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. In this paper, the motor imagery of left and right hand actions are identified using new features which are fed to a set of optimized SVM classifiers. Multi classifiers based classification showed having high faculty to improve the classification accuracy when using different kind or diversified features. Features selection was performed by genetic algorithm optimization. In single optimized SVM classifier, a mean classification accuracy of 89.8% was reached. To further improve the rate of classification, three SVMs classifiers have been suggested and optimized in order to find suitable features for each classifier. The three SVMs classifiers were optimized and achieved a performance mean of 94.11%. The achieved performance is a significant improvement comparing to the existing methods which does not exceed 81% while using the same database. Here, combining multi classifiers with selecting suitable features by optimization can be a good alternative for BCI applications.

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. The proposed fuzzy classier recognized automatically the isolated MUAPs with a performance of 95.03% which was improved to 97.8% by adjusting the certainty grades of rules using genetic algorithms (GA). Synthetic signals were used as reference to further evaluate the performance of the elaborated classifier. The recognition of the isolated MUAPs depends largely on noise level and is acceptable down to the signal to noise ratio of 20 dB with a detection probability of 0.96. The recognition of overlapped MUAPs depends slightly on the noise level with a detection probability of about 0.8. The corresponding misrecognition is caused principally by the synchronisation and the small overlapping degree.

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. Four differently oriented bipolar leads have been calculated to identify not only a proper electrode location but also an adequate orientation of the bipolar lead. Relative measures have been introduced to compare changes in the spatial RMS distribution. It has been shown that in the case of the soleus muscle, bipolar electrodes should be placed on the lateral side. Additionally, the location of the electrodes should be rather proximal than distal, and the orientation of the bipolar lead should be 45° to the lateral side with respect to a line connecting the insertion of the Achilles tendon and the junction between both gastrocnemius heads. Our results have been used to identify adequate electrode locations and orientations in a muscle with such a complex architecture like the soleus muscle. Additionally, new parameters have been introduced, helping to analyze the resulting information about the spatial activation pattern in the soleus muscle.