Multi optimized SVM classifiers for motor imagery left and right hand movement identification.

EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG signals. Brain task identification based on EEG signals is very difficult task and is still challenging researchers.

Fuzzy MUAP recognition in HSR-EMG detection basing on morphological features.

The idea of 'besides the MU properties and depending on the recording techniques, MUAPs can have unique pattern' was adopted. The aim of this work was to recognise whether a Laplacian-detected MUAP is isolated or overlapped basing on novel morphological features using fuzzy classifier. Training data set was constructed to elaborate and test the 'if-then' fuzzy rules using signals provided by three muscles: the abductor pollicis brevis (APB), the first dorsal interosseous (FDI) and the biceps brachii (BB) muscles of 11 healthy subjects.

Effect of contraction force and knee joint angle on the spatial representation of soleus activity using high-density surface EMG.

The meaningful use of surface electromyographic signals (sEMG) is to find an electrode position and orientation in which the sEMG signals can be detected reliably. This becomes more challenging when muscles with pinnate fiber architecture are investigated. In this study, the effects of contraction force and knee inclination on the spatial representation of the soleus muscle activity on the skin surface have been investigated by using two-dimensional electrode grids.