Investigating the impact of feedback update interval on the efficacy of restorative brain–computer interfaces

Abstract

Restorative brain–computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.

Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

“…In the current study only one out of 4 participants achieved ARAT improvement beyond MCID limit of 5.7, while in [22] and [46] it was 2 out of 5 and 6 out of 10 respectively. However, the percentage of average improvement in ARAT was comparable to the existing studies [22], [25], [46] and also very close to the MCID limit.…”

“…The average improvement in the ARAT score was 5.66 from the average baseline measurement of 17.25, which is a 32.81% improvement. A study conducted by Darvishi et al [25] achieved 36% improvement in ARAT, whereas Bundy et al [46] reported an average improvement in ARAT of 46.27% (i.e. increase of 6.2 over a baseline 13.4).…”

“…The multimodal feedback comprising of different combinations of visual, auditory and proprioceptive means, happens to be more encouraging for the patients, leading to improved performance [23], [24]. Darvishi et al also found it important to design optimal feedback update interval (FUI) for better rehabilitative outcome during their clinical study on stroke patients [25]. The strategy of attempting a movement rather than only imagining has been suggested by many researchers as it helps subjects to focus more on the movement they are imagining which in-turn improves the BCI performance [26], [1].…”

Reaction Time Predicts Brain–Computer Interface Aptitude

“…Notably, in BCIs for communication, accuracy is a critical measure [43] , whereas, in therapeutic BCIs, both real-time mymargin accuracy and brain facilitation are equally important. For instance, a recent study has demonstrated the significant effect of the FUI value on the efficacy of restorative BCIs for stroke rehabilitation [44] . Overall, our results suggest that FUI customization may benefit both good and poor performers with therapeutic BCIs as well as BCIs for communication.…”

“…The classifier outputs were used to update the flexion angle of the orthoses at every FUI. For further details on the BCI setup refer to [42] , and [44] .…”