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The fundamental frequency (F0) of human voice is generally controlled bychanging the vocal fold parameters (including tension, length and mass), whichin turn is manipulated by the muscle exciters, activated by the neuralsynergies. In order to begin investigating the neuromuscular F0 controlpathway, we simulate a simple biomechanical arm prototype (instead of anartificial vocal tract) that tends to control F0 of an artificial soundsynthesiser based on the elbow movements. The intended arm movements aredecoded from the EEG signal inputs (collected simultaneously with the kinematichand data of the participant) through a combined machine learning andbiomechanical modeling strategy. The machine learning model is employed toidentify the muscle activation of a single-muscle arm model in ArtiSynth (frominput brain signal), in order to match the actual kinematic (elbow joint angle)data . The biomechanical model utilises this estimated muscle excitation toproduce corresponding changes in elbow angle, which is then linearly mapped toF0 of a vocal sound synthesiser. We use the F0 value mapped from the actualkinematic hand data (via same function) as the ground truth and compare the F0estimated from brain signal. A detailed qualitative and quantitativeperformance comparison shows that the proposed neuromuscular pathway can indeedbe utilised to accurately control the vocal fundamental frequency, therebydemonstrating the success of our closed loop neuro-biomechanical controlscheme.

EEG-to-F0: Establishing artificial neuro-muscular pathway for kinematics-based fundamental frequency control