Brain oscillatory activity as a biomarker of motor recovery in chronic stroke

Abstract In the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.

tions over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement.
Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization.
Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.

K E Y W O R D S
EEG, motor control, neuronal plasticity, rehabilitation, stroke 1 | INTRODUCTION Stroke is a major global health problem. The number of stroke victims has been rising in the past years all around the world. Millions of stroke survivors are left with very limited motor function or complete paralysis and depend on assistance (Feigin et al., 2016). Therapeutic approaches such as constraint-induced movement therapy are not applicable to the group of patients with severe limb weakness (Birbaumer, Ramos-Murguialday, & Cohen, 2008). However, brain-machine interface (BMI) training has demonstrated efficacy in promoting motor recovery in chronic paralyzed stroke patients (Ramos-Murguialday et al., 2013), and long term effects (Ramos-Murguialday et al., 2019). Subsequent work has replicated and confirmed BMI efficacy. Arm and hand movements are trained using a body actuator (e.g., orthotic robots) that is controlled by oscillatory activity of the brain (Ang et al., 2014;Frolov et al., 2017;Leeb et al., 2016;Mokienko et al., 2016;Ono et al., 2014). Brain signals can thus travel to the limb muscles along an alternative pathway. Contingently linking movementrelated patterns of brain activity and visuo-proprioceptive feedback of the movement supports associative learning (Ramos-Murguialday et al., 2012;Sirigu et al., 1995).
Changes in sensorimotor brain oscillations involved in planning and execution of movements were used as control signals for the BMI in the aforementioned studies. The sensorimotor rhythm (SMR) is an oscillation within the alpha frequency range of the EEG during a motionless resting state over the central-parietal brain regions. Movement planning, imagination and execution lead to its suppression. In the present work, we investigate EEG brain oscillations of the alpha frequency, ranging from 8 to 12 Hz, over the motor cortex, and we term them "alpha oscillations." Biomarkers could be defined as indicators "of disease state that can be used as a measure of underlying molecular/cellular processes that may be difficult to measure directly in humans" (Boyd et al., 2017). When dealing with a condition as heterogeneous as stroke validated biomarkers of recovery could help plan treatments and support efficient allocation of resource while maximizing outcome for the patients. Alpha brain oscillations have been evaluated as markers of ischaemia and predictors of clinical outcome in acute patients (Finnigan & van Putten, 2013;Rabiller, He, Nishijima, Wong, & Liu, 2015). Desynchronization in the alpha frequency range has also been investigated as a marker of stroke and a predictor of recovery in the same patient group. Tangwiriyasakul, Verhagen, Rutten, and Putten (2014) showed that the recovery of motor function was accompanied by an increase of alpha desynchronization on the ipsilesional side. In subacute patients presenting mild to moderate motor deficits recovery lead to a similar increase of alpha desynchronization on the affected hemisphere (Platz, Kim, Engel, Kieselbach, & Mauritz, 2002).
Furthermore, first attempts investigated correlations of alpha desynchronization with motor improvements in chronically impaired patients (Kaiser et al., 2012). In a controlled study, a group of subacute patients with severe deficits used motor imagery, guided by a braincomputer interface, in addition to their regular physiotherapeutic rehabilitation protocol. They showed a higher probability for motor improvements with increased alpha desynchronization (Pichiorri et al., 2015).
In the present work, we investigated what changes in the oscillatory activity of the brain a proprioceptive BMI coupled with physiotherapy produces over the course of a training intervention and if these correlate with recovery in severely paralyzed chronic stroke patients. We hypothesized that functional motor improvements are accompanied by an ipsilesional increase and a contralesional decrease in alpha desynchronization indicating reorganization of compensatory brain activity from the contralesional to the ipsilesional hemisphere.
We intend to establish alpha oscillatory activity as a biomarker of motor impairment and as a building block of statistical models of stroke neurorehabilitation.
