JPH10262943A - Method, instrument for measuring electroencephalogram, method and device for transmitting will using the measuring method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method and a device for measuring electroencephalogram, that can measure target spontaneous electroencephalogram, and a will transmitting device for discriminating the spontaneous will of human being using this measuring method. SOLUTION: The electroencephalogram data of respective channels detected by a cap-shaped electrode 1 are amplified by a multichannel amplifier 2 and converted into digital data by an A/D converter 4. Then, a prescribed frequency component is extracted by a digital band pass filter part 7, and the data of respective channels are temporally weighted by a window function processing part 8 and converted to the data of frequency area by a frequency analytic processing part 9. Then, frequency band differential data and mapping data based on these frequency band differential data are found for each piece of data in the frequency area of each channel by a frequency band differential arithmetic part 10, and an electroencephalogram topographic map based on the mapping data is displayed on a display 6 by an image processing part 11.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an electroencephalogram measurement method and apparatus for measuring spontaneous electroencephalograms of a human body, and a communication device using the electroencephalogram measurement method.

[0002]

2. Description of the Related Art Hitherto, a method and a device for measuring human brain waves and discriminating human intention from the measured brain waves have been proposed. One of the methods and devices of this type is "Method of presenting selected items for communication using event-related brain potential (ERP)" by Nakanaka, Kawamura, Inoue, Kobayashi, Kawakami, and Nakajima (Human Interface,
68-3, September 12, 1996). This method uses an event-related brain potential (hereinafter referred to as ERP: Event Relate b) that appears in a subject's brain wave in response to a sensory stimulus.
rain potential).

[0003] Here, ERP is, in a broad sense, a sensory stimulus,
It refers to any electrical activity of the brain that is recorded in connection with an event, such as exercise or cognitive activity. In a narrow sense, it is triggered by the psychological meaning or type of stimulus that is received when a certain stimulus is received It means the potential that appears in brain waves in relation to cognitive activity.

In the above-described intention determination method, the intention of the subject is determined by extracting a selection reaction potential P300 which is one of the components of the ERP. P300 is a component of the ERP described above, and is a potential induced when a stimulus including information of interest to the subject is given to the subject. For example, when the subject presents a visual stimulus such as a character or a color visual target to the subject, the P300 selects a specific one from the presented text or color visual target and stores it. It occurs mainly in the parietal and median parietal regions. In addition, P300 is 250 to 700 ms from the time when a certain stimulus is given to the subject (hereinafter referred to as a stimulus point).
Appears in EEG after ec.

In the above-mentioned intention determination method, specifically, a visual stimulus is applied to a subject a plurality of times, and brain wave data measured from the median central portion and median parietal portion of the brain in response to the visual stimulus is converted to a center frequency of 4 Hz. After passing through a band-pass filter of (1), averaging is performed. Then, based on the electroencephalogram data obtained as a result of the averaging, 250 to 600 ms from the stimulation point
The peak value between ec and the area of the positive potential portion in the electroencephalogram waveform between them are determined, and the subject's intention to select is determined based on the obtained peak value and area. Further, in this determination method, P300 is emphasized by applying a window function having a large weight at about 300 to 400 msec from the stimulation point to the measured brain wave data, thereby facilitating the detection thereof.

[0006] As another conventional technique, Fujioka, Narazaki, and Fujii, "A communication device using brain waves for visual stimulation" (12th Symposium on Human Interface, Oct. 22-2).
5, 1996, Yokohama). This apparatus is characterized in that the characteristic amount of the EEG observed in the occipital region of the subject (latency about 120 to 140 msec) differs greatly between the case where the inequality judgment task is given to the subject and the case where the square selective task is given. And the intention of the subject is determined by detecting the feature amount.

[0007] Here, the inequality judgment task is to present a very simple correct inequality (for example, “4> 3”) or an incorrect inequality (for example, “6 <3”) to the subject. This refers to the task of determining whether the inequality is right or wrong. The square selective task is a task of randomly presenting, for example, a white square or a red square to a subject, and counting the number of times the white square is presented in the mind.

