Identification of Human Gripping-force Control from Electro-encephalographic Signals by Artificial Neural Networks

The exact mechanism of information transfer between different brain regions is still not known. The theory of binding tries to explain how different aspects of perception or motor action combine in the brain to form a unitary experience. The theory presumes that there is no specific center in the brain that would gather the information from all the other brain centers, governing senses, motion, etc., and then make the decision about the action. Instead, the centers bind together, when necessary, maybe through electromagnetic (EM) waves of specific frequency. Therefore, it is reasonable to assume that the information that is transferred between the brain centers is somehow coded in the electroencephalographic (EEG) signals. The aim of this study was to explore whether it is possible to extract the information on brain activity from the EEG signals during visuomotor tracking task. In order to achieve the goal, artificial neural network (ANN) was used to predict the measured gripping-force from the EEG signal measurements and thus to show the correlation between EEG signals and motor activity. The ANN was first trained with raw EEG signals of all the measured electrodes as inputs and gripping-force as the output. However, the ANN could not be trained to perform the task successfully. If we presume that brain centers transmit and receive information through EM signals, as suggested by the binding theory, a simplified model of signal transmission in brain can be proposed. We propose a mathematical model of a human brain where the information between centers is transmitted as phase-modulated signal of certain carrier frequency. Demodulated signals were then used as the inputs for the ANN and the gripping-force signal was estimated on the output. The ANN could be trained to efficiently predict the gripping-force signal from the phase-demodulated EEG signals. Copyright c ©2005 IFAC