Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study

Abstract

Background: There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to confirm patient engagement during an MI in the absence of any on-line measure. Fortunately an EEG-based brain-computer interface (BCI) can provide an on-line measure of MI activity as a neurofeedback for the BCI user to help him/her focus better on the MI task. However initial performance of novice BCI users may be quite moderate and may cause frustration. This paper reports a pilot study in which a BCI system is used to provide a computer game-based neurofeedback to stroke participants during the MI part of a protocol.

Methods: The participants included five chronic hemiplegic stroke sufferers. Participants received up to twelve 30-minute MI practice sessions (in conjunction with PP sessions of the same duration) on 2 days a week for 6 weeks. The BCI neurofeedback performance was evaluated based on the MI task classification accuracy (CA) rate. A set of outcome measures including action research arm test (ARAT) and grip strength (GS), was made use of in assessing the upper limb functional recovery. In addition, since stroke sufferers often experience physical tiredness, which may influence the protocol effectiveness, their fatigue and mood levels were assessed regularly.

Results: Positive improvement in at least one of the outcome measures was observed in all the participants, while improvements approached a minimal clinically important difference (MCID) for the ARAT. The on-line CA of MI induced sensorimotor rhythm (SMR) modulation patterns in the form of lateralized event-related desynchronization (ERD) and event-related synchronization (ERS) effects, for novice participants was in a moderate range of 60-75% within the limited 12 training sessions. The ERD/ERS change from the first to the last session was statistically significant for only two participants.

Conclusions: Overall the crucial observation is that the moderate BCI classification performance did not impede the positive rehabilitation trends as quantified with the rehabilitation outcome measures adopted in this study. Therefore it can be concluded that the BCI supported MI is a feasible intervention as part of a post-stroke rehabilitation protocol combining both PP and MI practice of rehabilitation tasks. Although these findings are promising, the scope of the final conclusions is limited by the small sample size and the lack of a control group.

Figure 1

An illustration of a Brain-Computer Interface: (a) Main components of a BCI. (b) Timings of a ball-basket game paradigm.

Figure 2

A Type-2 Fuzzy Classifier: (a) A two-dimensional cluster in the feature space and the corresponding T1 fuzzy rule. (b) Footprint of a Gaussian interval type-2 fuzzy set with uncertain mean m∈[m₁,m₂]. (c) Illustrative comparison of a one-rule T2FLS and T1FLS-based classifiers (Δm and Δc define the initial bounds of uncertainty modeled in the system. (d) Structure of a sample T2 fuzzy rule base (the domain of the antecedents' membership functions is normalised).

Figure 3

BCI Classification accuracies over the feedback sessions.

Figure 4

Quantification of synchronized/desynchronized EEG activity within the adjusted μ and β bands over 12 recording sessions for all participants: a) ${\text{ERD/ERS}}_{\text{μ}}^{\text{(C3L)}}$${\text{ERD/ERS}}_{\text{μ}}^{\text{(C4L)}}$, ${\text{ERD/ERS}}_{\text{μ}}^{\text{(C3R)}}$ and ${\text{ERD/ERS}}_{\text{μ}}^{\text{(C4R)}}$ b) ${\text{ERD/ERS}}_{\text{β}}^{\text{(C3L)}}$, ${\text{ERD/ERS}}_{\text{μ}}^{\text{(C4L)}}$, ${\text{ERD/ERS}}_{\text{β}}^{\text{(C3R)}}$, and ${\text{ERD/ERS}}_{\text{β}}^{\text{(C4R)}}$. The ratios in the μ band are represented as ${\text{ERD/ERS}}_{\text{μ}}^{(xy)}$ and that in the β band as ${\text{ERD/ERS}}_{\text{β}}^{(xy)}$, where x may denote the EEG channels C3 or C4 and y may denote either left upper limb MI (L) or right upper limb MI (R).

Figure 5

Recording of rehabilitation outcome measures with respect to time-points wi_j, where i represents the week and j represents the session number: (a) Motricity Index score (/100). (b) ARAT Score (/57). (c) NHPT Score (/6). (d) Grip strength (lbs.).

Figure 6

Monitoring of Fatigue: (a) Visual analog scores (VAS) for fatigue plotted with respect to time-points wi_j, where i represents the week and j the session number. (b) Dependency between CA results and fatigue VAS-plot of the subject-wise CA percentile rank (inter-subject mean with standard deviation) matched with fatigue VAS quartiles (i.e. inter-quartile ranges). (c) Dependency between CA results and fatigue VAS-plot of the inter-subject variance of subject-wise CA percentile ranks matched with fatigue VAS quartiles (i.e. inter-quartile ranges).

Figure 7

Monitoring of Mood: (a) Visual analog scores (VAS) for mood plotted with respect to time-points wi_j, where i represents the week and j the session number. (b) Dependency between CA results and mood VAS-plot of the subject-wise CA percentile rank (inter-subject mean with standard deviation) matched with mood VAS quartiles (i.e. inter-quartile ranges).