The above two tasks are presented to the subject, and a question that can be answered with yes / no is given to the subject, and the answer is shown by performing one of the tasks. Then, from the brain waves appearing in the occipital region of the subject, a decision is made based on a human yes / no alternative.

That is, in this communication device, the brain waves collected from the occiput of the subject are added four times, the average is obtained, and an FFT (fast Fourier trajectory) is added to the obtained brain wave pattern.
nsform) processing, and based on the result, it is determined whether the subject has performed an inequality determination task or a rectangular selection task, in other words, yes or no.

[0010]

By the way, in the above-mentioned intention discriminating method and intention transmitting device, P300
In addition, since the brain wave pattern about 120 to 140 msec after the stimulus point is measured by evoked brain waves that appear in response to external stimuli, human decision making is judged, I could not determine my intention. That is, for example, it has not been possible to determine a decision made when the subject moves his or her hands or feet.

In addition, since human brain waves are very weak electric signals and include a random brain wave waveform called brain noise, the S / N ratio is hard to be obtained, and the target brain wave data cannot be collected. Very difficult. In the above-described intention discrimination method and communication device, a specific evoked brain wave is measured, and since the stimulation point and the time of occurrence of the target evoked brain wave are clear, a method of averaging the measured brain waves is used. Can be used to obtain a sufficient S / N ratio. However, when determining the subject's spontaneous intention, it is necessary to measure brain waves generated during the subject's spontaneous actions (hereinafter, referred to as spontaneous brain waves),
Even if the averaging is performed to detect the brain wave as in the conventional brain wave detection method and the brain wave detection device, the noise is eventually added, and it is impossible to detect the intended spontaneous brain wave. there were.

The present invention has been made in view of the above-mentioned circumstances, and has an electroencephalogram measuring method and apparatus capable of measuring a spontaneous electroencephalogram as a target, and a human voluntary intention using the measuring method. It is an object of the present invention to provide a communication device that enables discrimination.

[0013]

According to a first aspect of the present invention, a plurality of brain wave data are detected on a scalp surface, a predetermined frequency component is extracted from each of the plurality of detected brain wave data, and the predetermined frequency component is extracted. After performing a common temporal weighting on each of the frequency component data, the data is converted into frequency domain data, frequency band difference data is obtained for each of the frequency domain data, and based on the frequency band difference data, And displaying an electroencephalogram topography.

According to a second aspect of the present invention, a plurality of electroencephalogram detection means are arranged on the scalp surface and detect electroencephalogram data at each location, and a plurality of electroencephalogram data detected by the plurality of electroencephalogram detection means are used. Band-pass filter means for respectively extracting brain wave data in a predetermined frequency region; weighting means for performing common temporal weighting on brain wave data in each predetermined frequency region extracted by the band-pass filter means; Means for converting each brain wave data weighted by the means into data in the frequency domain; and obtaining frequency band difference data based on the data in the frequency domain, and creating an electroencephalogram topography from the obtained frequency band difference data. Mapping data generating means for generating mapping data of A brain wave measuring apparatus characterized by comprising a display means for displaying the brain wave topography.

According to a third aspect of the present invention, in the electroencephalogram measurement apparatus according to the second aspect, the display means displays a predetermined number of electroencephalographic topographs at predetermined time intervals in a time series.

According to a fourth aspect of the present invention, a head electroencephalogram change including an electroencephalogram change occurring in a primary motor area, a gait motor area, and a premotor area is obtained from brain wave data detected from a plurality of locations on a scalp surface. Detecting as distribution information, comparing the time-series change pattern of this plane distribution information with the change pattern systematized in advance in association with the movement of each part of the human limb, and intending to move the human limb and trying to move This is a communication method characterized by judging a part that is present and outputting a control signal for controlling various devices based on the judgment result.

