Detection of Lower Limb Movements using Sensorimotor Rhythms

Abstract

In contrast to other brain imaging methods, electroencephalography (EEG) has become a feasible method for investigating brain activity and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and high temporal resolution. In this work, sensorimotor rhythms (SMR) signal was utilized to classify ankle joint movements. To achieve this goal the EEG signal in the motor cortex area was measured using 21 electrodes during the motor execution task of ankle joint movements. The event-related (de)synchronization (ERD/ ERS) technique was utilized to quantify the event-related in relation to EEG power changes. Inter and intralimb ankle movements were detected and classified. The results show interlimb movements can be recognized better than intralimb movements. Where the average classification accuracy of the interlimb movements was 89.44 ± 10.26% and 84.83 ± 13.65% for the intralimb movements.