Brain wave analyzing system and method
First Claim
1. A brain wave analyzing system, comprising:
- an electroencephalograph for detecting brain waves;
an A-D converter coupled to said electroencephalograph for converting the detected brain waves from said electroencephalograph into digital data;
a data operation and processor unit coupled to said A-D converter for calculating and processing said digital data from said A-D converter;
a memory unit for storing decision-criterion data for criticizing any brain wave supplied to said A-D converter and data obtained during the calculating and processing steps of said operation and processor unit;
a controller unit for controlling said data operation and processor unit and said memory unit; and
an output unit coupled to said data operation and processor unit for displaying or recording the output of said data operation and processor unit;
said memory unit including means for storing, as said decision-criterion data for any brain wave to be examined, a mean vector A, a variance vector C and at least one significant level F.sub.α
obtained from F-distribution, said mean vector A being the mean value of autoregressive coefficient vectors calculated by said data operation and processor unit from respective brain waves of the standard brain wave group comprising K number of standard brain waves, said variance vector C being calculated as a function of said mean vector and said autoregressive coefficients, and said F-distribution being a distribution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain wave group;
said data operation and processor unit including means for calculating an L-dimensional row vector X of the brain wave to be examined from the autoregressive coefficients of said brain wave to be examined, means for calculating the distance FX between said row vector X and said mean vector A, means for comparing the value of said distance FX with said significant level F.sub.α
stored in said memory unit, and means responsive to said comparing means for producing a decision output to indicate according to the result of said comparison whether or not the pattern of said brain wave to be examined has a significant level different from that of said standard brain wave group.
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Abstract
A memory unit stores, as decision-criterion data for a brain wave to be examined, a mean vector A, a variance vector C and at least one critical value F.sub.α at an appropriate significant level α obtained from F-distribution, the mean vector A being the mean value of autoregressive coefficient vectors calculated by a data operation and processor unit from respective brain waves of a standard brain wave group consisting of K number of standard brain waves, the variance vector being calculated from the mean vector and the autoregressive coefficients. An L-dimensional row vector X of the brain wave to be examined is calculated by the data operation and processor unit from the autoregressive coefficients of the brain wave to be examined. The distance FX between the row vector X and the mean vector A is also calculated. Further, the value of the distance FX is compared with the significant level F.sub.α to determine whether or not the pattern of the brain wave to be examined has a significant level different from that of the standard brain wave group.
79 Citations
8 Claims
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1. A brain wave analyzing system, comprising:
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an electroencephalograph for detecting brain waves; an A-D converter coupled to said electroencephalograph for converting the detected brain waves from said electroencephalograph into digital data; a data operation and processor unit coupled to said A-D converter for calculating and processing said digital data from said A-D converter; a memory unit for storing decision-criterion data for criticizing any brain wave supplied to said A-D converter and data obtained during the calculating and processing steps of said operation and processor unit; a controller unit for controlling said data operation and processor unit and said memory unit; and an output unit coupled to said data operation and processor unit for displaying or recording the output of said data operation and processor unit; said memory unit including means for storing, as said decision-criterion data for any brain wave to be examined, a mean vector A, a variance vector C and at least one significant level F.sub.α
obtained from F-distribution, said mean vector A being the mean value of autoregressive coefficient vectors calculated by said data operation and processor unit from respective brain waves of the standard brain wave group comprising K number of standard brain waves, said variance vector C being calculated as a function of said mean vector and said autoregressive coefficients, and said F-distribution being a distribution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain wave group;said data operation and processor unit including means for calculating an L-dimensional row vector X of the brain wave to be examined from the autoregressive coefficients of said brain wave to be examined, means for calculating the distance FX between said row vector X and said mean vector A, means for comparing the value of said distance FX with said significant level F.sub.α
stored in said memory unit, and means responsive to said comparing means for producing a decision output to indicate according to the result of said comparison whether or not the pattern of said brain wave to be examined has a significant level different from that of said standard brain wave group. - View Dependent Claims (2)
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3. A brain wave analyzing system, comprising:
