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This study introduces a portable multi-channel EEG signal acquisition system. The system is mainly composed of EEG electrode connector, signal conditioning circuit, EEG acquisition part, main control MCU and power supply part. The low-power EEG acquisition front-end ADS1299 and STM32 are used to form the signal acquisition and data communication part. The collected EEG signal can be transmitted to the PC for real-time display. After relevant tests, the system has small volume, low power consumption, high signal-to-noise ratio, and meets the requirements of portable wearable medical devices.
Subject(s)
Electric Power Supplies , Electrodes , Electroencephalography , Signal Processing, Computer-Assisted , Signal-To-Noise RatioABSTRACT
Abstract Introduction Early detection of epilepsy by the review of large electroencephalographic (EEG) recordings is very stressful, time-consuming, and subjective for neurologists. Several automatic seizure detection systems have been proposed in the literature to solve this problem. Methods This study proposes two complementary wavelet-based approaches for detecting epileptiform paroxysms in EEG signals. First methodology applied the wavelet multiresolution analysis (MRA) to filter non-epileptiform activity in long-term EEG. Second methodology used the wavelet dyadic scalogram to analyze which scales were related to the epileptiform paroxysms. For tests, 65 wavelet functions were selected between daubechies, biorthogonal, symlets, reverse biorthogonal and coiflet wavelet families in order to evaluate their performance. Results For MRA, it was noted a better performance by using the db4 function, by reaching 48.30% of energy with 8 wavelet coefficients, 0.717658 of correlation and 36.799 of root mean square error (RMSE). For wavelet dyadic scalograms, were chosen bior3.9 and rbio1.5 functions, by reaching 77.98% of sensitivity, 94.08% of specificity, 87.87% of efficiency and 0.9613 of area under the curve (AUC value) by using bior3.9. Conclusion The presented approaches are highly complementary for a whole automatic seizure detection system by using the MRA as pre-processing stage to filter non-epileptiform activity, and wavelet dyadic scalogram for extracting desired features from filtered EEG signals.
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Objective To investigate the effects of continuous theta burst stimulation (cTBS) on the motor network in the motor area,in order to provide a new way for the basic treatment of motor function.Methods A total of 12 healthy subjects were recruited to stimulate the primary motor areas of the brain by continuous theta burst transcranialmagnetic stimulation,and finger movements were tested before and after stimulation,while the EEG was collected.Pearson correlation method was used to analyze the related characteristics of EEG signals,construct and analyze the functional network of brain,and study the characteristic parameters of brain function network.Results The correlation coefficient matrix,the topological map of the functional network and the parameters of the functional network of the brain were not changed before and after the intervention in the pseudo stimulus group (all P＞0.05);Before stimulation,as compared with after stimulation,cTBS stimulated the motor area,the key button exercise test mean reaction time increased,the degree of network node decreased,the cluster coefficient increased,the network of "small world" property reduced.After cTBS stimulation,there were significant differences (P＜0.05).Conclusion The cTBS mode of transcranial magnetic stimulation (TMS) can change the topological structure and network parameters of brain function network,and improve the motor function.
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This paper reviews the concept and structure of Virtual Instruments and the application of VB6.0 to Virtual Instruments. As an example, an acquisition and procession apparatus of EEG signals based on VB6.0 and virtual instruments is introduced in detail.
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A new multi-channel EEG signal acquisition system,which adopts Cypress's EZ-USB FX2 series USB as its micro-controller,is presented in this paper.The system employs AD620 and OP07 at the pre-amplifying stage and the following two amplifying stages respectively.Universal Series Bus(USB) technology is used to realize the real-time recording,displaying and tele-monitoring of EEG signal,which can also be stored in CF card after compression.The USB software developing tool is WinDriver.