A study published in Spinal Cord indicates that EEG-based brain-computer interface (BCI) has potential, although limited, to help tetraplegics with activities of daily living.
Gelu Onose, the Teaching Emergency Hospital Bagdasar-Arseni, Bucharest, Romania, and others reported that BCI, which uses cerebral motor commands to control robotic devices (eg, a robotic arm), has the potential to improve motor complete tetraplegics’ quality of life. They wrote that this would be mainly in terms of “autonomy and self-esteem, as consequence of regaining even a limited capacity to voluntarily control some common actions, which help fulfil basic needs within activities of daily living.” They added that a previous study have already shown that, via BCI, primates could learn to control a robotic arm without moving their own. In another study, an invasive BCI system called “Brain Gate” was implanted into a chronic tetraplegic patient, who was able to control a cursor to operate a multi-jointed robotic arm device. However, Onose et al wrote that although invasive BCI is of a “higher order of magnitude” than non-invasive BCI, its duration and success in humans is unclear because of a lack of evidence.
Regarding non-invasive BCI systems, according to Onose et al, EEG-BCI is the only “realistically practical non-invasive BCI method at present”. The authors explain: “Alternative imaging modalities, such as functional MRI, magneto-encephalography and positron emission tomography are quite expensive, technically demanding and not portable in terms of electrical energy usage and (except for near infrared spectroscopy) size”. Therefore, these modalities are rather inadequate for BCI systems meant for daily functional assistance, especially in the home.”
Therefore, in their study, Onose et al used a sensory-motor rhythm (SMR) EEG-BCI system in nine patients with chronic (had the condition for more than six months) tetraplegia, motor complete or severe incomplete. The aim was to assess whether the patients could, via the system, use a robotic arm to reach or grab an object.
In the first part of the study, patients underwent a training session in which they were asked to imagine arrhythmic movements of the right hand, left hand, both ankles, and no movement without moving their eyes or limbs. In the second, feedback, part of the experiment, patients were asked move a cursor on screen to a specified target. Eye tracking was also used to provide endpoint information to the robotic arm by inferring the location of the object to be grasped from the gaze focus point concurrent with motor imagination.
In a follow-up questionnaire, 77.7% of patients reported that they felt that were in control of the cursor and 33.3% reported that they were in control of the robot as well. Onose et al reported: “EEG-BCI performance classification trial/EEG-BCI performance accuracy averaged 81% with median 79.2% whereas feedback training performance/feedback training accuracy (in which subjects were asked to reach virtual targets within a maximum of 10 seconds left/right on the display) reached an average of 70.5% with a median 68.8%.”
Onose et al concluded that an EEG-BCI system could be a “valuable method” for compensating for the disability associated with chronic tetraplegics. They added: “The current study promises the development of a rational and effective procedure of screening and training post-SCI tetraplegics for EEG-BCI-BMI [brain machine interface] use, given the reality that only a moderate fraction of these may be able to ultimately benefit.”
