KR20000006606A - Real-time brain wave analysis system using FFT and method thereof - Google Patents

Abstract

PURPOSE: A real-time brain wave analyzing method and a device thereof are provided to display various graphs for brain wave data being measured by a brain wave measuring device for a user to read easily. CONSTITUTION: A method for a real-time analysis of brain wave data includes:a step for storing the received brain wave data in a memory of a computer; a step for high speed Fourier transforming the brain wave data being stored in the memory of the computer; a step for obtaining an average value per time and per frequency band for the high speed Fourier transformed brain wave; and a step for generating a real time graph by using the average value per time and per frequency band. This method can display an analysis of the frequency, the average value per frequency band as well as the brain wave signal of a time area as a frequency/time/brain wave three dimensional graph, an average value/time per frequency band two dimensional graph, and a bar graph per frequency band at the same time.

Description

Real-time brain wave analysis system using FFT and method

The present invention relates to an EEG analysis device and a method thereof, and more particularly, to a real-time EEG analysis device and method for analyzing the real-time EEG data measured by the EEG in real time to provide to the user.

EEG is measured on the human scalp and is a wavelength with a potential difference of several tens of microvolts and a frequency of less than 30 Hz, a physical value that reflects the state of consciousness of the human being. There are four types of brain waves: alpha waves, beta waves, theta waves, and delta waves. A beta wave is a brain wave with a frequency of 13 kHz or higher and is usually related to daily activities when the five senses of humans function. Alpha waves are brain waves with a frequency of 8 to 13 kHz and are associated with relaxed creative states. Theta waves are brain waves with a frequency of 4 to 8 Hz and are often found in adolescents with learning disabilities. Delta waves are brain waves with a frequency of 0.5-4 kHz and are typical in normal sleep. To date, a great deal of research has been done on EEG, but the information contained in EEG has not been sufficiently interpreted, and the reading remains a difficult problem.

EEG reading methods include a reading method in the time domain and an analysis method in the frequency domain. Reading brain waves in the time domain requires much experience and skill, and it is difficult to distinguish minute differences. Current widely used frequency analysis methods require processing of the measured signal so that the subject's condition can be easily read in real time.

The present invention was made to solve the above problems, and an object thereof is to provide a real-time EEG analysis method and apparatus for expressing various graphs that are easy for a user to read with respect to EEG data measured by an EEG measurement device. .

1 conceptually illustrates a usage environment of a real-time EEG analysis apparatus according to an embodiment of the present invention.

Figure 2 is a block diagram showing the configuration of one embodiment of an EEG measuring apparatus for measuring EEG data used in the EEG analysis apparatus of the present invention.

Figure 3 shows the structure of the headband used in the EEG.

4 is a diagram for describing an encoding process by an EEG apparatus.

5 is a block diagram showing a hardware configuration of a real-time EEG analysis apparatus according to an embodiment of the present invention.

Figure 6 is a block diagram showing the functional configuration of a real-time EEG analysis apparatus according to an embodiment of the present invention.

Figure 7 shows an example of the multiple graph displayed on the monitor of the EEG analysis apparatus according to the present invention.

Figure 8 shows another example of the multiple graph displayed on the monitor of the EEG analyzing apparatus according to the present invention.

FIG. 9 illustrates EEG signals on a time axis generated by the multigraph generator of FIG. 6.

FIG. 10 illustrates a bar graph for each frequency domain generated by the multigraph generator of FIG. 6.

FIG. 11 illustrates a three-dimensional graph of EEG amplitudes in the frequency and time planes generated by the multigraph generator of FIG. 6.

FIG. 12 illustrates a two-dimensional graph of EEG average values for an arbitrary time for each frequency region generated by the multigraph generator of FIG. 6.

In order to achieve the above object, the real-time analysis method of the EEG data according to the present invention comprises the steps of storing the received EEG data in the memory of the computer; Converting the EEG data stored in the memory of the computer into Fast Fourier; Obtaining an average value for each time and frequency band of the fast Fourier transformed EEG data; And generating a real-time graph by using the average value for each time and frequency band.