2 | METHODS 2.1 | Study design of the original trial Thirty chronic stroke patients took part in the original study (Ramos-Murguialday et al., 2013). They presented no active finger extension due to their severe motor impairment, as measured by the modified upper limb Fugl-Meyer Assessment (FMA; Table 1). Apart from the complete paralysis of one hand, the inclusion criteria were: age between 18 and 80 years, at least 8 months since the insult, no psychiatric or neurological condition other than stroke, no cerebellar lesion or bilateral motor deficit, no epilepsy and a mini-mental state (MMS) score of above 21. The patients were recruited publicly via stroke associations, rehabilitation centers and hospitals within Germany from December 2007 to March 2013. 504 patients were contacted, out of which 263 did not meet the inclusion criteria, 202 declined to participate and 9 were excluded because of other reasons, leading to a final pool of 30 patients. This number met the criteria for statistical power calculated in study using a similar technique (Buch et al., 2008). Half of the patients showed lesions with involvement of the motor cortex ("mixed" lesion type), the others presented subcortical lesions only ("subcortical" lesion type). The primary clinical outcome measure of the original trial was the combined modified Fugl-Meyer assessment (cFMA). It comprises the sum of the arm and hand scores excluding scores related to coordination, speed and reflexes. The maximum score is 54 points. Details on the movements assessed in the cFMA test are presented in Supporting Information, section 5. The assessment was administered at the post test and two tests prior to the intervention. The mean of both baseline FMAs was used to calculate the difference between the values before and after the intervention.

| Standard protocol approvals, registrations, patient consent
The original clinical trial and the analysis presented here were conducted at the University of Tübingen, Germany. Informed consent was obtained from all patients and the studies were approved by the T A B L E 1 Means and standard deviations of demographic data at the time of enrollment in the study

Sex
Age ( The column "lesion distribution" shows the number of mixed lesions (i.e., lesions including cortical and subcortical areas) and subcortical lesions in the experimental group ("Cont") and the control group ("Sham").
ethics committee of the Faculty of Medicine of the University of Tübingen, Germany. Authorization has been obtained for disclosure of the person recognizable in Figure 1.
We fully acknowledge that clinical trials should be registered publicly for transparency. However, by the time the clinical trial of the original study was conducted the registration of such trials was neither mandatory nor common practice, which is why the trial was not registered. A posteriori registration of the trial is pending.

| Intervention protocol of the original trial
The patients were randomly divided into an experimental group (n = 16) and a control group (n = 14). The original study was doubleblinded to avoid potential bias introduced by experimenters. In both groups, electric brain activity was recorded using electroencephalography (EEG). Changes in the SMR of the ipsilesional hemisphere during movement attempts of fingers and arm were contingently translated into movement of the arm and hand orthosis only in the experimental group. Decrease of the power of the SMR with respect to baseline led to movement of the arm or the hand and a relative increase stopped the movement. In the control group, the setup was similar but the movements executed by the robot were independent of brain activity. The movements were triggered randomly but the period of time the orthosis was moving was approximately equivalent to that of the experimental group. Both groups received identical physiotherapy after the BMI training. Each subject performed 17 ± 1.8 (mean ± SD) sessions of BMI-training within a period of up to 6 weeks. Each session consisted of 165 ± 19.5 (mean ± SD) trials. A training trial consisted of an intertrial interval (4-7 s), a preparation phase (2 s) and the movement phase (5 s F I G U R E 1 Schematics of the data acquisition phase and the offline analysis for EEG and EMG. Neurophysiological data was acquired using a 16 channel EEG cap and 4 bipolar EMG electrodes on each arm. EEG data were cleaned from eye movement artifacts and trials containing other artifacts (e.g., cranial EMG, head movements, and so on). EMG data were analyzed to detect compensatory muscle contractions on the healthy upper limb and on the paretic side during resting intervals to identify these trials as contaminated because the muscle activity is a sign of undesired EEG activity. Only data free of artifacts were used for the final analysis of EEG oscillatory activity onsets or involuntary contractions. The signals were sampled at 500 Hz. The arm orthosis was a ReoGo rehabilitation robot (Motorika, Cesarea, Israel). We used a robotic orthotic system developed inhouse to exert hand movements.
The individual SMR frequency was obtained from EEG recorded in a calibration session on the day before the training. The power of the EEG signal while the patients rested and while they were trying to open and close the paretic hand was compared. The frequency range showing the maximum variance between the two conditions as measured by the coefficient of determination r 2 was defined as individual SMR frequency. The most discriminative electrodes in the centralparietal region were selected.

