Article

Most of the research devoted to BMI development consists of methodological
studies comparing different online mathematical algorithms, ranging from simple
linear discriminant analysis (LDA) (Dornhege et al., 2007)
to nonlinear
artificial neural networks (ANNs) or support vector machine (SVM) classification.
Single cell spiking for the reconstruction of hand movements requires different
statistical solutions than electroencephalography
(EEG)-rhythm classification for
communication. In general, the algorithm for BMI applications is computationally
simple and differences in classification accuracy between algorithms used for a
particular purpose are small. Only a very limited number of clinical studies with
neurological patients are available, most of them single case studies. The
clinical target populations for BMI-treatment consist primarily of patients with
amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord
injuries and stroke resulting in substantial deficits in communication and motor
function. However, an extensive body of literature started in the 1970s using
neurofeedback training. Such training implemented to control various EEG-measures
provided solid evidence of positive effects in patients with otherwise
pharmacologically intractable epilepsy, attention deficit disorder, and
hyperactivity ADHD. More recently, the successful introduction and testing of
real-time fMRI and a NIRS-BMI opened an exciting field of interest in patients
with psychopathological conditions.

Langue : ANGLAIS

Tiré à part : OUI