Decoding center-out hand velocity from MEG signals during visuomotor adaptation

Bradberry, Trent J.; Rong, Feng; Contreras-Vidal, José L.

doi:10.1016/j.neuroimage.2009.06.023

Cited by 66 publications

(76 citation statements)

References 54 publications

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“…Other studies confirm that the primary sensorimotor cortex encodes hand kinematics at a microscale (Georgopoulos et al, 1986;Moran and Schwartz, 1999;Wessberg et al, 2000;Serruya et al, 2002;Schwartz et al, 2004;Kim et al, 2006), mesoscale Pistohl et al, 2008;Sanchez et al, 2008), and macroscale (Kelso et al, 1998;Jerbi et al, 2007). Several MEG studies report that the IPL also encodes hand kinematics (Jerbi et al, 2007;Bradberry et al, 2009a). Regardless of scale, decoding methods like the one we report here rely on a subsecond history of neural data to reconstruct hand kinematics (Serruya et al, 2002, Sanchez et al, 2008Bradberry et al, 2009a).…”

Section: Discussionsupporting

confidence: 76%

“…Several MEG studies report that the IPL also encodes hand kinematics (Jerbi et al, 2007;Bradberry et al, 2009a). Regardless of scale, decoding methods like the one we report here rely on a subsecond history of neural data to reconstruct hand kinematics (Serruya et al, 2002, Sanchez et al, 2008Bradberry et al, 2009a). Our choice of a 100 ms lag aligns with this convention as well as the rationale that these lags consist of planning activity of the brain associated with the current kinematic sample of the hand.…”

Section: Discussionmentioning

confidence: 93%

“…However, two studies report off-line, continuous reconstruction of 3D hand kinematics from intracranial neuronal activity (Wessberg et al, 2000; Kim et al, 2006), and several studies report off-line, continuous reconstruction of 2D hand and tool kinematics from MEG (Georgopoulos et al, 2005, Jerbi et al, 2007, Bradberry et al, 2008, 2009a. Of the MEG investigations, Bradberry et al (2008Bradberry et al ( , 2009a exclusively employs a center-out movement paradigm, the de facto standard for comparison among decoding studies with BCI implications. These other studies report slightly higher r values (supplemental Table 1, available at www.jneurosci.org as supplemental material), but uniquely our study involves more ambitious experimental settings, such as more reaching targets, greater extent of multijoint movements, self-initiated movements, and self-selected targets.…”

Section: Discussionmentioning

confidence: 99%

“…Only a few studies report continuous decoding of two-dimensional (2D) hand and tool kinematics from magnetoencephalography (MEG) (Georgopoulos et al, 2005, Jerbi et al, 2007, Bradberry et al, 2008, 2009a. Although MEG demonstrates a proof of concept, it is immobile and therefore unsuitable for practical BCI systems.…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals

2010

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It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multijoint movements of the upper limb. Here, we challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated reaches and self-selected targets. Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between measured and reconstructed velocity profiles compared reasonably well to that reported by studies that decoded hand kinematics from neural activity acquired intracranially. We subsequently examined the individual contributions of EEG sensors to decoding to find substantial involvement of scalp areas over the sensorimotor cortex contralateral to the reaching hand. Using standardized low-resolution brain electromagnetic tomography (sLORETA), we identified distributed current density sources related to hand velocity in the contralateral precentral gyrus, postcentral gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a findingtoconsiderduringthedevelopmentofbrain-computerinterfacesystems.Overall,theabilitytocontinuouslydecode3Dhandvelocityfrom EEG during natural, center-out reaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired individuals.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals

2010

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“…Of note is the fact that even though we recorded EEG from 60 channels, which some investigators consider to be high-density recordings (Tononi et al 2010), we showed that as few as 16 sensors were required for decoding with high accuracy. Encouraged by promising results achieved in previous studies carried out in our laboratory (Bradberry et al 2008(Bradberry et al , 2009a(Bradberry et al , 2009b(Bradberry et al , 2010, we designed neural decoders by using timedomain EEG features extracted solely from the fluctuations in the amplitude [i.e., amplitude modulation (AM)] in the EEG signals in the low delta frequency band (0.1-2 Hz).…”

Section: Discussionmentioning

confidence: 99%

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Presacco

Goodman²,

Forrester

et al. 2011

Journal of Neurophysiology

191

199

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Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function. brain computer interface; brain-machine interface; electroencephalography LITTLE IS KNOWN about the organization, neural network mechanisms, and computations underlying the control of walking in humans (Choi and Bastian 2007). Although central pattern generators for locomotion are important in the control of walking, supraspinal networks, including the brain stem, cerebellum, and cortex, must be critical, as demonstrated by the changing motor and cognitive (i.e., spatial attention) demands imposed by bipedal walking in unknown or cluttered dynamic environments (Choi and Bastian 2007;Grillner et al. 2008;Nielsen 2003;Rossignol et al. 2007). Neuroimaging studies show that rhythmic foot or leg movements recruit primary motor cortex (Christensen et al. 2001;Dobkin et al. 2004;Heuninckx et al. 2005Heuninckx et al. , 2008Luft et al. 2002;Sahyoun et al. 2004), whereas electrophysiological investigations demonstrate electrocortical potentials related to lower limb movements (Wieser et al. 2010), as well as a greater involvement of human cortex during steady-speed locomotion than previously thought (Gwin et al. , 2011. In this regard, studies using functional near-infrared spectroscopy (fNIRS) show involvement of frontal, premotor, and supplementary motor areas during walking (Harada et al. 2009;Miyai et al. 2001;Suzuki et al. 2004Suzuki et al. , 2008. That primary sensorimotor cortices carry information about bipedal locomotion has been directly proven by the work of Nicolelis and colleagues (Fitzsimmons et al. 2009), who demonstrated that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to predict the kinematics of bipedal wa...

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Concurrent stable and unstable cortical correlates of human wrist movements

Witte

Galán

Waldert

et al. 2014

Human Brain Mapping

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Cortical activity has been shown to correlate with different parameters of movement. However, the dynamic properties of cortico-motor mappings still remain unexplored in humans. Here, we show that during the repetition of simple stereotyped wrist movements both stable and unstable correlates simultaneously emerge in human sensorimotor cortex. Using visual feedback of wrist movement target inferred online from MEG, we assessed the dynamics of the tuning properties of two neuronal signals: the MEG signal below 1.6 Hz and within the 4 to 6 Hz range. We found that both components are modulated by wrist movement allowing for closed-loop inference of movement targets. Interestingly, while tuning of 4 to 6 Hz signals remained stable over time leading to stable inference of movement target using a static classifier, the tuning of cortical signals below 1.6 Hz significantly changed resulting in steadily decreasing inference accuracy. Our findings demonstrate that non-invasive neuronal population signals in human sensorimotor cortex can reflect a stable correlate of voluntary movements. Hence, we provide first evidence for a stable control signal in non-invasive human brain-machine interface research. However, as not all neuronal signals initially tuned to movement were stable across days, a careful selection of features for real-life applications seems to be mandatory.

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Decoding center-out hand velocity from MEG signals during visuomotor adaptation

Cited by 66 publications

References 54 publications

Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals

Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Concurrent stable and unstable cortical correlates of human wrist movements

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