| Artifact detection
Even though the participants were instructed to minimize movements of head and body during the recordings, contamination of the EEG by movement artifacts could not be completely prevented as the experiment involved movements of the body. Detection and rejection of artifacts in the data were carried out using a fully automated process ( Figure 1). First, artifacts caused by eye movements were removed from the EEG signal by way of extracting the independent components representing these artifacts identified in the EOG (Halder et al., 2007). Then, trials contaminated by cranial muscle artifacts were detected. The EEG signal of all channels was

| Movement-related features of the EEG power spectrum
Movement planning, imagination, and execution lead to suppression of brain oscillatory activity over the motor cortex. It has been shown that stroke patients can (re-) learn to voluntarily modulate this rhythm to control movements of their paretic limbs by way of robotic orthoses (Buch et al., 2008;Ramos-Murguialday et al., 2013). The phenomenon is often defined as mμ rhythm or as SMR. There are various works describing the effect within the alpha frequency range of the EEG (Klimesch, Sauseng, & Hanslmayr, 2007;Kuhlman, 1978;Pfurtscheller & Lopes da Silva, 1999). Similar synchronization and desynchronization effects have been reported with other functional relevance in the beta frequency range (van Wijk, Beek, & Daffertshofer, 2012). Peak frequency and amplitude of the SMR vary between individuals but movement-related desynchronization in healthy populations spreads across the whole alpha range (Pfurtscheller, 2003). As alpha oscillations may also constitute indicators of underlying processes not related to movements it may be difficult to discern alpha central oscillations from genuine sensorimotor oscillatory activity in patients involved in visuo-proprioceptive motor tasks (Klimesch et al., 2007). However, since first, significant power decreases in healthy subjects during execution of a BMI lasting several seconds have mainly been found in the alpha frequency range Event-related desynchronisation (ERD) was calculated following Pfurtscheller and colleagues (Pfurtscheller & Lopes da Silva, 1999) as the proportional decrease of EEG power in a movement attempt interval, M, relative to a reference interval, R: ERD over the sensorimotor cortex was extracted from both hemispheres separately using the EEG signal of the electrodes C3, Cp3, P3 and C4, Cp4, P4, respectively, and within the alpha frequency band (8-12 Hz). The power spectral density was computed using Welch's method and the mean power of that frequency range was extracted.
Furthermore, the EEG power was averaged over the three channels on each hemisphere. Note that because of averaging the power of the three channels no other spatial filters were used. Mean ERD was computed as described in Equation (1) over all trials of each session using the EEG data of the last 4 s of the intertrial interval as reference R and the EEG data of the movement attempt phase as M.
It is important to note that a larger relative difference between neural activity during rest (synchronized, larger EEG power) and action (desynchronized, smaller EEG power) is represented by a numerically smaller, more negative ERD value (Equation 1) and vice-versa. We thus report "strong" ERD when the ERD values are more negative and "weak" ERD when they are less negative.
Works on brain oscillatory biomarkers of stroke rehabilitation were often limited to predicting behavioral changes by brain activity measured before and after spontaneous recovery or intervention (Stinear, 2017).

| Statistical modeling
In order to model the cross-sectional response (the clinical outcome measure ΔcFMA) with the longitudinal predictors (progression of the ERD across training sessions) we employed a two-stage modeling process. First, the individual time courses of the ERD of all patients were modeled using a linear mixed-effects model. In the second step, the coefficients of these modeled time courses were used to predict each patients' motor improvement.
Linear mixed-effects models are suited for describing longitudinal physiological data because (a) they allow to reflect individual differences of intercepts and slopes with respect to population means; (b) data may be modeled even though measurements are unequally timed; (c) the number of measurements per subject is not required to be equal (Lang et al., 2016) (for a thorough description of linear mixed models, LMEMs, see [Verbeke & Molenbergs, 2001]). Shetty, Morrell, and Najjar (2009) showed that estimating the value of the explanatory variable(s) with a LMEM approach leads to the best regression parameters for predicting a clinical outcome.
