The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial

Fig. 1

The virtual mirror (left), a sequence of the visual feedback (central), and combined visual-haptic modality (right)

On the other hand, the RAGT-VR group was not provided an avatar but a smile, indicating the goodness of the leg movements. The biofeedback of the Lokomat gait orthosis is based on the interaction torques between the participant and the orthosis. To this end, hip and knee linear drives are equipped with force sensors that measure the human-machine interaction forces at the hip and knee joints for stance and swing phase, which are required to keep the participant on a predefined gait trajectory. The weighting functions of the gait cycle were defined for each part so that the resulting biofeedback values increased for therapeutically desirable movements, i.e. hip and knee flexion-extension force. Thus, biofeedback force values (representing the physical activity of the participants) were positive when the patient was actively participating and negative when passively participating in Lokomat training (or when muscles were inappropriately or involuntary activated) [36]. We obtained an average biofeedback value for each joint in the hemiparetic leg.

Virtual reality

The 2D VR set-up consisted of a 42-in. flat-screen placed in front of the Lokomat and a 7.1 Dolby Surround system (Fig. 1). The Lokomat device served as a multimodal feedback system: the human-machine interaction forces measured from the Lokomat device were used as an input device for the patient’s movements into the VR (i.e., to animate the motion of the human figure in VR at a 60 Hz refresh rate in real time on the screen – virtual mirror. There was no lag between motions of the subject and virtual figure) or the smiling face. The orthosis guided subject’s leg movements in the sagittal plane within individually adapted hip and knee joint trajectories. Lokomat potentiometers provided real-time information of the subject’s hip and knee angles. The measured joint angles were used to animate the subject’s human figure in the virtual mirror. The distance between the subject and projection screen was 1.5 m. Furthermore, since the run lane was not always straight, the VR running game used an asymmetrical physical activity of the legs to induce turning in the virtual environment. In particular, turning right and left was induced by increasing the activity of the contralateral leg of the desired direction and, respectively, decreasing the activity of the ipsilateral one. Lokomat device provided visual and acoustic feedback that reflected the interactions with objects represented in the virtual environment (e.g., the boundaries of the run lane, or objects to be avoided or collected). Further, Lokomat provided haptic feedback by the gait orthosis, so that the subjects using the device were provided with a haptic experience from proprioceptive (joint angles) feedback about their movements [47, 48].

The use of 2D displays, which are not as realistic as the true stereo 3D ones (full-3D VR), are akin to looking at a scene through a window and offer a limited sense of presence, potentially limiting the significance of our findings. We potentially provided a higher sense of presence by using depth cues, such as perspective, relative motion, occlusion, and aerial perspective, despite the use of a 2D VR [7].

EEG recording and preprocessing

EEG was recorded using a Brain-Quick System (Micromed; Mogliano Veneto, Italy), from standard 19 electrodes headset according to the International 10-20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2, ground on the forehead), for 10 min while performing Lokomat training. To monitor eye movements, an electro-oculogram (EOG) with a bipolar montage (one pair of electrodes traced horizontal eye movements, a second pair the vertical ones) was also collected. EEG end EOG were sampled at 500 Hz, high pass filtered at 1 Hz using a zerophase FIR filter (order 7500) to minimize drifts, low pass filtered at 200 Hz (zerophase FIR filter order 36), and referenced to Cz [49]. An adaptive filter was applied to allow real-time filtering of signals recorded from EOG [50, 51].

Electrode impedance was kept below 5kΩ. During the entire EEG recording (as well as during the entire gait training), an experimenter checked for possible signs of drowsiness (e.g., abrupt worsening in gait performance, closed eyes, increase of proportion of theta and alpha activity in the eyes-open condition) [52], which were counted (given that monotonous gait pattern provided by RAGT may tend to induce sleepiness, thus decreasing arousal that negatively affects gait training progress). Patients were prohibited from drinking coffee, smoking, and change their bedtime during the three days prior EEG recording.

