Abstract：To locate the malfunction characteristics of the electroencephalogram (EEG) quickly and accurately under many kinds of
interference, the present study proposed an EEG detection method based on the spectrum feature of hilbert-huang. The signal collection
technology was used for the diagnosis of EEG machine data acquisition and signal fitting.The second-order adaptive IIR trapper
collection was applied to perform the EEG signal interference filter purification processing, and the time-frequency analysis was carried
out on the purification of the output signal. EEG machine fault signal was decomposed into several intrinsic mode functions through
the empirical mode decomposition analysis, and each intrinsic mode components was conducted a Hilbert Huang transform to realize
the Hilbert Huang-spectrum feature extraction. The interference of EEG machine fault detection was finally realized by using the
extracted characteristic as the training sample. The results of simulation showed that using this method to interfere with the accuracy
of the fault detection of EEG machine was good, which had a strong anti-jamming capability and good ability of fault diagnosis.
闫小如，刘军. 基于脑电图机中的干扰故障检测方法研究[J]. 中国医疗设备, 2017, 32(12): 52-56.
YAN Xiaoru, LIU Jun. Research on Interference Fault Detection Method Based on Electroencephalograph. China Medical Devices, 2017, 32(12): 52-56.