Using this approach, in the first step of modeling a LMEM is con- To assess the interhemispheric asymmetry of brain activation during recovery, the laterality coefficient is often used (Kaiser et al., 2012;Pivik, Broughton, Davidson, Fox, & Nuwer, 1993;Tangwiriyasakul et al., 2014;van Putten, 2007). The sign of the coefficient represents the laterality of the desynchronization, that is, which of the hemispheres is more active during a certain condition such as the movement of the paretic arm. In order to assess the progression of the asymmetry of the interhemispheric oscillatory activity of the brain, we expanded the laterality coefficient to encompass the tempo- In linear models with interaction terms two independent variables might exert an effect on the dependent variable. They might also modulate each other. In order to understand and interpret the interaction the data is usually separated into smaller subsets (Aiken & West, 1991). One variable is "fixed" and defines these subsets while the other variable is investigated independently within each subset. This procedure allows to observe whether the value of the "fixed" variable influences the "free" variable depending on the subset or not. If the "fixed" variable is categorical the subsets are naturally defined. However, here, the variable of interest, initial ERD, is a continuous variable and the separation is defined based on prior knowledge and the characteristics of the data (Aiken & West, 1991). Given the amount of data points a division into few subsets is the best choice.
Furthermore, even though there is no standardized definition, "strong initial ERD" and "weak initial ERD" may be meaningful for the interpretation. For these reasons, we split the data into equal subsets at the median. The procedure supports intuitive visualization of the linear model (Breheny & Burchett, 2016). Moreover, it facilitates interpretation of the analysis of the brain activity on the healthy hemisphere because we saw that the ipsilesional brain activity of the patients is modulated differently in the subgroups. We thus show the correlation of the progression of ERD and ΔcFMA for two subgroups presenting relatively strong and relatively weak initial ERD (higher and lower than the median). The median value of the initial ERD is In summary, the patients presenting a relatively weak ipsilesional ERD at the beginning of the intervention, presented a larger motor improvement if their ERD decreased on the healthy hemisphere (i.e., activating their contralesional hemisphere less) during paretic hand movements using the BMI.

| Prediction of ΔcFMA from interhemispheric asymmetry of brain activation
To investigate the interhemispheric asymmetry during motor recovery, the progressive laterality coefficient pLC ERD was used to predict the clinical change ΔcFMA. The F-test for this linear regression equation was significant: F(1, 20) = 9.11, p = .007 with an adjusted r 2 = .28 ( Figure 4). The analysis thus demonstrated that the patients who progressively produce more ipsilesional relative to contralesional brain oscillatory activity (stronger desynchronization) in the alpha band during the course of training improved motor function.
Since The results indicate that the patients might have used two strategies to gain control over the orthoses to link brain oscillatory activity and upper limb movement. Their success rebalancing ipsi−/contralesional activity plays an important role in impairment reduction.
Considering that the proprioceptive feedback was initiated based on the individual SMR ERD, patients who could elicit a strong ERD at the beginning could learn to control the robotic orthosis BMI more easily. We show that a strong ERD on the ipsilesional side during movement attempts of the paretic limb and a subsequent further increase of the ERD was linked to recovery whereas a strong ERD on the healthy hemisphere was not. The results indicate that generating a strong ERD on the hemisphere of the lesion may suffice to regain control of the paretic limb via BMI and to reduce motor impairment. It was indicated that patients without transfer of ERD from the contrato the ipsilesional hemisphere do not improve as predicted by the concept of learned nonuse (Daly & Wolpaw, 2008). A linear relationship between the relative progressions of the ERD on both hemispheres was observed. The modeling presented, links greater improvement of motor function to stronger ERD on the affected hemisphere than on the healthy hemisphere during BMI intervention.
Patients with weak ERD during movement attempts of the paretic arm at the beginning of the intervention improved if they showed progressively reduced ERD on the hemisphere of the lesion and an even more pronounced progressive reduction of desynchronization on the healthy hemisphere. One additional explanation to the learned nonuse model of rehabilitation for this phenomenon is that when having acquired proficiency in performing the motor task, reduced ERD represents more efficient inhibition of systems that are not task-relevant on the ipsilesional side (Klimesch et al., 2007;Taub et al., 1994).