In order to achieve the above another object, the real-time analysis device of the EEG data according to the present invention EEG data receiving unit for receiving the EEG data measured and encoded by an external EEG measurement device; An EEG data conversion unit for decoding the encoded EEG data received by the EEG data receiving unit and converting the decoded EEG data into a fast Fourier; Statistical data generation unit for obtaining the average time value and the average value for each frequency band for the Fast Fourier transformed EEG data; A multigraph generator configured to generate a real-time multigraph using the fast Fourier transformed EEG data, an average value of time, and an average value of frequency bands; And a multigraph output unit for outputting the real-time multigraph on a display screen.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 conceptually illustrates a usage environment of a real-time EEG analysis apparatus according to an embodiment of the present invention. According to FIG. 1, when a subject wears the headband 1 with an electrode attached thereto or attaches the cup electrode 2 to the scalp of the subject, or uses both the headband 1 and the cup electrode, the subjects have a wired line with the electrodes. The connected interface unit 3 amplifies a voltage of several tens of microvolts appearing on the scalp to a voltage of several volts, converts it into a digital value, and encodes the EEG analyzer 4 to read it through the serial port. The EEG analyzing unit 4 obtains the intensity of the EEG for each frequency band by converting the EEG signal into a fast Fourier transform.

According to Figure 2, one embodiment of the EEG measuring device for measuring the EEG data used in the EEG analysis apparatus according to the present invention includes an EEG detecting unit 20, the interface unit 30 and the serial port connection unit 40 It is composed.

The EEG 20 detects EEG signals of four channels at predetermined positions of the subject's scalp using a plurality of electrodes. In an embodiment of the present invention, the EEG 20 includes a head channel of two channels and cup electrodes of two channels. The two-channel headband is for easy measurement of the EEG in the frontal lobe, and the two-channel cup electrodes are for selectively measuring the EEG at a location other than the frontal lobe, that is, the part of the parietal, temporal or occipital lobe to be measured. .

The interface unit 30 is connected to the brain wave detection unit in a wired manner, and a shield wire is preferably used to prevent noise. The interface unit 30 includes an amplifier 31, an analog / digital converter 32, an encoder 33, and a computer interface 34. The amplifier 31 amplifies the amplitude of the weak EEG signals detected by the EEG sensor 20, first filter the EEG signals to remove noise, and then amplify approximately 50,000 times. The analog / digital converter 32 samples the amplified multiple channel EEG signals 120 times per second and converts them into digital values. The encoder 33 sequentially encodes the identifier of each channel and the digital value of 1 byte for each channel in real time. The computer interface unit 34 transmits the digital signal encoded by the encoding unit 33 to the serial port of the computer.

The serial port connection unit 40 is connected to the interface unit 30 by wire, and an RS232-C type 9 pin connector or 25 pin connector for connecting to the serial port of a computer may be used.

Figure 3 shows the structure of the headband used in the EEG. Five gold-plated electrodes attached to the band adhere to the forehead (ie, the frontal lobe) by wearing a headband. Among the five electrodes, the center electrode is used as the ground electrode, the left two electrodes are the electrodes for measuring the EEG of the frontal lobe of the left brain, and the right two electrodes are used as the electrodes for measuring the EEG of the frontal lobe of the right brain. Five wires connected to the electrodes are connected to the amplifier 31 and shield wires are used to prevent noise. The amplifier 31 amplifies the potential difference between the left two electrodes to calculate an EEG signal of one channel, and amplifies the potential difference between the two right electrodes to calculate an EEG signal of another channel.

4 is a diagram for describing an encoding process by an EEG apparatus. After the letter "A", one byte of channel 1 data, after the letter "B", one byte of channel 2 data, after the letter "C", one byte of channel 3 data, and after the letter "D" Causes 1 byte of channel 4 to be located. Here, the identifier for each channel is to minimize the transmission error. The receiver discards data for which the identifier of each channel is not confirmed. The EEG signals encoded as described above are transmitted 120 times per second, and thus, several EEG channels may be transmitted in real time to one serial port of the computer.