Infomax independent component analysis (ICA) was computed on the preprocessed EEG signal to decompose neural and artefactual sources [53, 54, 55, 56, 57]. In detail, ICA was computed two times. First, 500 ms-segmented EEG signals were removed if its probability distribution exceeded the average distribution by 5 ± SD. Then, ICA was computed to reject epochs based on the probability distribution of the IC projections. Thus, EEG segments were re-filtered (8-40 Hz) and a second ICA was computed a second time. The so-obtained IC were grouped into clusters using a k-means algorithm (based on the feature vector of dipole location, power spectra, and scalp map). The IC closest to the cluster centroid was remained for each subject, so to have equal contribution of each subject to the cluster-wise analysis.

EEG analysis

EEG analysis consisted of the computation of the power spectral density (PSD) (using Welch’s Method) and the time-frequency analysis to evaluate Event-related-spectral-perturbations (ERSPs) for each IC [58].

EEG was segmented into 1.4 s epochs (−700;700)ms with regard to the heel strike (HS) (i.e., the first moment the foot comes into contact with the floor) [59], thus obtaining 428 epochs. About that, the force-sensing resistor of the Lokomat device detected the movement onset of both lower limbs, which was synchronized with the EEG data. Epochs were rejected by using an automatic artifact rejection method (epochs with values of [−100;100]μV, ≥5SD of the mean kurtosis value, ≥5SD of the mean probability distribution, drifts of ≥50 μV/epoch and with a R2 limit ≤0.3, spectra deviating from the mean by ±50 dB in the 0-2 Hz frequency window and by [−100;50]dB in the 20-100 Hz frequency window), visually inspection for artifacts, and if the power perturbation in the 20–40 Hz band deviated by +25 or -100 dB from the baseline at least for one IC [57, 60]. Rejection rate was 5%. This low rate is not surprising, given that it has been shown that scalp EEG recording during low-speed treadmill walking is not invalidated by excessive artefacts [61]. After this, the segmented data were time-warped and averaged together for all strides, so that initial affected-side heel strike, unaffected-side toe off, unaffected-side heel strike, affected-side toe off, and the subsequent affected-side heel strike occurred at the same times [49]. Spectrum analysis was carried using a standard fast Fourier transform (FFT) algorithm (Hanning-window, frequency resolution 0.7 Hz) within ϑ (4-7 Hz), μ (8–12 Hz), β (12–30 Hz), low-γ (Lγ) (31-45 Hz), and high-γ (Hγ) (46-70 Hz) bands [62, 63], and related to the phases of the gait cycle [55]. We opted to analyze these rhythms as it has been reported a different, specific role of each oscillation in sensory-motor pattern [27, 28, 29, 30, 31, 32, 33, 34, 35, 64]. For instance, there is evidence for a difference between the low and high α oscillations, which express action execution and observation, respectively [65, 66].

Single trial spectograms were computed and time-warped (thus aligning the time-points for right and left heel strike) over trials using a linear interpolation function to generate gait cycle ERSPs (i.e., epochs were based on the heel strike events, being the unaffected-side, the affected-side, and the next unaffected-side heel strike time-warped to 0, 50%, and 100% of the gait cycle, respectively). Relative changes in spectral power were obtained by averaging the difference between each single-trial log spectogram and baseline (the mean IC log spectrum over all gait cycles per training) [49]. To visualize significant ERSP changes, deviations from the average gait cycle log spectrum were computed with a bootstrap method [56, 57]. For statistical concern, bandwidth ERSP of each IC were averaged within each 10% of the gait cycle (10-point ERPS curve, frequency resolution 0.7 Hz) [67]. The average log spectrum for all movement cycles was subtracted from the log spectrogram for each movement cycle. We thus calculated the resulting PSD changes from this baseline (defined as the percentage decrement, event-related desynchronization –ERD- and increment, event-related synchronization –ERS- as a function of the percentage of the normalized gait cycle) [53] for each band and electrode-group of interest (with regard to the areas of activation of MNS reported in the literature [68], i.e., ipsi and contralesional frontal -Fp1/F7/F3, Fp2/F8/F4-, central -T3/C3, T4/C4- and parieto-occipital -T5/P3/O1, T6/P4/O2) [69, 70, 71, 72, 73].