Moreover, a connection between alpha synchronization of the EEG and focalized suppression of areas involved in generation of movements irrelevant to the task is assumed (Klimesch et al., 2007;Pfurtscheller, Stancák, & Neuper, 1996). The reduction of desynchronization on the healthy hemisphere as compared to the affected hemisphere indicates less recruitment of the healthy hemisphere during the course of the training as predicted by the model of learned nonuse (Taub et al., 1994). Experiments have shown that interhemispheric inhibition from the healthy to the affected hemisphere is associated with deficient motor recovery (Murase, Duque, Mazzocchio, & Cohen, 2004). Concordant with this interpretation the increased desynchronization of the healthy hemisphere is associated with poorer recovery (Kaiser et al., 2012).
The stratification of the patients into two subgroups ( The longitudinal analysis of the desynchronization of beta oscillation does not allow concise interpretation because the model is not significant. An explanation could be that beta desynchronization is not upheld throughout the whole trial, as has been shown in a healthy population (Ramos-Murguialday & Birbaumer, 2015). There, significant beta desynchronization only occurred in the beginning of the movement period of the trials. In the present analysis the spectral activity was computed over the whole movement period of trials. Furthermore, we might not be able to capture the dynamics of beta oscillations in terms of linear modeling of desynchronization. More complex metrics such coherence might be more suited (Nicolo et al., 2015). The longitudinal analysis of the individual SMR frequency band shows weaker fits of the model than the analysis of the alpha band.
An explanation might be that this analysis included some patients that were rewarded for SMR desynchronization in the beta frequency range during the intervention (see Supporting Information, section 4, Table S2). In healthy populations significant power decreases during execution of a BMI task lasting several seconds have mainly been found in the alpha frequency range (Ramos-Murguialday & Birbaumer, 2015) and movement-related activity is known to spread across the whole alpha range (Pfurtscheller & Lopes da Silva, 1999). This result underlines the potential of alpha desynchronization as a biomarker as it explains the variance of the changes in the Fugl-Meyer scores better than the other frequency ranges.Alpha ERD was also evaluated in the pre and post assessment of the trial. The patients performed repeated movement attempts of their paralyzed arm. We found no difference of alpha ERD between the pre and the post assessment and between groups (see Supporting Information,  high (Platz et al., 2005), but its sensitivity especially in severe patients might not be sufficient. Therefore, several measures were taken to ensure that the changes in the original study are adequately captured.
First, the assessors were blinded to group allocation to avoid a potential retest bias. If there had been a general repetition effect all patients should have improved, which is not the case. Second, the mean of both baseline FMAs was used to measure improvement (Whitall et al., 2010). Statistical analysis of the Fugl-Meyer values of arm and hand of the two baseline assessments for the present cohort showed that the distributions are not different (Wilcoxon signed-rank test: p = .30).
This underlines that the test-retest reliability of the FMA is high in our sample. Third, the assessment focused only on the upper limb motor scores of arm and hand without coordination and speed, and without scores related to reflexes, further reducing variability (Crow & Harmeling-van der Wel, 2008). Linear mixed-effects models are suited for describing physiological data because they acknowledge individual deviations from the population mean and account for unequal number and unequal spacing of data points (Lang et al., 2016). However, each model is a simplification of the data. Learning processes in Neurofeedback have also been described with much higher orders (Gunkelman & Johnstone, 2005).
The model coefficients provide the best description of the data in a least squares sense and the LMEM including subject-specific slopes describes the data significantly better than a model not allowing deviation from the general slope. A likelihood ratio test of a model comparison shows a significant difference (χ 2 = 6.57, p = .038). However, even with the flexibility that linear-mixed effects models allow, assuming linear progression of the ERD values could be an oversimplified description of the true time course. Moreover, the twostaged linear modeling employed in the present work could introduce further simplification due to the second modeling step, which might blur the results. On the other hand, linear models allow for the description of the underlying processes with only a few parameters, which is an advantage for intuitive interpretation and quantitative comparison of the models and necessary for the two-stage analysis employed here.