One embodiment of the EEG apparatus according to the present invention may be implemented as computer software in the form of computer readable program code executed on a general purpose computer such as the computer 4 shown in FIG. The keyboard 50 and the mouse 51 are connected to the bidirectional system bus 60. The keyboard and mouse are for introducing user input into a computer system and for transmitting the user input to the central processing unit (CPU) 53. Other suitable input devices may be used in addition to or in place of the mouse 51 and the keyboard 50. I / O (input / output) device 58 connected to the bidirectional system bus 60 represents input / output components such as serial / parallel ports and the like.

Computer 3 includes video memory 54, main memory 55, and mass storage 52, all of which, together with keyboard 50, mouse 51, and CPU 53, have a bidirectional system bus ( 60). Mass storage 52 may include fixed and removable media such as magnetic, optical or magneto-optical storage systems or any other available mass storage technology. Bus 60 may have, for example, 32 address lines for addressing video memory 54 or main memory 55. The system bus 60 also uses a 32-bit data bus, for example, for data transfer between components such as the CPU 53, main memory 55, video memory 54 and mass storage 52. It can be provided. Alternatively, multiplex data / address lines can be used instead of individual data lines and address lines.

In one embodiment of the invention, the CPU 53 is a microprocessor or 80x86 or Pentium® manufactured by Motorola®, such as 680x0. Microprocessors manufactured by Intel, such as processors, or SPARC from Sun Microsystems. It is a microprocessor. However, any other suitable microprocessor or microcomputer may be used. The main memory 55 is composed of dynamic random access memory (DRAM). Video memory 54 is dual port random access storage. One port of video memory 54 is connected to video amplifier 56. Video amplifier 56 is well known in the art of computers and may be implemented by any suitable device. This circuit converts the pixel data stored in the video memory 54 into a raster signal suitable for use by the monitor 57. Monitor 57 is a type of monitor suitable for displaying graphical images.

According to Figure 6, the real-time EEG analysis apparatus according to the present invention EEG data receiving unit 61, EEG data conversion unit 62, statistical data generating unit 63, multi-graph generating unit 64 and multi-graph output unit ( 65).

The EEG data receiver 61 receives EEG data measured and encoded by an EEG measuring apparatus as shown in FIG. 2. In one embodiment of the present invention, the EEG data receiver is preferably configured as a serial input and output device of the RS232-C method. The EEG data received by the EEG data receiver 61 is temporarily stored in the main memory 55 of the computer and buffered. The EEG data converter 62 decodes the encoded EEG data received by the EEG data receiver 61 and converts the decoded EEG data into a fast Fourier transform. The statistical data generation unit 63 calculates an average value for each time and an average value for each frequency band of the EEG data obtained from the fast Fourier transform. The multigraph generator 64 generates a real-time multigraph using the fast Fourier transformed EEG data, an average value for each time, and an average value for each frequency band.

The multigraphs generated by the multigraph generator 64 are illustrated in FIGS. 7 and 8. A graph of reference numeral 5 of FIG. 7 shows an EEG signal on a time axis, and is separately shown in FIG. 9. That is, the graph of FIG. 9 uses the brain wave data decoded by the brain wave data converter 62 to show the amplitude of the brain wave according to the change of time for each line, and the y axis shows the amplitude of the brain wave.

7 is a bar graph for each frequency domain and is separately displayed in FIG. 10. According to the graph of FIG. 10, delta waves, theta waves, alpha waves, beta low waves, beta low waves, and beta waves using the hourly average values and the average values of the frequency bands of the EEG data transformed by the statistical data generator 63 using the fast Fourier transform. EEG amplitude, cumulative average value or average value of sampling unit in each frequency domain of high (β high) wave is indicated by the height of bar graph. The instantaneous value at that time may be displayed for each measurement time, or the average value for a predetermined time period designated by the user may be obtained.