Source localization

The source localization approach allows examining brain activities in various sources at different temporal phases of motor control. Because of high temporal resolution of the EEG signals, brain activities before and after the movement onset can be localized in order to distinguish cortical activities related to both motor planning (movement preparation) and motor execution (corticospinal pathway activation). The Estimation of Current Densities was carried by using Low Resolution Brain Electromagnetic Tomography (LORETA; free release of LORETA-KEY alpha-software) [63, 74, 75, 76]. The main components detected with the ICA (signal-to-noise ratio   >  1) were chosen for the source reconstruction. The distributed current density model (LORETA) with L1 norm method (based on the Montréal Neurological Institute (MNI) brain MRI) was then applied to the ICA data [77, 78]. The sources were constrained to the reconstructed layer of the folded cortex [79, 80].

Outcome measures

The primary endpoint, with respect to VR efficacy in post-stroke condition, was the proportion of patients achieving a 20% improvement in lower limb gait and balance at the end of the training, as measured by the Rivermead Mobility Index (RMI), the Tinetti Performance Oriented Mobility Assessment (POMA), and the gait cycle-related ERSPs. Indeed, a 20% improvement correspond to a significant minimal detectable change in RMI [81] and POMA [82]. According to previous work on assisted gait training in post-stroke patients, these changes are paralleled by EEG signal modification of at least 20% to be significant [83, 84].

As secondary outcomes, we considered the global MAS score derived from the muscles of hip, knee, and ankle, the Hamilton Rating Scale for Depression (HRS), the hip and knee flexion/extension force measured by the RAGT device, the extent to which a patient felt him/herself entrained in the VR training (reported on a visual analogue scale -VAS- ranging from zero -not at all- to ten -very much), and the mean of the episodes of drowsiness.

Statistical analysis

The normal distribution of the data was evaluated with the Kolmogorov-Smirnov test. Baseline data were compared between the two groups using a Student t-test for continuous variables if data were normally distributed, whereas a Mann-Whitney U test was used for non-normally distributed ordinal scale. Likewise, Wilcoxon test, Mann-Whitney U test, or t-test were used for within-group and between-group comparisons, depending on the types of data measurements.

The ERD/ERS changes for each frequency band were assessed by means of three-way ANOVA for repeated measures, employing the factor time (two levels: TPRE and TPOST) and electrode-set (three levels: frontal, central, and parieto-occipital) as within-subject factors, and group (two levels: RAGT + VR and RAGT-VR) as between-subject factor. Based on the significance of the F-value, post-hoc paired-sample t-tests were carried out to assess the significance of interactions (Bonferroni correction). A p-value <0.05 was considered significant.

BWS and GF were included as covariates in the ANOVA analysis. In fact, it has been reported that RAGT training usually implies a steady progression of BWS and GF across the training program, so it is necessary to update and analyze these parameters in relation to the assessment of the outcomes [85]. In fact, both BWS and GF can influence spatiotemporal movement characteristics, thus affecting functional gait pattern. Indeed, finely tuning BWS and GF may somehow improve possible spatiotemporal gait asymmetries. On the other hand, missing the correction of these parameters augments inter-limb gait asymmetry for an extended duration in people with stroke [86]. Besides the factor electrode-set (which was employed in the ANOVA analysis to carry the spatio-temporal analysis of EEG signals at scalp level), we also added the factors lesion localization as covariate in the ANOVA analysis (according to the localization within left or right frontal, parietal, occipital, and temporal lobe). Such factor was added to augment inter-subject evaluation, as the sample was non-homogeneous for stroke localization, which can affect EEG signals beyond the overhead electrodes [87], also influencing both motor deficit degree and recovery [88].

ERSPs were computed in each frequency range for RAGT + VR and RAGT-VR using the average gait cycle log spectrum computed from the RAGT-VR as common baseline. The gait cycle was divided into in two stationary (S1, 10–30%, and S2, 60–80%) and two transition phases (T1, 30–60%, and T2, 80–10%). The stationary phases correspond to the midstance (10–30%), initial swing (60–70%), and midswing phases (70–90%), whereas the transition phases correspond to the terminal stance (30–50%), preswing (50–60%), terminal swing (90–100%), and loading response (0–10%) [89]. An ANOVA for repeated measures with the factors time (in relation with the gait cycle phases) (eight levels: two PRE and POST stationary and two PRE and POST transition phases), electrode-set (three levels: frontal, central, and parieto-occipital), and group (two levels: RAGT + VR and RAGT-VR) was computed for each frequency band. Multiple comparisons were corrected controlling for false discovery rate (p < 0.05) [90]. Sphericity assumption violations were Greenhouse-Geisser corrected.

