High-frequency band temporal dynamics in response to a grasp force task

Branco, Mariana P.; Geukes, Simon H.; Aarnoutse, Erik J.; Vansteensel, Mariska J.; Freudenburg, Zachary V.; Ramsey, Nick F.

doi:10.1088/1741-2552/ab3189

Cited by 11 publications

(10 citation statements)

References 51 publications

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“…In general, higher frequencies have lower saliencies (Fig. 6D) in line with previous invasive research showing that high-frequency bands are more attenuated in EEG [58], and more relevant for sensorimotor feedback [31,59,60].…”

Section: Cortical Encoding Of Hand-specific Activities During Simultaneous Hand Usesupporting

confidence: 90%

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding

Ortega

Faisal

2021

J. Neural Eng.

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ObjectiveNon-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to interact with the environment. To enable meaningful and stable physical interactions, BMIs need to decode forces. Although previously addressed in the unimanual case, controlling forces from both hands would enable BMI-users to perform a greater range of interactions. We here investigate the decoding of hand-specific forces.Approach We maximise cortical information by using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and developing a deep-learning architecture with attention and residual layers (cnnatt) to improve their fusion. Our task required participants to generate hand-specific force profiles on which we trained and tested our deep-learning and linear decoders.Main results The use of EEG and fNIRS improved the decoding of bimanual force and the deep-learning models outperformed the linear model. In both cases, the greatest gain in performance was due to the detection of force generation. In particular, the detection of forces was hand-specific and better for the right dominant hand and cnnatt was better at fusing EEG and fNIRS. Consequently, the study of cnnatt revealed that forces from each hand were differently encoded at the cortical level. Cnnatt also revealed traces of the cortical activity being modulated by the level of force which was not previously found using linear models.Significance Our results can be applied to avoid hand-cross talk during hand force decoding to improve the robustness of BMI robotic devices. In particular, we improve the fusion of EEG and fNIRS signals and offer hand-specific interpretability of the encoded forces which are valuable during motor rehabilitation assessment.

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Section: Cortical Encoding Of Hand-specific Activities During Simultaneous Hand Usesupporting

confidence: 90%

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding

Ortega

Faisal

2021

J. Neural Eng.

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“…5F) was substantially shorter than the average duration of the force-matching part of the behavioral task (;1 s). A phasic rise in high g modulation near the onset of behavior has been shown during other grasp force behaviors (Chen et al, 2014;Branco et al, 2019b), as well as isotonic movement (Flint et al, 2017). Single-neuron studies in nonhuman primates also support the phasic modulation with force onset (Hendrix et al, 2009), or more often, phasic-tonic modulation (Maier et al, 1993;Mason et al, 2002;Intveld et al, 2018).…”

Section: Discussionmentioning

confidence: 85%

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

Flint

Tate²,

et al. 2020

eNeuro

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The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement [0.7 6 0.3 fractional variance accounted for (FVAF)] and force (0.7 6 0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a stateof-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle (NVA). Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90 6 6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces (BMIs).

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“…In our study, the neural state can be disrupted during movement initiation by a variety of factors, such as increased attention or prediction error once the arm is in motion. In contrast, high-frequency power increases may indicate active sensorimotor processing [90][91][92][93][94]. Low-frequency and highfrequency power changes are thought to represent two separate processes [95,96], which could explain the difference seen in the spatial spread of cortical power changes between low and high frequencies.…”

Section: Discussionmentioning

confidence: 99%

Behavioral and Neural Variability of Naturalistic Arm Movements

Peterson

Singh

Wang

et al. 2020

Preprint

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Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed "in the wild." Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with an important difference: there was considerable neural variability across subjects and events. We modeled inter-event variability using ten behavioral and environmental features; the most important features explaining this variability were reach angle and recording day. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during spontaneous movements and contributes to our understanding of long-term naturalistic behavior.

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High-frequency band temporal dynamics in response to a grasp force task

Cited by 11 publications

References 51 publications

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

Behavioral and Neural Variability of Naturalistic Arm Movements

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