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During imagery motor movements tasks, the so called mu and beta event relateddesynchronization (ERD) and synchronization (ERS) are taking place, allowing usto determine human patient imagery movement. However, initial recordings ofelectroencephalography (EEG) signals contain system and environmental noise aswell as interference that must be ejected in order to separate the ERS/ERDevents from the rest of the signal. This paper presents a new technique basedon a reworked Second Order Blind Identification (SOBI) algorithm for noiseremoval while imagery movement classification is implemented using SupportVector Machine (SVM) technique.

Reworked Second Order Blind Identification and Support Vector Machine technique towards imagery movement identification from EEG signals