–frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (θ + α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.ConclusionCompared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

Chen

et al. 2022

Front. Aging Neurosci.

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“…In this study, subjects sat quietly 50 cm away from a screen, and the stimulus material (500 mL full drinks) was placed in the right-hand position, facing the table [ 14 ]. The subjects were asked to imagine repeatedly moving his/her right hand (i.e., grasping and lifting) in the state of open eyes.…”

Section: Methodsmentioning

confidence: 99%

“…Giacomo [ 13 ] found significant changes in alpha and beta power in the frontal lobe region when subjects performed hand motion imagination tasks. Cattai [ 14 ] also selected the hand imagination task and found that network features based on spectral coherence increased in the sensorimotor region during motor imagery. Although these studies suggest that activation of different brain regions is associated with changes in motor-imagery-related rhythm characteristics, the exact relationship between the two remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

Guo

et al. 2022

Brain Sciences

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Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded over the human motor cortex have been shown to be consistently suppressed during both the imagination and performance of movements, although the specific effect on brain function remains to be confirmed. In this study, Granger causality (GC) was used to construct the brain functional network of subjects during motor imagery and resting state based on EEG in order to explore the effects of motor imagery on brain function. Parameters of the brain functional network were compared and analyzed, including degree, clustering coefficient, characteristic path length and global efficiency of EEG mu/beta rhythm in different states. The results showed that the clustering coefficient and efficiency of EEG mu/beta rhythm decreased significantly during motor imagery (p < 0.05), while degree distribution and characteristic path length increased significantly (p < 0.05), mainly concentrated in the frontal lobe and sensorimotor area. For the resting state after motor imagery, the changes of brain functional characteristics were roughly similar to those of the task state. Therefore, it is concluded that motor imagery plays an important role in activation of cortical excitability.

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“…However, in this study, subjects were not asked to perform MI techniques. Recent evidence suggests that it may be possible to use indices of functional connectivity using MI tasks ( Cattai et al, 2021 ). More studies are needed to understand the differences observed.…”

Section: Methodsmentioning

confidence: 99%

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Franc

Altamira

Guillen

et al. 2022

Front. Hum. Neurosci.

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Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.

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Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks

Cited by 17 publications

References 72 publications

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

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