Application of EEMD-ICA algorithm to EMG signals measured in laryngeal muscles
Tomislav Juric, Mirjana Bonković, Maja Rogić Vidaković · 2013
This paper describes the application of EEMD-ICA algorithms on electromyographic signals measured in laryngeal muscles. The method was used for the separation of singlechannel data into independent components. During the speech, there was a transcranial magnetic stimulation of the motor cortex area of the brain for speech production i.e. primary motor region of the laryngeal muscles (M1) and Broca's region. Manifestation of magnetic stimulation of those cortex areas and speech itself is recorded in the form of electromyographic signals in laryngeal muscles. The measured signals are a mixture of two different sources: natural stimulus (speech) and the effect of electromagnetic stimulation depending on the area of the speech cortex that is stimulated. This research demonstrated that using EEMD-ICA method, signal which is a mixture of speech and the effect of electromagnetic stimulation to specific areas of the speech cortex, can be successfully separated to the original components. The results were obtained using Matlab. The impact of magnetic stimulation to brain regions is detected and isolated from the laryngeal muscle signal.