Brain Computer Interfaces
Brain-computer interface (BCI) technology utilizes brain signals instead of spinal or peripheral motor systems to drive external devices (Birbaumer et al. 2006; Collinger et al. 2013). These devices act as assistive technology to help individuals with SCI complete activities of daily living, without requiring physical movement (Huggins et al. 2015). In order to control a BCI, the user’s brain activity is recorded (via a neural recording device, e.g. EEG) while performing or thinking of performing a motor movement (Collinger et al. 2013; Van Dokkum et al. 2015). After recording brain activity, the information is decoded and turned into visual, auditory or haptic feedback and even the control of external devices to help facilitate movement (Collinger et al. 2013; Van Dokkum et al. 2015). Besides helping to facilitate movement, BCI technology may promote neuroplasticity through the recruitment of brain areas involved in motor planning and execution to operate training devices (Van Dokkum et al. 2015). However, BCI technology has only recently emerged a rehabilitative treatment following SCI, therefore, the evidence base for this intervention rather limited.
The methodological details and results of eight studies evaluating BCI for upper extremity rehabilitation in SCI patients are presented in Table 8.
Discussion
There has been considerable progress in neuroscience and technology, allowing for the development of aids for mobility regeneration. The emergence of neural interface technologies has provided an innovative approach to aid patients with sensorimotor deficits. All of the studies presented in Table 8 demonstrated that the use of BCI technology, although diverse, was feasible. However, the efficacy of BCI technology varied between studies. One randomized controlled trial found that BCI-FES technology not only provided benefit as an assistive device but also improved neurological recovery and muscle strength, possibly through neuroplasticity (Osuagwu et al. 2016). Similarly, Foldes et al. (2015) found that a MEG based BCI improved sensorimotor rhythms to promote neuroplasticity following SCI.
The remainder of articles focused on BCI technology to control external devices. In these studies, it was found that control of a robotic device using BCI technology is feasible and individuals with SCI are interested in using the technology. In a survey that was conducted, 80% of respondents would consider adopting a BCI technology, if it could restore some hand grasp (Blabe et al. 2015). However, it was less likely to be adopted if it was aesthetically unpleasing, unreliable, difficult or embarrassing to use. It should be noted that participant performance on functional tasks was relatively poor. This may be due to the fact that participants needed more time training with the device or that the technology needs to be developed further to provide real benefit for self-assistance. Nonetheless, BCI is a promising rehabilitative device for individuals with SCI.
The importance of BCI applications in the future will depend on their reliability, and technological and functional advantages over conventional technology/rehabilitation. BCI technology has the potential to improve autonomy and independence in basic activities of daily life. For example, simple tasks such as drinking, eating, or moving hair away from the eyes can fundamentally improve quality of life and were identified as the most relevant by a focus group (Collinger et al. 2013). Despite the advantages of this technology, there are some drawbacks including increased donning times, cost and prototype technology that often needs improvement. Future research should focus on determining the long-term effects of BCI use and examine whether this technology could be adapted as a functional rehabilitative device.
Conclusion
There is level 1b evidence (from one randomized controlled trial: Osuagwu et al. 2016) that BCI-FES should be considered as a therapeutic tool rather than solely an assistive device, as combined BCI-FES therapy results in better neurological recovery and muscle strength than FES alone.
There is level 2 evidence (from two prospective controlled trials: Athanasiou et al. 2017; Pfurtscheller et al. 2009) that robotic control of a wireless or EEG controlled BCI is possible in SCI patients, however, multiple training sessions and tailored BCI algorithms are needed to improve performance.
There is level 4 evidence (from one pre-post test: Foldes et al. 2015) that a MEG based BCI may provide realistic, efficient and focused neurofeedback in SCI patients to promote neuroplasticity.
There is level 4 evidence (from one pre-post test: Pedrocchi et al. 2013) that the MUNDUS platform may provide functional assistance in activities of daily living to patients with SCI.
There is level 5 evidence (from two observational studies: Collinger et al. 2013; Blabe et al. 2015) that individuals with SCI are interested in contributing to the design of BCIs and would adopt autonomous BMI systems for control of external devices or the restoration of upper extremity function.
There is level 5 evidence (from one observational study: Onose et al. 2012) that EEG-BCI-mechatronic devices may contribute real but limited potential for self-assistance in individuals with SCI.
