Comparing Sensorimotor Cortex Activation during Actual and Imaginary Movement

Athanasiou, Alkinoos; Chatzitheodorou, Eleni; Kalogianni, Konstantina; Lithari, Chrysa; Moulos, Ioannis; Bamidis, Panagiotis D.

doi:10.1007/978-3-642-13039-7_28

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…The sensorimotor cortex, during the practice of motor imagery tasks, has been proven to produce similar patterns of activation with actual motor execution tasks [4,6], something our work also confirms [17]. Cortex activation is found to be less intense during motor imagery as compared to motor execution, which is shown by ERD/ERS amplitude as well as by source activation maps produced by eConnectome.…”

Section: Discussionsupporting

confidence: 80%

“…Regarding the ability to perform motor imagery, four out of seven subjects performed equally well in both FMI and HMI, activating their cortex during imaginary movements in the same patterns as in real movements (P > 0.05) in all of the three time intervals as reported previously by our group [17].…”

Section: Event-related Desynchronization/synchronization (Erd/ Ers) Asupporting

confidence: 81%

“…The results of ERD/ERS statistical analysis, published in a previous paper [17] and presented here in a summary (Table 1), encouraged us to further investigate MI movements in terms of functional connectivity.…”

Section: Event-related Desynchronization/synchronization (Erd/ Ers) Amentioning

confidence: 69%

“…None of the subjects had any experience in the concept of motor imagery prior to their participation. The setup of the experiment and part of the analysis has been analytically described in our previous work [17]. Each subject performed four motor tasks: (a) real hand movement (biceps-flexion of the forearm), (b) imaginary hand movement, (c) real foot movement (quadricepsstretch of the lower leg), and (d) imaginary foot movement.…”

Section: Methodsmentioning

confidence: 99%

“…Initially, the extracted signals were processed with an ERD/ERS of mu-rhythm EEG paradigm in mind. This paradigm of cue-paced motor imagery [17], is presented here as an intermediate step that serves to instigate our research towards functional connectivity. The second part, consisting of source depiction and functional connectivity of the extracted signals, deploys different processing methodology, as we describe accordingly.…”

Section: Methodsmentioning

confidence: 99%

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Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Athanasiou

Lithari

Kalogianni

et al. 2012

Advances in Human-Computer Interaction

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Introduction. Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas.Materials and Methods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome.Results and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution.Conclusions. Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces.

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Section: Discussionsupporting

confidence: 80%

Section: Event-related Desynchronization/synchronization (Erd/ Ers) Asupporting

confidence: 81%

Section: Event-related Desynchronization/synchronization (Erd/ Ers) Amentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Athanasiou

Lithari

Kalogianni

et al. 2012

Advances in Human-Computer Interaction

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Recognition of the semantics and kinematics of gestures: Neural responses to “what” and “how”?

Dahan

Reiner

2017

International Journal of Psychophysiology

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Asynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields

2016

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The method proposed in this work makes use of probabilistic graphical models to incorporate temporal information in the classification of finger movements from electrocorticographic recordings. The proposed method highlights the importance of including prior information about the task that the subjects execute. As the results show, the combination of these two features effectively produce a significant improvement of the system's classification performance.

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Comparing Sensorimotor Cortex Activation during Actual and Imaginary Movement

Cited by 8 publications

References 9 publications

Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Recognition of the semantics and kinematics of gestures: Neural responses to “what” and “how”?

Asynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields

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