According to a fifth aspect of the present invention, there is provided a communication device for generating a control signal for controlling a controlled device in accordance with human intention, wherein a plurality of control signals are arranged on a scalp surface, and the electroencephalogram data at each of the arrangement locations is provided. A plurality of electroencephalogram detection means, a bandpass filter means for respectively extracting electroencephalogram data in a predetermined frequency region from each electroencephalogram data detected by the plurality of electroencephalogram detection means, and Weighting means for performing common temporal weighting on brain wave data in a predetermined frequency domain; converting means for converting each brain wave data weighted by the weighting means into frequency domain data; The frequency band difference data is obtained based on the EEG, and an EEG topography is created from the obtained frequency band difference data. Mapping data generating means for generating mapping data of the same, change pattern recognizing means for recognizing a change pattern of the mapping data over time, and the change pattern systematized in association with the intention to start exercise of each part of the human limb A storage unit that previously stores a plurality of change patterns equivalent to the above, a change pattern recognized by the change pattern recognition unit, and a plurality of change patterns stored in the storage unit are compared, and there is a matched change pattern. A comparison indicating means for outputting a signal indicating that the intention to start exercise has occurred, and information indicating a part of a limb associated with the coincidence change pattern stored in the storage means; and Control signal generating means for generating a control signal for the controlled device in accordance with the information and the signal. Characterized in that it.

[0018]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. Such an embodiment shows one aspect of the present invention, and does not limit the present invention in any way, and can be arbitrarily changed within the scope of the present invention. FIG. 1 shows a configuration of an electroencephalogram measurement apparatus according to the present embodiment. In this figure, reference numeral 1 denotes a cap-type electrode mounted on the subject's head. In the case of the present embodiment, the cap 1a of the cap-type electrode 1 has 31 electrodes 1b, 1
are provided, and these electrodes are brought into contact with the scalp of the subject to detect brain waves at various parts of the head.

Here, the arrangement of the above-mentioned electrodes is shown in FIG.
This will be described with reference to FIG. FIG. 2 is a diagram showing an electrode arrangement when the subject's head is looked down from above with the cap-type electrode 1 attached. In this figure, each electrode position is shown in a form developed on a two-dimensional plane. I have. Also, in the figure, the upper side is the front (that is, the face side), and the lower side is the back (that is, the back of the head). In the present embodiment, each electrode is composed of 19 sites (PF1, PF2,
F3, F4, F7, F8, Fz, C3, C4, Cz, P3, P4, Pz, T3, T4, T5, T6, O1, O2
) And 12 sites according to the method combinatorial nomenclature (AF3, AF4, FC1, FC2, FC5, FC6, CP1,
CP2, CP5, CP6, PO3, PO4). In the same figure, the site where the electrode was not arranged (A, B, C, D,
The potentials of Fpz, Oz, AF7, AF8, FT9, FT10, TP9, TP10, PO7, and PO8) can also be calculated from the potentials of neighboring electrodes by mathematical interpolation.

Returning to FIG. 1, reference numeral 2 denotes a multi-channel amplifier, and 31 electrodes 1b, 1b,
Are amplified individually and output as 31-channel EEG data. Reference numeral 4 denotes an A / D converter which samples 31-channel brain wave data output from the multi-channel amplifier 2 at a predetermined sampling period, and converts the data into digital data. Here, in the present embodiment, the sampling period is set to 5 msec.

Reference numeral 5 denotes a signal processing device, which is an A / D converter 4
Various digital processing is performed on the digital data of each channel output from, and an electroencephalography map based on the collected electroencephalogram data is displayed on the display 6. here,
The electroencephalogram topographic map is a map in which electroencephalograms measured by the respective electrodes are displayed as a potential activity on the scalp surface in a planar equipotential diagram. The electroencephalogram topography map presents brain waves as images that can be understood in association with brain structures, and is useful in that the activity of each part of the brain and the movement of the active part over time can be easily observed. .

In the signal processing device 5 described above, first,
The digital data of each channel output from the A / D converter 4 is input to the digital bandpass filter unit 7. The digital band-pass filter unit 7 uses a non-recursive ideal filter, and the pass band is within a range of up to a half of the sampling frequency and is a firing site of a neuron which is a source of an electroencephalogram to be detected. Or a frequency band corresponding to the operation pattern. By passing through the digital bandpass filter unit 7, thereafter, digital processing is performed only on the frequency components of the pass band set in the digital bandpass filter unit among the digital data of each channel.