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an electroencephalograph for detecting brain waves; an A-D converter coupled to said electroencephalograph for converting the detected brain waves from said electroencephalograph into digital data; a data operation and processor unit coupled to said A-D converter for calculating and processing said digital data from said A-D converter; a memory unit for storing decision-criterion data for criticizing any brain wave group supplied to said A-D converter and data obtained during the calculating and processing steps of said operation and processor unit; a controller unit for controlling said data operation and processor unit and said memory unit; and an output unit coupled to said data operation and processor unit for displaying or recording the output of said data operation and processor unit; said memory unit including means for storing, as said decision-criterion data for any brain wave group to be examined, a mean vector A, a variance vector C and at least one significant level F.sub.α
obtained from F-distribution, said mean vector A being the mean value of autoregressive coefficient vectors calculated by said data operation and processor unit from respective brain waves of a standard brain wave group comprising K number of standard brain waves, said variance vector C being calculated as a function of said mean vector and said autoregressive coefficients, and said F-distribution being a distribution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain wave group;said data operation and processor unit including means for calculating an L-dimensional row vector Xj and the mean vector X of said row vector Xj from the autoregressive coefficients of the individual brain waves among a brain wave group to be examined which comprises J number of brain waves, means for calculating the difference value D between said mean vector X of said row vector and said mean vector A of said standard brain waves, means for calculating, using said difference value D, the distance FD between said mean vector X of said brain wave group to be examined and said mean vector A of said standard wave group, means for comparing the value of said distance FD with said significant level F.sub.α
stored in said memory unit, and means responsive to said comparing means for producing a decision output to indicate according to the result of said comparison whether or not the patterns of said brain wave group to be examined has a significant difference with respect to that of said standard brain wave group. - View Dependent Claims (4)
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5. Method for analyzing brain waves comprising:
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(a) detecting K number of standard brain waves; (b) converting said detected standard brain waves into digital data by means of an A-D (analogue-to-digital) converter; (c) obtaining a mean vector A from said digital data, said mean vector A being the mean value of autoregressive coefficient vectors calculated from the digital data corresponding to said respective standard brain waves; (d) obtaining a variance vector C calculated from said mean vector A and said autoregressive coefficients; (e) setting a least one significant level F.sub.α
obtained from F-distribution, said F-distribution being a distribution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain waves;(f) detecting a brain wave to be examined; (g) converting said detected brain wave to be examined into digital data by means of said A-D converter; (h) obtaining an L-dimensional row vector X of said brain wave to be examined from the autoregressive coefficients of the digital data corresponding to said brain wave to be examined; (i) calculating the distance FX between said L-dimensional row vector X and said mean vector A, said distance FX being calculated from said mean vector A, said variance vector C and said L-dimensional row vector X; and (j) comparing said distance FX with said significant level F.sub.α
for deciding whether said brain wave to be examined belongs to said standard brain waves.
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6. Method for analyzing brain waves comprising:
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(a) detecting K number of standard brain waves; (b) converting said detected standard brain waves into digital data by means of an A-D (analogue-to-digital) converter; (c) obtaining a mean vector A from said digital data, said mean vector A being the mean value of autoregressive coefficient vectors calculated from the digital data corresponding to said respective standard brain waves; (d) obtaining a variance vector C calculated from said mean vector A and said autoregressive coefficients; (e) setting two significant levels F.sub.α
1 and F.sub.α
2 (F.sub.α
1 <
F.sub.α
2) obtained from F-distribution, said F-distribution being a distribution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain waves;(f) detecting a brain wave to be examined; (g) converting said detected brain wave to be examined into digital data by means of said A-D converter; (h) obtaining an L-dimensional row vector X of said brain wave to be examined from the autoregressive coefficients of the digital data corresponding to said brain wave to be examined; (i) calculating the distance FX between said L-dimensional row vector X and said mean vector A, said distance FX being calculated from said mean vector A, said variance vector C and said L-dimensional row vector X; and (j) comparing the value of said distance FX with said two significant levels F.sub.α
1 and F.sub.α
2 for deciding that said brain wave to be examined belongs to said standard brain waves when FX ≦
F.sub.α
2, that said brain wave to be examined does not belong to said standard brain waves when FX >
F.sub.α
1, and that it is impossible to determine whether said brain wave to be examined belongs to said standard brain waves when F.sub.α
2 <
FX ≦
F.sub.α
1.