Results

Participant flow

We summarized in the CONSORT flow diagram (Fig. 2): the numbers of participants who were randomly assigned, who received the intended treatment, and who were analyzed for the primary outcome; the losses and exclusions during periods of recruitment, randomization, and follow-up. All treated patients completed the robotic training without reporting any side effect (Fig. 2). The main analysis focused on the consequences of the avatar observation on the RMI, POMA, and gait-related ERS/ERD, and involved all the patients who were randomly assigned to the two groups. Twelve patients were enrolled in each group (RAGT + VR: mean age 60 ± 4 years, 7 males and 5 females, disease duration 8 ± 2 months; RAGT-VR: mean age, 63 ± 6 years, 7 males and 5 females, disease duration 8 ± 2 months) (Table 1). There were no significant differences concerning any parameter between the two groups. Mean and standard deviation values of all outcome measures are reported in Table 1.

Fig. 2

The CONSORT flow diagram

Clinical and kinematic data

RMI improved more in the RAGT + VR than the RAGT-VR group, whereas POMA improved only in the RAGT + VR group (Table 2). HRS score equally decreased in both groups. MAS did not change significantly in both groups. VAS improved only in RAGT + VR group (Table 2). Knee force showed a greater improvement in RAGT + VR than RAGT-VR group, whereas hip force improved only in RAGT + VR group (Table 2).

parameter	Group	PRE	POST	Within–group	Between–group	CI (95%)
RMI	RAGT + VR	8 ± 1	14 ± 1	<0.001	0.001	1.2 to 7.6
	RAGT-VR	7 ± 1	9 ± 1	0.01
POMA	RAGT + VR	17 ± 3	23 ± 3	0.001	0.01	1.2 to 7.6
	RAGT-VR	12 ± 4	15 ± 4
MAS	RAGT + VR	2 ± 0.5	2 ± 0.5
	RAGT-VR	2 ± 0.5	2 ± 0.5
HRS	RAGT + VR	11 ± 3	7 ± 3	0.01
	RAGT-VR	13 ± 3	10 ± 3	0.02
VAS	RAGT + VR	6 ± 1	8 ± 1	<0.001	0.01	1.3 to 15.3
	RAGT-VR	5 ± 1	6 ± 1
drowsiness episodes	RAGT + VR	5 ± 1	2 ± 1	<0.001	0.009	1 to 6.9
	RAGT-VR	5 ± 1	5 ± 1
Hip force	RAGT + VR	36 ± 7	42 ± 3	0.01	0.02	−6.2 to −3.8
	RAGT-VR	34 ± 6	38 ± 10
Knee force	RAGT + VR	31 ± 8	47 ± 6	<0.001	0.02
	RAGT-VR	30 ± 7	36 ± 3	0.04

Legend: RMI Rivermead Mobility Index; POMA Tinetti Performance Oriented Mobility Assessment; MAS Modified Ashwort Scale; HRS Hamilton Rating Scale for depression; VAS visual analogic scale

Electrophysiological data

We found three main areas of brain activation that were more evident in the RAGT + VR group as compared to the RAGT-VR group across the gait cycle (Fig. 3a): (1) BA6 (Tailarach coordinates -x,y,z- 5, −1, 60); (2) BA7 (−14, −56, 53); and (3) BA17 (−20, −88, 3).

Fig. 3

T_PRE-T_POST changes in area activation during the entire gait cycle (panel a) or each phase of gait cycle (S1, T1, S2, and T2) (panel b) as identified by LORETA in RAGT + VR (a) and RAGT-VR (b) groups. Lesioned area is plotted on the left hemisphere. The colors refer to the voxel-by-voxel t-tests values for T_PRE-T_POST changes (see Table 3)

Concerning frontal activation (Fig. 3b; Table 3), RAGT + VR induced significant μ/β ERSP changes from baseline to S1, T1, and S2 phase of the gait cycle, as reflected by the significant interaction between gait phases and training for gait cycle related modulations concerning RAGT + VR group (Table 4). In particular, we observed a frontal ERD before the heel strike, followed by a frontal ERS during the heel strike (Fig. 4; Table 4). This changes also presented a significant difference in scalp projections between the trainings (Table 5; Fig. 5), with a greater magnitude in RAGT + VR group.