The digital data of each channel output from the digital bandpass filter unit 7 is subjected to window function processing in the window function processing unit 8 in units of 512 points for each channel. Here, the time between each point is equal to the sampling period, and therefore, 512 points are 2.56 seconds in time. Thereby, common temporal weighting is performed on the data of each channel.

FIG. 3 shows a window function used in the above-described window function processing. In this figure, the vertical axis indicates a weighting coefficient, and the horizontal axis indicates points. As can be seen from this figure, the center point (256th point) of the digital data for 512 points on which the window function processing is performed is given the highest weight. By performing this window function processing, in the processing performed thereafter,
It is less susceptible to digital data that deviates from the center point.

The window function process is performed while shifting one point at a time for each sampling timing in units of 512 points from the measurement start time. That is, after the measurement starts, the window function processing shown in FIG. 3 is performed on the data from the first point to the 512th point, and the same window function is performed on the data from the second point to the 513th point at the next sampling timing. An operation of performing processing is sequentially performed.

Next, the data for each 512 points weighted by the window function in FIG. 3 is converted from time domain data to frequency domain data by the frequency analysis processing unit 9 at each sampling timing. Here, the fast Fourier transform is used, but other various frequency analysis methods (for example, a maximum entropy method (maximum entropy method)) are used.
method)) is available. Then, the data converted into the frequency domain data of 512 points at each sampling timing is sequentially input to the frequency band difference calculation unit 10, and the frequency band difference data is obtained by the following equation (1).

(Equation 1)

In the equation (1), i is the sampling timing, f (i) is the frequency band difference data at the sampling timing i, and Fn is the maximum level value of the frequency domain data of each of the 31 channels at each sampling timing.

According to the above equation (1), the frequency band difference calculating section 10 calculates the frequency band difference data for each channel,
In other words, frequency band difference data respectively corresponding to the electrodes 1b, 1b,... Is obtained. That is, the maximum level average value of the frequency domain data from the sampling timing i + 1 to i + 100 and the maximum level average value from the sampling timing i to i + 99 are obtained for each electrode,
The difference is obtained at each sampling timing. As described above, by obtaining the frequency band difference data, the influence of the individual difference (DC level) of the electroencephalogram of the subject is reduced, and the peripheral region in the electroencephalogram topography map obtained by the electroencephalogram measurement apparatus of the present embodiment is emphasized. be able to.

In the right side of the equation (1), the number of samples to be averaged is 100. However, this number may be changed as appropriate in accordance with the processing capability of the frequency band difference calculator 10. That is, for example, when the number of samples to be averaged is 150, the sampling timing i + 1 to 1
The value obtained by subtracting the maximum level average value of the sampling timings i to i + 149 from the maximum level average value of the frequency domain data of i + 150 is defined as frequency band difference data.

The frequency band difference data between each electrode position (between measurement points) is interpolated based on each frequency band difference data, and is output to the image processing unit 11 as 48 × 48 matrix mapping data. Then, the image processing unit 11 sequentially generates 48 × 48 matrix image data based on the input mapping data, and further generates image data in which a predetermined number of matrix image data are arranged in a time series at predetermined time intervals. It is output to the display 6.

Here, an example of an image displayed on the display 6 is shown in FIG. In this image example, in the figure, 12 matrix images obtained in the order of A to ヲ are displayed in time series at 100 ms intervals. Further, each matrix image shows an electroencephalogram topographic map in a frequency band set by the band-pass filter unit 7, and in that frequency band, a part of the brain in which the brain potential has changed within one sampling cycle time is shown. The color and density are displayed according to the amount of change. For example, a portion where the brain potential has increased within one sampling cycle time is displayed in blue, and a portion where the brain potential has decreased is displayed in red, and the degree of change (the amount of change) is represented by the shading of each color. By performing such display, the level change amount of spontaneous brain waves in each part of the brain,
Further, it is possible to more clearly grasp the transition of the change amount over time.

In the above-described brain wave measuring device,
Electrodes were used as means for detecting brain waves, but the invention is not limited to this. For example, means for detecting brain waves using infrared light radiated from the scalp, and non-contact sensors such as SQUIDs using superconducting magnets May be used to detect brain waves. Also, the number of measurement channels is not limited to 31 channels, but may be more or less. When the number of measurement channels is small, the number of measurement channels can be apparently increased using interpolation from adjacent points.

