Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.
In order to investigate the effect of deep brain stimulation on diseases such as epilepsy, we developed a closed-loop electrical stimulation system using LabVIEW virtual instrument environment and NI data acquisition card. The system was used to detect electrical signals of epileptic seizures automatically and to generate electrical stimuli. We designed a novel automatic detection algorithm of epileptic seizures by combining three features of field potentials: the amplitude, slope and coastline index. Experimental results of rat epileptic model in the hippocampal region showed that the system was able to detect epileptic seizures with an accuracy rate 91.3% and false rate 8.0%. Furthermore, the on-line high frequency electrical stimuli showed a suppression effect on seizures. In addition, the system was adaptive and flexible with multiple work modes, such as automatic and manual modes. Moreover, the simple time-domain algorithm of seizure detection guaranteed the real-time feature of the system and provided an easy-to-use equipment for the experiment researches of epilepsy control by electrical stimulation.
We proposed a research of a heart sound envelope extraction system in this paper. The system was implemented on LabVIEW based on the Hilbert-Huang transform (HHT). We firstly used the sound card to collect the heart sound, and then implemented the complete system program of signal acquisition, pretreatment and envelope extraction on LabVIEW based on the theory of HHT. Finally, we used a case to prove that the system could collect heart sound, preprocess and extract the envelope easily. The system was better to retain and show the characteristics of heart sound envelope, and its program and methods were important to other researches, such as those on the vibration and voice, etc.
Using LabVIEW programming and highspeed multifunction data acquisition card PCI6251, we designed an electrocardiogram (ECG) signal generator based on Chinese typical ECG database. When the ECG signals are given off by the generator, the generator can also display the ECG information annotations at the same time, including waveform data and diagnostic results. It could be a useful assisting tool of ECG automatic diagnose instruments.