Four patients of the control group showed a decline of their motor function regardless of the dynamics of their ERD throughout the intervention (two squares below the zero line in Figure 2). It has been suggested that the contingency of brain-activity and visuo-  (Mane et al., 2019). Studies of oscillatory brain activity during motor imagery and movement of the paretic hand of moderately to severely affected chronic stroke patients (Kaiser et al., 2012) as well as subacute patients of mild to moderate (Platz et al., 2002) and severe impairment (Pichiorri et al., 2015) support our findings suggesting that the level of impairment is negatively correlated to the desynchronization of alpha oscillations on the ipsilesional hemisphere. Moreover, an increase of ipsilesional ERD was observed after spontaneous recovery in acute stroke (Tangwiriyasakul et al., 2014) with concomitant lack of ERD on the healthy hemisphere, which indicates our results might generalize in acute and sub-acute stroke patients. The sensorimotor ERD magnitude has also been shown to correlate with recovery in spinal cord patients (López-Larraz, Montesano, Gil-Agudo, Minguez, & Oliviero, 2015), supporting the validity of this metric as a viable and easily obtainable biomarker of clinical progress in patients suffering from motor impairments and as a measure of brain plasticity (Takemi, Masakado, Liu, & Ushiba, 2015). Moreover, the presence of alpha oscillations at cortical sites of the sensorimotor systems reflects the intact balance of thalamic circuits, particularly reticular thalamic recurrent inhibition of thalamocortical afferents (Steriade, Gloor, Llinas, da Silva, & Mesulam, 1990). Lack of these oscillations in relaxed wakefulness and sleep thus does not allow the excitatory blockade of inhibitory reticular-thalamic and centro-thalamic circuits at the ipsilesional thalamo-cortical system. Reappearance of the delicate excitatoryinhibitory balance in the thalamocortical circuits after stroke in the course of a learning process directly targeting this oscillatory mechanisms, clearly supports the neurophsyiosological logic of BMI strategies (Birbaumer & Cohen, 2007;Birbaumer, Elbert, Canavan, & Rockstroh, 1990). Stinear et al. (2017) proved the performance of their sequential algorithm PREP2. It is based on clinical, neurophysiological and neuroimaging markers. Not only does it correctly predict the clinical outcome for 75% of patients after stroke, but it also shows that transcranical magnetic stimulation and clinical tests may replace much more expensive assessments such as magnetic resonance imaging without loss of accuracy. EEG-based biomarkers of stroke could serve the same purpose of improving treatment outcome while reducing effort. Furthermore, biomarkers of stroke and recovery could also support stratification of participants for clinical trials and thus improve statistical power by reducing unexplainable variance. In the present analysis changes in alpha ERD are only found in the data of the training, which underlines the proprioceptive and longitudinal aspect.
Using an orthosis providing proprioceptive feedback when recording data for pre-and postassessments could enable an evaluation. This would increase the effort of obtaining the data for the screening but might support patient stratification based on the model presented here: If a patient shows strong ERD in the assessment the intervention could focus on further strengthening of desynchronization. If the patient has less ability to generate ERD on the ipsilesional hemisphere the intervention could focus on bilateral asymmetry. Indeed, all patients might profit from changing the focus of down-regulating ipsilesional alpha oscillations to modulating the interhemispheric balance of alpha oscillations, which might represent a more beneficial bio-target (i.e., BMI control signal) for EEG-based BMI applications in stroke rehabilitation.
Our results constitute a building block of more generalizable statistical models of the process of motor recovery in chronic stroke.
However, it is important to emphasize that models as the one presented here only show correlations to outcome variables. Despite the statistical strength of the predictions no causal inference can be made.
Therefore, including further neurophysiological markers and clinical information would improve the prediction of outcome, informing procedures and tracking of progress. The present results encourage more efforts to pool data of stroke rehabilitation procedures like the ENIGMA Stroke Recovery initiative (http://enigma.ini.usc.edu/ongoing/enigma-stroke-recovery/) to conceive statistical models that will further improve predictive power of and conclusions drawn from data such as presented here. Quantitative statistical comparison of performance of different markers and different combinations and sequences of markers could eventually yield the optimal procedure and best outcome for the individual patient.