Further, a personal computer may be used in place of the signal processing device 5, and the same processing as that in each section of the signal processing device 5 may be performed by executing a program. Further, the program may be provided by a recording medium such as a floppy disk, a CD-ROM, a DVD (digital video disk), and a tape for a DAT (digital audio tape).

The formula for calculating the frequency band difference data in the frequency band difference calculation unit 10 may be obtained by the following formula (2) in addition to the above formula (1).

(Equation 2)

In the equation (2), the first term F on the right side
_{(i + 100)} indicates the maximum level value of the frequency domain data of each of the 31 channels at the _{(i + 100)} th sampling timing. In the second term on the right side, the maximum level average value of the frequency domain data from sampling timing i to i + 99 is calculated. Therefore,
In equation (2), from the maximum level value of the frequency domain data acquired at the (i + 100) th sampling timing, i
The frequency band difference data is obtained by subtracting the average value of the maximum level value of the frequency domain data acquired at the (i + 99) th sampling timing.

When the frequency band difference data is calculated using the equation (2), the S / N ratio for detecting an electroencephalogram pattern can be further improved as compared with the equation (1). In the second term on the right side of the equation (2), the number of samples to be averaged is set to 100, but this number may be appropriately changed according to the processing capability of the frequency band difference calculation unit 10 and the like. In this case, the sampling timing of the maximum level value of the frequency domain data of the first term on the right side is also changed according to the number of samples. That is, for example, if the number of samples is 150
In this case, the first term on the right side uses the maximum level value of the frequency domain data of each of the 31 channels at the (i + 150) th sampling timing.

Next, a communication device using the above-described brain wave measuring device will be described. The intention communication device described below determines a person's intention to exercise before a person exercises, and drives the control means of various devices based on the result, thereby transmitting the person's exercise intention. Device. Hereinafter, the principle of this communication device will be described.

In general, when a person exercises spontaneously, a change in the brain activity in each region called a primary motor area, a gait motor area, and a premotor area occurs recently prior to the start of the exercise. Research has revealed. Further, according to an experiment performed by the present applicant, in the electroencephalogram topography map measured by the electroencephalogram measurement device described above, a remarkable change was observed in the frontal lobe centering on the motor cortex before the subject actually exercised. At the same time, it became clear that the electroencephalographic topographic map at that time was clearly different depending on the part of the limb that attempted to exercise.

Therefore, the communication device described below measures head electroencephalogram changes including electroencephalogram changes that occur in the primary motor area, the gait motor area, and the premotor area by measuring surface distribution information (that is, by measuring the above-described electroencephalogram measurement apparatus). EEG topography map), and compares the time-series change pattern of the surface distribution information with the change pattern systematized in advance in association with the movement of each part of the human limb, and intends to move the human limb ( Hereinafter, this will be referred to as an exercise start intention), and further, a part to be moved is determined, and various devices are controlled based on the determination result.

Next, the configuration of the communication device will be described with reference to FIG. Note that, in this figure, the same reference numerals are given to the components corresponding to each unit of the electroencephalogram measurement device shown in FIG. 1 and the description thereof is omitted. Further, in the intention communication device shown in FIG. 5, the portions different from the electroencephalogram measurement device in FIG. 1 are as follows. Here, in the communication device described above, the pass band of the band-pass filter unit 7 should be experimentally adjusted according to the detection content as described above, but is not used in the conventional detection. 30
Finally, the frequency is set to 20 Hz to 50 Hz, including a high frequency band from 50 Hz to 50 Hz.

Reference numeral 12 denotes a change pattern recognizing unit, which outputs a frequency of 20 Hz to 50 Hz output from the frequency band difference calculating unit 10.
Based on the mapping data of the 48 × 48 matrix at the frequency of の, a change pattern of the mapping data over time (hereinafter, simply referred to as a change pattern) is recognized. Reference numeral 13 denotes a change pattern storage unit, which previously stores a plurality of change patterns systematically obtained in association with the intention to start exercise of each part of the human limb obtained by an experiment or the like.

