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Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.

ABSTRACT: Brain computer interfaces (BCIs) provide a direct communication channel by using brain signals, enabling patients with motor impairments to interact with external devices. Motion intention detection is useful for intuitive movement-based BCI as movement is the fundamental mode of interaction with the environment. The aim of this paper is to investigate the temporal dynamics of brain processes using electroencephalography (EEG) to explore novel neural correlates of motion intention. We investigate the changes in temporal dependencies of the EEG by characterising the decay of autocorrelation during asynchronous voluntary finger tapping movement. The evolution of the autocorrelation function is characterised by its relaxation time, which is used as a robust marker for motion intention. We observed that there was reorganisation of temporal dependencies in EEG during motion intention. The autocorrelation decayed slower during movement intention and faster during the resting state. There was an increase in temporal dependence during movement intention. The relaxation time of the autocorrelation function showed significant (p < 0.05) discrimination between movement and resting state with the mean sensitivity of 78.37 ± 8.83%. The relaxation time provides movement related information that is complementary to the well-known event-related desynchronisation (ERD) by characterising the broad band EEG dynamics which is frequency independent in contrast to ERD. It can also detect motion intention on average 0.51s before the actual movement onset. We have thoroughly compared autocorrelation relaxation time features with ERD in four frequency bands. The relaxation time may therefore, complement the well-known features used in motion-based BCI leading to more robust and intuitive BCI solutions. The results obtained suggest that changes in autocorrelation decay may involve reorganisation of temporal dependencies of brain activity over longer duration during motion intention. This opens the possibilities of investigating further the temporal dynamics of fundamental neural processes underpinning motion intention.

SUBMITTER: Wairagkar M

PROVIDER: S-EPMC5839569 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.

Wairagkar Maitreyee M Hayashi Yoshikatsu Y Nasuto Slawomir J SJ

PloS one 20180306 3

Brain computer interfaces (BCIs) provide a direct communication channel by using brain signals, enabling patients with motor impairments to interact with external devices. Motion intention detection is useful for intuitive movement-based BCI as movement is the fundamental mode of interaction with the environment. The aim of this paper is to investigate the temporal dynamics of brain processes using electroencephalography (EEG) to explore novel neural correlates of motion intention. We investigat ...[more]

PMID: 29509785

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Project description:Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.

Project description:The neural correlates of anesthetic-induced unconsciousness have yet to be fully elucidated. Sedative and anesthetic states induced by propofol have been studied extensively, consistently revealing a decrease of frontoparietal and thalamocortical connectivity. There is, however, less understanding of the effects of halogenated ethers on functional brain networks.The authors recorded simultaneous resting-state functional magnetic resonance imaging and electroencephalography in 16 artificially ventilated volunteers during sevoflurane anesthesia at burst suppression and 3 and 2 vol% steady-state concentrations for 700?s each to assess functional connectivity changes compared to wakefulness. Electroencephalographic data were analyzed using symbolic transfer entropy (surrogate of information transfer) and permutation entropy (surrogate of cortical information processing). Functional magnetic resonance imaging data were analyzed by an independent component analysis and a region-of-interest-based analysis.Electroencephalographic analysis showed a significant reduction of anterior-to-posterior symbolic transfer entropy and global permutation entropy. At 2 vol% sevoflurane concentrations, frontal and thalamic networks identified by independent component analysis showed significantly reduced within-network connectivity. Primary sensory networks did not show a significant change. At burst suppression, all cortical networks showed significantly reduced functional connectivity. Region-of-interest-based thalamic connectivity at 2 vol% was significantly reduced to frontoparietal and posterior cingulate cortices but not to sensory areas.Sevoflurane decreased frontal and thalamocortical connectivity. The changes in blood oxygenation level dependent connectivity were consistent with reduced anterior-to-posterior directed connectivity and reduced cortical information processing. These data advance the understanding of sevoflurane-induced unconsciousness and contribute to a neural basis of electroencephalographic measures that hold promise for intraoperative anesthesia monitoring.

Project description:ObjectiveSeventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs.ApproachTo address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations.Main resultsOur decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort.SignificanceThe NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.

Dataset Information

Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.

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Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.

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