43 results about "Delta wave sleep" patented technology

A portable therapeutic field generator device (10) generates a reduced magnetic field component for a relatively large electric field by virtue that adjacent conductive tracks in a radiating element (40, 50) conduct electrical current in opposing directions. The device (10) includes a processor (12) that, in response to a control algorithm stored in memory (14), supplies a pulse wave (22) to a circuit containing the radiating element (40, 50). The pulse establishes generation of a base heartbeat reference having a frequency around a nominal heart rate. At periodic times, the base heart beat reference is supplemented by mutually exclusive periods of alpha wave, theta wave and delta wave generation (or other selected frequencies below about 200 Hz) based on selected brainwave frequencies for these waves, with the pulses having a duration generally corresponding to that ascribed to neuron depolarisation pulses. The device (10) cycles through multiple cycles (88) and, dependent upon the effect that is desired, may see delta wave frequencies follow theta wave frequencies that follow alpha wave frequencies. Delays may exist between transitions between the generated waves, with delta wave generation occurring no later than sixty minutes from commencement of alpha wave production for a mode that induces sleep. Delta waves production has, typically, a duration that is twice that of either the alpha or theta wave production. A second mode of operation sees longer pulse times for higher frequencies—between about 100 Hz and 500 Hz—associated with muscle stimulation.

The present invention relates to a portable brainwave measuring method, which measures a weak brainwave signal detected from a human scalp through a noninvasive method to measure a degree of concentration through an analysis and process of the detected brainwave signal, and a control method enabled for short distance control of an electronic device or remote monitoring through the internet, by using the brainwave signal. An acceleration sensor is put on the head of a user, and a brainwave detecting means put on the head of a user to detect a brainwave and the acceleration sensor that outputs an acceleration value of three axes including XYZ axes as a signal of predetermined data are used to detect movement of the head, to thereby control the direction and speed of a brainwave-related device.; In more detail, after a signal outputted from the brainwave detecting means is converted into a wireless signal and transmitted, a value of the wireless signal is inputted to a receiving unit of a display device and is expressed numerically. By setting the value inputted to the display device, a portable brainwave measuring device provides accurate device control. A control system is characterized in that a signal according to the slope direction of the head is detected and analyzed, and then numerically expressed by using 6 brainwave signals such as a delta wave (delta), theta wave (theta), alpha wave (alpha), SMR wave, beta wave (beta) and gamma wave (sigma), which are measured through the portable brainwave measuring device and the acceleration sensor put on the head. A wireless system, including a short distance wireless module for transmitting / receiving the wireless signal, includes a wireless receiving module, a signal analyzing unit, and a control output unit. Additionally, the wireless system is connected to a PC or controls a short / long distance external device.

The invention provides a method for determining fatigue state according to electroencephalogram (EEG) which adopts a plurality of electroencephalographs and connecting electrodes for realizing the real time acquisition of electroencephalogram. The method comprises the following steps: running interface programs of a PC and the electroencephalographs; realizing the synchronous acquisition of data by using a VC++ to compile visual interface program of the electroencephalographs under the Windows platform, and displaying EEG waveforms acquired in real-time; pre-processing the acquired data; carrying out the low-pass filtering at 0Hz to 30Hz to the data by an FIR (Finite Impulse Response) filter, so as to eliminate the power frequency noise and external interference; decomposing the filtered EEG waveforms by the blind-source separation method, so as to acquire each component of the mixed signal comprising electro-oculogram (EOG) and left and right brain EEGs; carrying out the fast Fouriertransform (FFT) on the left and right brain EEGs, and converting the time-domain signals to the frequency-domain signals; working out the energy of alpha, beta, theta and delta waves in the EEGs and classifying the BP (back propagation) neural network of the multi-layer perceptron. The invention has the characteristics of directness and rapidness.

The invention discloses a wearable electroencephalogram sleeping quality evaluator. A virtual screen is generated at a position which is two meters away from a human eye by a portable projector, and video simulating signals are provided for sleeping quality evaluation. Audio signals are outputted by an audio interface, and audio simulating signals such as background music and training instructions are provided for sleeping quality evaluation. Electroencephalogram electrodes are placed at the forehead of a brain by the aid of a rotary adjusting rod and a telescopic adjusting rod. The electroencephalogram electrodes transmit acquired brain wave signals into an embedded processing unit, the embedded processing unit performs spectral analysis for the brain wave signals, delta wave components reflecting the sleeping state of the brain are extracted, neural feedback is performed for sleeping quality evaluation, and data are transmitted to a man-machine interaction device through a wireless data interface. Electroencephalograms are used as feedback signals of sleeping quality evaluation, and sleeping quality evaluation efficiency and reliability are greatly improved. By the aid of a wearable computer, many unnecessary interference factors are eliminated, and a training environment is improved.

The invention relates to a method and system for labeling the type of electroencephalographic signal data in a sleep state. The method includes the steps that wavelet decomposition is carried out on an electroencephalographic signal sample, an electroencephalographic signal is reconstructed according to a preset wavelet coefficient in a low-frequency stage, and a low-frequency electroencephalographic signal is obtained; brain waves are extracted from the reconstructed low-frequency electroencephalographic signal; K synthetic waves and delta waves are detected from the brain waves according to the waveform characteristics of the K synthetic waves and the delta waves; the number of the detected K synthetic waves and delta waves is calculated, and when the number exceeds a preset number threshold value, the signal type of the electroencephalographic signal sample is labeled as sleep. By labeling the type of the electroencephalographic signal sample according to the technical scheme, interference elements mixed into the electroencephalographic signal sample can be eliminated, so that a personal classifier trained with the labeled electroencephalographic signal sample is higher in recognition accuracy rate, and the reliability of the detection result of the personal sleep state detected in a later stage is improved.

The invention discloses a magnetic resonance electromyographic signal trigger, comprising a sensor device, a main control unit, a surface electromyography output channel and a nuclear magnetic resonance spectrometer output channel, wherein the sensor device is connected with the main control unit; and the surface electromyography output channel and the nuclear magnetic resonance spectrometer output channel are respectively connected with the main control unit. The trigger only generates a signal the amplitude of which is greater than the threshold upper limit of Vmax; complicated electrocardio signals can be replaced by sine waves, square, waves or triangular waves and the like; meanwhile, simpler trigger signals can ensure the accuracy of the trigger edge, improve the trigger precision and has smaller time error.