Brain–computer interfaces (BCI) can detect the neuronal activity of patients’ motor intention to control external devices. With the feedback from the device, the neuronal network in the brain to reorganizes due to neuroplasticity.
Material and method
The BCI controls an avatar and functional electrical stimulation (FES) to provide the feedback. The expected task for the patient is to imagine either left or right wrist dorsiflexion according to the instructions. The training was designed to have 25 sessions (240 trials of either left or right motor imagery) of BCI feedback sessions over 13 weeks. Two days before and two days after we did clinical measures to observe motor improvement. The primary measure was upper extremity Fugl–Meyer assessment (UE-FMA), which evaluates the motor impairment. Four secondary measures were also performed to exam the spasm (modified Ashworth scale, MAS), tremor (Fahn tremor rating scale, FTRS), level of daily activity (Barthel index, BI), and finger dexterity (9-hole peg test, 9HPT).
One male stroke patient (53 years old, 11 months since stroke, and right upper limb paralyzed) participated in the training. He quickly learned to use the BCI and the maximal classification accuracy was over 90% after the 5th session. The UE-FMA increased from 25 to 46 points. The BI increased from 90 to 95 points. MAS and FTRS decreased from 2 to 1 and from 4 to 3 points respectively. Although he could not conduct the 9HPT until 18th training session, he was able to complete the test from 19th session in 10 min 22 s and the time was reduced to 2 min 53 s after 25th session.
The patient could be more independent in his daily activity, he had less spasticity and tremor. Also, the 9HPT was possible to do, which was not before. The system is currently validated with a study of 50 patients.