A comparison / determination unit 14 compares the change pattern recognized by the change pattern recognition unit 12 with the change pattern stored in the change pattern storage unit 13 sequentially, and recognizes the change pattern by the change pattern recognition unit 12. When a change pattern that matches the change pattern is present in the change patterns stored in the change pattern storage unit 13, the exercise start intention generation signal indicating that the exercise start intention has occurred and the change pattern storage unit 13 It outputs the stored part information indicating the part of the limb associated with the matching change pattern.

Reference numeral 15 denotes a control signal generator, which controls the controlled equipment (for example, a computer, a video game machine, an electronic musical instrument, an artificial hand, etc.) in accordance with the site information output from the comparison / determination section 14 and the exercise start intention generation signal.・ Generate a control signal for the prosthesis.

For example, when the subject wearing the cap-type electrode 1 attempts to move the right hand, the change pattern recognizing unit 12 recognizes the change pattern of the mapping data accompanying the manifestation of the intention by the above-described communication device. You. Then, the recognized change pattern is compared with the comparison judgment unit 1.
In 4, the change patterns are sequentially compared with the change patterns stored in the change pattern storage unit 13, and as a result, the intention to start exercise and the part of the limb to exercise (the right hand in this case) are determined. As a result, the control signal generator 15 outputs a control signal corresponding to a process or operation to be performed when the subject moves his / her right hand to a controlled device (not shown).

As described above, by the communication device described above, before the subject actually exercises, various devices are operated.
It is possible to output a control signal according to a part to be exercised and its intention. As a result, for example, it becomes possible to control a computer, a video game machine, an electronic musical instrument, or the like at will, or to realize a prosthetic hand or leg that operates smoothly.

As the change pattern storage unit 13 and the comparison judgment unit 14 of the communication device described above, a learning judgment structure such as a neural network can be used. The configuration of a neural network such as a parsevtron is an electrical thinking judgment circuit that simulates the neural configuration of a living body,
Generally, it consists of an input layer, an intermediate layer, and an output layer. If the input layer and the output layer are repeatedly learned to add a time series to the part information of the limb to be actually exercised and the mapping data actually obtained at that time, the intermediate layer The association information based on the learning result is accumulated in the structure, which becomes the change pattern stored in the change pattern storage unit 13, and can also function as the comparison determination unit 14.

[0047]

As described above, according to the first and second aspects of the present invention, brain wave data at a plurality of locations are detected on the scalp surface, and a predetermined number of each of the detected brain wave data are detected. After extracting a frequency component and performing a common temporal weighting on each of the predetermined frequency component data, the data is converted into frequency domain data, and frequency band difference data is obtained for each of the frequency domain data. ,
Since the electroencephalogram topography map is displayed based on the frequency band difference data, the influence of the electroencephalogram individual difference is reduced, and the peripheral region of the electroencephalogram topography map can be emphasized, and the degree of change in brain activity can be increased. Since the brain wave topography map created by the display is displayed, it is possible to more clearly grasp the level change amount of the spontaneous electroencephalogram in each part of the brain, and the transition of the change amount over time,
Detection and analysis of the target spontaneous electroencephalogram becomes easy.

According to the third aspect of the present invention, a predetermined number of the brain wave topographs at predetermined time intervals are displayed in a time series, so that the level change amount of spontaneous electroencephalogram at each part of the brain and the time It is possible to more clearly grasp the transition of the change amount with the lapse of time.

Further, according to the present invention, the electroencephalogram change occurring in the primary motor area, the gait motor area, and the premotor area is obtained from the electroencephalogram data detected from a plurality of locations on the scalp surface. Detects changes in the head brain waves including surface brain wave information as surface distribution information, compares the time-series change pattern of this surface distribution information with the change pattern systematized in advance in association with the movement of each part of the human limb, and moves the human body limb Judgment of the intention to do so and the part to be moved are made, and control signals for controlling various devices are output based on the result of the judgment, so the part to be exercised before humans actually exercise. Also, a control signal according to the intention can be output to various devices. As a result, for example, it becomes possible to control a computer, a video game machine, an electronic musical instrument, or the like at will, or to realize a prosthetic hand or leg that operates smoothly.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration of an electroencephalogram measurement apparatus according to an embodiment of the present invention.

