Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.

Two behavioral goals are achieved simultaneously during forward trunk bending in humans: the bending movement per se and equilibrium maintenance. The objective of the present study was to understand how the two goals are achieved by using a biomechanical model of this task. Since keeping the center of pressure inside the support area is a crucial condition for equilibrium maintenance during the movement, we decided to model an extreme case, called "optimal bending", in which the movement is performed without any center of pressure displacement at all, as if standing on an extremely narrow support. The "optimal bending" is used as a reference in the analysis of experimental data in a companion paper. The study is based on a three-joint (ankle, knee, and hip) model of the human body and is performed in terms of "eigenmovements", i.e., the movements along eigenvectors of the motion equation. They are termed "ankle", "hip", and "knee" eigenmovements according to the dominant joint that provides the largest contribution to the corresponding eigenmovement. The advantage of the eigenmovement approach is the presentation of the coupled system of dynamic equations in the form of three independent motion equations. Each of these equations is equivalent to the motion equation for an inverted pendulum. Optimal bending is constructed as a superposition of two (hip and ankle) eigenmovements. The hip eigenmovement contributes the most to the movement kinematics, whereas the contributions of both eigenmovements into the movement dynamics are comparable. The ankle eigenmovement moves the center of gravity forward and compensates for the backward center of gravity shift that is provoked by trunk bending as a result of dynamic interactions between body segments. An important characteristic of the optimal bending is the timing of the onset of each eigenmovement: the ankle eigenmovement onset precedes that of the hip eigenmovement. Without an earlier onset of the ankle eigenmovement, forward bending on the extremely narrow support results in falling backward. This modeling approach suggests that during trunk bending, two motion units--the hip and ankle eigenmovements--are responsible for the movement and for equilibrium maintenance, respectively.

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Upper trunk bending movements were accompanied by opposite movements of the lower body segments. These axial kinematic synergies maintained equilibrium during the movement performance by stabilizing the center of gravity (CG), which shifted on average across all the subjects by 1 +/- 4 cm in the anteroposterior direction and thus always remained within the support area. The aim of the present investigation was to provide an insight into the central control responsible for the performance of these synergies. The kinematic analysis was performed by the method of principal components (PC) analysis applied to the covariation between ankle, knee and hip joint angles and compared with CG shifts during upper trunk bending. Subjects were asked to perform backward or forward upper trunk bending in response to a tone. They were instructed to move as fast as possible or slowly (2 s), with high or low movement amplitudes. PC analysis showed a strong correlation between hip, knee and ankle joint changes. The first principal component (PC1) representing a multijoint movement with fixed ratios between joint angular changes, accounted, on average, for 99.7% +/- 0.2% of the total angular variance in the forward trunk movements and for 98.4% +/- 1.4% in the backward movements. The instructed voluntary regulation of the amplitude and velocity of the movement was achieved by adapting the bell-shaped profile of the velocity time course without changes in interjoint angular relations. Fixed ratios between changes in joint angles, represented by PC1, ensured localization of the CG within the support area during trunk bending. The ratios given by PC1 showed highly significant dependence on subjects, suggesting the adaptability of the central control to each subject's biomechanical peculiarities. Subject's intertrial variability of PC1 ratios was small, suggesting a stereotyped automatic interjoint coordination. When changing velocity and amplitude of the movement, the ratios remained the same in about half the subjects while in others slight variations were observed. A weak second principal component (PC2) was shown only for fast movements. In forward movements PC2 reflected the early knee flexion that seems related to the disturbances caused by the passive interaction between body segments, rather than to the effect of a central command. In fast backward movements, PC2 reflected the delay in hip extension relative to the movement onset in the ankle and knee that mirrors intersubject differences in the initiation process of the axial synergy. The results suggest that PC1 reflects the centrally controlled multijoint movement, defining the time course and amplitude of the movement and fixing the ratios between changes in joint angles. They support the hypothesis that the axial kinematic synergies result from a central automatic control that stabilizes the CG shift in the anteroposterior direction while performing the upper trunk bending.

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