7 is a stereoscopic graph of EEG amplitudes in the frequency and time planes, which are separately shown in FIG. In this graph, the x-axis is time, the y-axis is frequency, and the z-axis is amplitude of brain waves. The graph of FIG. 11 is updated by real-time EEG data converted by Fast Fourier by the statistical data generating unit 63, and the update rate and the number of sampling at this time can be arbitrarily adjusted by the user.

The graph of reference numeral 8 of FIG. 8 is separately indicated by FIG. 12 as a two-dimensional graph of the EEG average value for an arbitrary time for each frequency domain. That is, the graph of FIG. 12 shows the temporal change of the average value of the EEG amplitudes in each frequency domain for a user-specified time, with the x axis representing time and the y axis representing the amplitude of the EEG. The amplitude change of the band is shown. This graph displays the average value of each frequency band in real time, making it easy to track changes in EEG of a specific frequency over time. The number and type of frequencies displayed on this graph are determined by the user's choice.

The multigraph generator 64 has the following features.

1. The display unit of each graph is a separate window unit, and the user can selectively open or close it.

2. Up to 4 channels are supported, so you can compare and analyze EEG measurement values of each channel in real time.

3. Various scale functions are supported so that users can operate the graph in a form that is easy to see.

4. The screen output can be displayed or saved in general device-independent format such as document file (.txt), picture file (.BMP), hexadecimal file (.HEX), etc. Can be.

5. It saves and plays back data, so iterative graph output is possible.

An embodiment of the EEG data conversion unit 62, the statistical data generation unit 63, and the multigraph generation unit 64 constituting the diagnostic system of the personal computer according to the present invention are each written as a program that can be executed in a computer system. It is possible. And it can be implemented in a general-purpose digital computer system for operating a program from a medium used in the computer system. The medium may be a magnetic storage medium (e.g., ROM, floppy disk, hard disk, etc.), an optical reading medium (e.g., CD-ROM, D.V.D, etc.) and a carrier wave (e.g. Storage medium).

In addition, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the art to which the present invention belongs.

According to the present invention, in addition to the EEG signal in the time domain, the frequency analysis and the average value for each frequency band are obtained and simultaneously displayed in frequency / time / EEG 3-dimensional graph, average value / time 2-dimensional graph for each frequency band, bar graph for each frequency band, etc. It is easy to grasp in real time.

Claims (6)

Storing the received brain wave data in a memory of a computer; Converting the EEG data stored in the memory of the computer into Fast Fourier; Obtaining an average value for each time and frequency band of the fast Fourier transformed EEG data; And And real-time graph generation using the average value for each time and frequency band. EEG data receiving unit for receiving the EEG data measured and encoded by an external EEG measuring device; An EEG data conversion unit for decoding the encoded EEG data received by the EEG data receiving unit and converting the decoded EEG data into a fast Fourier; Statistical data generation unit for obtaining the average time value and the average value for each frequency band for the Fast Fourier transformed EEG data; A multigraph generator configured to generate a real-time multigraph using the fast Fourier transformed EEG data, an average value of time, and an average value of frequency bands; And And a multigraph output unit for outputting the realtime multigraph on a display screen. The method of claim 2, wherein the multigraph Real-time analysis system of EEG data, characterized in that it comprises a three-dimensional frequency / time / EEG wave in three-dimensional representation by dividing the amplitude of the EEG by height and color on the frequency / time plane. The method of claim 2, wherein the multigraph A real-time analysis system of the EEG data, characterized by obtaining an average value for a user-specified time for the EEG amplitudes for each frequency band, and a mean / time two-dimensional graph for each frequency band showing a temporal change of the average value. The method of claim 2, wherein the multigraph Real-time analysis system of the EEG data, characterized in that it comprises a bar graph showing the EEG amplitude of each frequency band in a bar shape. The method of claim 2, wherein the EEG data receiving unit Real-time analysis system of EEG data, characterized in that the serial input and output device of the RS232-C method.