FIG. 2 is a plan view for explaining an arrangement of each electrode in a cap-type electrode of the electroencephalogram measurement apparatus.

FIG. 3 is a graph showing a window function used in a window function processing unit of the electroencephalogram measurement apparatus.

FIG. 4 is an explanatory diagram for explaining an example of display contents of a measurement result by the same electroencephalogram measurement device.

FIG. 5 is a block diagram showing a configuration of a communication device using a part of the brain wave measuring device.

[Explanation of symbols]

1 ... cap-type electrode, 2 ... multi-channel amplifier, 3 ...
... Relay box, 4 ... A / D converter, 5 ... Signal processing device, 6 ... Display unit, 7 ... Digital bandpass filter unit, 8 ... Window function processing unit, 9 ... Frequency analysis processing unit Reference numeral 10: Frequency band difference calculation unit, 11: Image processing unit, 12: Change pattern recognition unit, 13: Change pattern storage unit, 14: Comparison judgment unit, 15: Control signal generation unit.

Claims (5)

[Claims] 1. A method for detecting brain wave data at a plurality of locations on the surface of a scalp, extracting predetermined frequency components from the detected plurality of brain wave data, and setting a common time for each of the predetermined frequency component data. EEG measurement after converting the data into frequency domain data, obtaining frequency band difference data for each of the frequency domain data, and displaying an electroencephalography topography based on the frequency band difference data. Method. 2. A plurality of electroencephalogram detection means arranged on the scalp surface for detecting electroencephalogram data at each location, and a method for converting electroencephalogram data in a predetermined frequency region from each electroencephalogram data detected by the plurality of electroencephalogram detection means. Band-pass filter means for extracting each; weighting means for performing common temporal weighting on the brain wave data of each predetermined frequency region extracted by the band-pass filter means; each brain wave weighted by the weighting means Conversion means for converting data into frequency domain data; mapping for obtaining frequency band difference data based on the frequency domain data; and generating mapping data for creating an electroencephalography topography from the obtained frequency band difference data. Data generation means; and an electroencephalogram topography based on the mapping data. Electroencephalogram measurement apparatus characterized by comprising a Shimesuru display means. 3. The electroencephalogram measurement apparatus according to claim 2, wherein the display unit displays a predetermined number of electroencephalogram topographies every predetermined time in a time series. 4. A brain wave change including a brain wave change occurring in a primary motor area, a gait motor area, and a premotor area is detected as surface distribution information from brain wave data detected from a plurality of locations on a scalp surface. Compare the time-series change pattern of the surface distribution information with the change pattern systematized in advance in association with the movement of each part of the human limb, determine the intention to move the human limb and the part trying to move, A communication method comprising: outputting a control signal for controlling various devices based on a determination result. 5. A plurality of electroencephalograms which are arranged on a scalp surface and which detect electroencephalogram data at each location, wherein the plurality of electroencephalograms are arranged on a scalp surface. Means, band-pass filter means for respectively extracting brain wave data in a predetermined frequency region from each of the brain wave data detected by the plurality of brain wave detection means, and brain wave data in each of the predetermined frequency regions extracted by the band-pass filter means. Weighting means for performing common temporal weighting; conversion means for converting each brain wave data weighted by the weighting means into frequency domain data; and frequency band difference data based on the frequency domain data. And mapping data for creating an electroencephalogram topography from the obtained frequency band difference data Mapping data generating means for generating; a change pattern recognizing means for recognizing a change pattern of the mapping data over time; and a change equivalent to the change pattern systematized in association with an intention to start exercise of each part of a human limb. A storage unit storing a plurality of patterns in advance; and comparing the change pattern recognized by the change pattern recognition unit with the plurality of change patterns stored in the storage unit. And a signal indicating that a limb has occurred, and information indicating a limb site associated with the coincidence change pattern stored in the storage means, and information and a signal output from the comparison and determination means. Control signal generating means for generating a control signal for the controlled device in accordance with That intention transmission device.