Abstract: The invention discloses a node multi-electrode resistivity measurement system provided with a transmitter, a PC terminal, acquisition nodes, transmitting electrodes and receiving electrodes. The transmitter, acquisition nodes and PC terminal are connected by Wifi, and the transmitter and acquisition nodes are connected in series by a five-core cable. So the weight of the cable is greatly reduced, and each core can be wrapped by shielding layer, reducing the influence of electromagnetic coupling and ensuring the quality of collected data. The weight of the system is very light so that work efficiency has been improved. Synchronization between transmitter and acquisition nodes is carried out by GPS.

Abstract: The present invention belongs to the technical field of processing and analysis of biomedical signals, and provides a calibration method for the critical point of mental fatigue based on self-organized criticality. It constructs a self-organized criticality model by using the dynamic characteristics of a brain network, and deduces the avalanche dynamics of mental fatigue, which is consistent with the internal mechanism of evolution of fatigue complexity. The critical state of calibration is dynamically stable and robust. Through the verification of the behavior data, the reliability of the critical state of mental fatigue determined from physiological and behavioral dimensions is high, providing support for the setting of the fatigue category labels to complete more accurate classification and recognition.

Abstract: The present invention belongs to the technical field of processing and analysis of biomedical signals, and provides a calibration method for the critical point of mental fatigue based on self-organized criticality. It constructs a self-organized criticality model by using the dynamic characteristics of a brain network, and deduces the avalanche dynamics of mental fatigue, which is consistent with the internal mechanism of evolution of fatigue complexity. The critical state of calibration is dynamically stable and robust. Through the verification of the behavior data, the reliability of the critical state of mental fatigue determined from physiological and behavioral dimensions is high, providing support for the setting of the fatigue category labels to complete more accurate classification and recognition.