Inferring hand movement kinematics from MEG, EEG and intracranial EEG: From brain-machine interfaces to motor rehabilitation

Jerbi, Karim; Vidal, Juan R.; Mattout, Jérémie; Maby, Emmanuel; Lecaignard, Françoise; Ossandón, Tomás; Hamamé, Carlos M.; Dalal, Sarang S.; Bouet, Romain; Lachaux, Jean-Philippe; Leahy, Richard M.; Baillet, Sylvain; Garnero, Line; Delpuech, Claude; Bertrand, Olivier

doi:10.1016/j.irbm.2010.12.004

Cited by 75 publications

(57 citation statements)

References 115 publications

(198 reference statements)

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“…The power attenuation of different rhythms is commonly used in brain machine interface research, for example, to decode fine movement properties using noninvasive techniques. In particular, the possibility of inferring motion kinematics based on noninvasive neural signals may be of great importance for developing and improving existing brain machine interface technologies to be used for motor rehabilitation (Jerbi et al, 2011).…”

Section: Erd and Motion Kinematicsmentioning

confidence: 99%

Alpha and Beta Band Event-Related Desynchronization Reflects Kinematic Regularities

et al. 2015

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The short-lasting attenuation of brain oscillations is termed event-related desynchronization (ERD). It is frequently found in the alpha and beta bands in humans during generation, observation, and imagery of movement and is considered to reflect cortical motor activity and action-perception coupling. The shared information driving ERD in all these motor-related behaviors is unknown. We investigated whether particular laws governing production and perception of curved movement may account for the attenuation of alpha and beta rhythms. Human movement appears to be governed by relatively few kinematic laws of motion. One dominant law in biological motion kinematics is the 2/3 power law (PL), which imposes a strong dependency of movement speed on curvature and is prominent in actionperception coupling. Here we directly examined whether the 2/3 PL elicits ERD during motion observation by characterizing the spatiotemporal signature of ERD. ERDs were measured while human subjects observed a cloud of dots moving along elliptical trajectories either complying with or violating the 2/3 PL. We found that ERD within both frequency bands was consistently stronger, arose faster, and was more widespread while observing motion obeying the 2/3 PL. An activity pattern showing clear 2/3 PL preference and lying within the alpha band was observed exclusively above central motor areas, whereas 2/3 PL preference in the beta band was observed in additional prefrontal-central cortical sites. Our findings reveal that compliance with the 2/3 PL is sufficient to elicit a selective ERD response in the human brain.

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Section: Erd and Motion Kinematicsmentioning

confidence: 99%

Alpha and Beta Band Event-Related Desynchronization Reflects Kinematic Regularities

et al. 2015

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“…Recently, it was demonstrated that MRCPs associated with movements performed with different levels of force and speed of the same body part (wrist and foot movements) could be decoded from the EEG using only information prior to the onset of the movement [13,14,21]. Also, different movement types have been classified such as hand grasping, opening and reaching [1,2,4], movement direction and kinematics (see [19] for a recent review), wrist movements [12,[40][41][42], shoulder and elbow movements [8,[47][48][49] and finger movements [26,27,44].…”

Section: Introductionmentioning

confidence: 99%

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

Jochumsen

Niazi

Dremstrup

et al. 2015

Med Biol Eng Comput

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Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention is crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior the movement onset. Fourteen healthy subjects performed the three grasps 100 times while EEG was recorded from 25 electrodes. Temporal and spectral features were extracted from each electrode, and feature reduction was performed using sequential forward selection (SFS) and principal component analysis (PCA).The detection problem was investigated as the ability to discriminate between movement preparation and the idle state. Furthermore, all task pairs and the three movements together were classified. The best detection performance across movements (79±8%) was obtained by combining temporal and spectral features. The best movement-movement discrimination was obtained using spectral features; 76±9% (2-class) and 63±10% (3-class). For movement detection and discrimination, the performance was similar across grasp types and task pairs; SFS outperformed PCA. The results show it is feasible to detect different grasps and classify the distinct movements using only information prior to the movement onset; which may enable brain-computer interface-based neurorehabilitation of upper limb function through Hebbian learning mechanisms.

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“…EEG is used to monitor ictal and inter-ictal activity in seizure patients [4,5] and to assess cognitive processes during neuroscience and psychology experiments [6,7]. In addition, EEG is used in brain computer interface devices, which enable command of an electronic device by brain activity modulation [8][9][10][11]. Current EEG systems can have as few as four electrodes [11] or as many as 256 electrodes.…”

Section: Introductionmentioning

confidence: 99%

How Many Electrodes Are Really Needed for EEG-Based Mobile Brain Imaging?

Lau¹,

Gwin²,

Ferris³

2012

JBBS

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A noninvasive method for imaging the human brain during mobile activities could have far reaching benefits for studies of human motor control, for research and treatment of neurological disabilities, and for brain-controlled powered prosthetic limbs or orthoses. Several recent studies have demonstrated that electroencephalography (EEG) can be used to image the brain during locomotion provided that signal processing techniques, such as independent Component Analysis (ICA), are used to parse electrocortical activity from artifact contaminated EEG. However, these studies used high-density 256-channel EEG sensor arrays, which are likely too time-consuming to setup in a clinical or field setting. Therefore, it is important to evaluate how reducing the number of EEG channel signals affects the electrocortical source signals that can be parsed from EEG recorded during standing and walking while concurrently performing a visual oddball discrimination task. Specifically, we computed temporal and spatial correlations between electrocortical sources parsed from high-density EEG and electrocortical sources parsed from reduced-channel subsets of the original high-density EEG. For this task, our results indicate that on average an EEG montage with as few as 35 channels may be sufficient to record the two most dominate electrocortical sources (temporal and spatial R2 > 0.9). Correlations for additional electrocortical sources decreased linearly such that the least dominant sources extracted from the 35 channel dataset had temporal and spatial correlations of approximately 0.7. This suggests that for certain applications the number of EEG sensors used for mobile brain imaging could be vastly reduced, but researchers and clinicians must consider the expected distribution of relevant electrocortical sources when determining the number of EEG sensors necessary for a particular application.

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Inferring hand movement kinematics from MEG, EEG and intracranial EEG: From brain-machine interfaces to motor rehabilitation

Cited by 75 publications

References 115 publications

Alpha and Beta Band Event-Related Desynchronization Reflects Kinematic Regularities

Alpha and Beta Band Event-Related Desynchronization Reflects Kinematic Regularities

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

How Many Electrodes Are Really Needed for EEG-Based Mobile Brain Imaging?

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