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7. Method for analyzing brain waves comprising:
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(a) detecting K number of standard brain waves; (b) converting said detected brain waves into digital data by means of an A-D (analogue to digital) converter; (c) obtaining a mean vector A from said digital data, said mean vector A being the mean value of autoregressive coefficient vectors calculated from the digital data corresponding to said respective standard brain waves; (d) obtaining a variance vector C calculated from said mean vector A and said autoregressive coefficients; (e) setting at least one significant level F.sub.α
obtained from F-distribution, said F-distribution being a distritution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain waves;(f) detecting J number of brain waves to be examined; (g) conerting said brain waves to be examined into digital data by means of said A-D converter; (h) obtaining an L-dimensional row vector Xj of said brain waves to be examined from the autoregressive coefficients of the digital data corresponding to the individual brain waves to be examined; (i) calculating a mean vector X of said L-dimensional row vectors Xj from the autoregressive coefficients of the digital data corresponding to the individual brain waves to be examined; (j) calculating the difference value D between said mean vector X of said L-dimensional row vectors Xj and said mean vector A of said standard brain waves; (k) calculating a distance FD between said mean vector X of said brain waves to be examined and said mean vector A of said standard brain waves, said distance FD being calculated by using said difference value D and said variance vector C; and (l) comparing said distance value D with said significant level F.sub.α
for deciding whether said brain waves to be examined belong to said standard brain waves.
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8. Method for analyzing brain waves comprising:
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(a) detecting K number of standard brain waves; (b) converting said detected brain waves into digital data by means of an A-D (analogue to digital) converter; (c) obtaining a mean vector A from said digital data, said mean vector A being the mean value of autoregressive coefficient vectors calculated from the digital data corresponding to said respective standard brain waves; (d) obtaining a variance vector C calculated from said mean vector A and said autoregressive coefficients; (e) setting two significant levels F.sub.α
1 and F.sub.α
2 (F.sub.α
1 <
F.sub.α
2) at significant levels α
1 and α
2 (α
1<
α
2) obtained from F-distribution, said F-distribution being a distribution of a distance between said mean vector A and a row vector X obtained from a given brain wave belonging to said standard brain waves;(f) detecting J number of brain waves to be examined; (g) converting said brain waves to be examined into digital data by means of said A-D converter; (h) obtaining an L-dimensional row vector Xj of said brain waves to be examined from the autoregressive coefficients of the digital data corresponding to the individual brain waves to be examined; (i) calculating a mean vector X of said L-dimensional row vectors Xj from the autoregressive coefficients of the digital data corresponding to the individual brain waves to be examined; (j) calculating the difference value D between said mean vector X of said L-dimensional row vectors Xj and said mean vector A of said standard brain waves; (k) calculating a distance FD between said mean vector X of said brain waves to be examined and said mean vector A of said standard brain waves, said distance FD being claculated by using said difference value D and said variance vector C; and (l) comprising the value of said distance FD with said two significant levels F.sub.α
1 and F.sub.α
2 for deciding that said brain waves to be examined belong to said standard brain waves when FD ≦
F.sub.α
2, that said brain waves to be examined do not belong to said standard brain waves when FD >
F.sub.α
1, and that it is impossible to determine whether said brain waves to be examined belong to said standard brain waves when F.sub.α
2 -FD ≦
F.sub.α
1.
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Specification