BCI Based Motor Functional Rehabilitation Design And Clinical Data Analysis

Motor imagery based BCI-FES system is a very promising and novel means forrehabilitation training of strokes, which provides an efective training way for patientstoreconstructthemotorfunctionallinksbetweenactivemotorimageryandmovementsof paralyzed limbs. We introduce a motor imagery based BCI-FES rehabilitation train-ing system, which can apply to daily training of store patients’ rehabilitation and helpthem reconstruct neural circuits on impaired brain cortex and restore neural pathwaybetween brain and limb. The rehabilitation efects of patients demonstrated the feasi-bility of our BCI-FES rehabilitation training system. From data analysis, we observedthe characteristics of EEG data from store patients and designed a series of algorithmsto process those data.The main contributions of this thesis are given as follows:1. System Design. We built a BCI-FES rehabilitation training system with multi-Neurofeedback to attract patients to take part in rehabilitation training. Func-tional Electrical Stimulation (FES) is given to patients according to their cor-responding motor imagery during training, helping patients learn external limbscontrollingthroughsimulatingnormallimbcontrollingprocessofheathypeople.2. Clinical Data Analysis and Functional Rehabilitation Mechanism. We collecteda large number of data during rehabilitation training which can build a databasefor future research. From data analysis, we described varies of characteristics ofEEG data from store patients and reveal mechanisms in rehabilitation.3. Algorithms Design. Motor imagery EEG of stroke patients lacks of specifc dis-criminant features as appearances of healthy people, which signifcantly blocks CSP to seek the optimal projection subspace. A series of algorithms have beendesigned and tested for advanced accuracy improvements and data analysis.In summary, this thesis investigated the mechanisms in stroke brain under cir-cumstances of specifed tasks. A rehabilitation platform is built for clinical test, whichwould be positive in BCI stroke research and development of practical BCI systems.