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...

Objective To quantitatively define levels of upper extremity movement impairment using cluster analysis of Fugl-Meyer upper extremity (FM-UE) with and without reflex items. Design Secondary analysis of FM-UE individual item scores compiled from baseline testing of 5 studies with consistent testing procedures. Setting University and VA research centers. Participants: Individuals (N=−247) with chronic stroke (>6 months post-stroke). Interventions Not applicable. Main Outcome Measures Cut-off scores defined by total FM-UE scores of clusters identified by two hierarchical cluster analyses run on full sample of FM-UE individual item scores (with/without reflexes). Patterns of motor function defined by aggregate item scores of clusters. Results FM-UE scores ranged from 2–63 (mean=26.9±15.7) with reflex items and 0–57 (mean=22.1 ±15.3) without reflex items. Three clusters were identified. The distributions of the FM-UE scores revealed considerable overlap between the clusters, therefore four distinct stroke impairment levels were also derived. Conclusions For chronic stroke, the cluster analyses of the upper extremity FM support either a three or a four impairment level classification scheme.

The identification of neurobiological markers that predict individual predisposition to pain are not only important for development of effective pain treatments, but would also yield a more complete understanding of how pain is implemented in the brain. In the current study using electroencephalography (EEG), we investigated the relationship between the peak frequency of alpha activity over sensorimotor cortex and pain intensity during capsaicin-heat pain (C-HP), a prolonged pain model known to induce spinal central sensitization in primates. We found that peak alpha frequency (PAF) recorded during a pain-free period preceding the induction of prolonged pain correlated with subsequent pain intensity reports: slower peak frequency at pain-free state was associated with higher pain during the prolonged pain condition. Moreover, the degree to which PAF decreased between pain-free and prolonged pain states was correlated with pain intensity. These two metrics were statistically uncorrelated and in combination were able to account for 50% of the variability in pain intensity. Altogether, our findings suggest that pain-free state PAF over relevant sensory systems could serve as a marker of individual predisposition to prolonged pain. Moreover, slowing of PAF in response to prolonged pain could represent an objective marker for subjective pain intensity. Our findings potentially lead the way for investigations in clinical populations in which alpha oscillations and the brain areas contributing to their generation are used in identifying and formulating treatment strategies for patients more likely to develop chronic pain.

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Abstract-Robotics is rapidly emerging as a viable approach to enhance motor recovery after disabling stroke. Current principles of cognitive motor learning recognize a positive relationship between reward and motor learning. Yet no prior studies have established explicitly whether reward improves the rate or efficacy of robotics-assisted rehabilitation or produces neurophysiologic adaptations associated with motor learning. We conducted a 3 wk, 9-session clinical pilot with 10 people with chronic hemiparetic stroke, randomly assigned to train with an impedance-controlled ankle robot (anklebot) under either high reward (HR) or low reward conditions. The 1 h training sessions entailed playing a seated video game by moving the paretic ankle to hit moving onscreen targets with the anklebot only providing assistance as needed. Assessments included paretic ankle motor control, learning curves, electroencephalograpy (EEG) coherence and spectral power during unassisted trials, and gait function. While both groups exhibited changes in EEG, the HR group had faster learning curves (p = 0.05), smoother movements (p