CN103815900B - A kind of method of the measurement Vigilance based on brain electricity frequency domain character indexing algorithm - Google Patents
A kind of method of the measurement Vigilance based on brain electricity frequency domain character indexing algorithm Download PDFInfo
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Abstract
The present invention relates to a kind of medicated cap based on brain electricity frequency domain character indexing algorithm measurement Vigilance and method.Described medicated cap also comprises eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, and described harvester is sampled to the simulation brain wave that people's cerebration produces, quantized, and becomes discrete digital signal, carries out follow-up process; The digital signal of quantification amplifies by described signal amplifier, the capacity of resisting disturbance in enhancement process and transmitting procedure; Described signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and therefrom extracts the parameter of reflection people cognitive state change, the state of assessment user; Described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit; Described terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back.Certainty of measurement of the present invention is high can be applied to various working environment.
Description
Technical field
The present invention relates to a kind of method based on brain electricity frequency domain character indexing algorithm measurement Vigilance.
Background technology
Brain electricity can reflect the attention of people or fatigue whether already for everybody known by, and the technology of being correlated with in laboratory environment also unusual maturation.Whether is awakening state significant, as classroom and factory floor etc., especially in driving environment, can report to the police when user is in sleepy, absent minded state or provide prompting at once if understanding tested in many occasions.But due to the signal of telecommunication (microvolt level) that brain electricity is very faint, the process recorded in actual environment is easy to be interfered, tired and the sleepy index of stable sign can be extracted and it is applied and real life or the technology in working very easily, not finding document or patent report.
Summary of the invention
Object of the present invention, provides a kind of medicated cap based on brain electricity frequency domain character indexing algorithm measurement Vigilance and method, objectively to judge, to assess the state of tester and to provide prompting timely.
Technical scheme of the present invention is as follows:
Based on a medicated cap for brain electricity frequency domain character indexing algorithm measurement Vigilance, described medicated cap also comprises eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, it is characterized in that:
Eeg signal acquisition device comprises eeg signal acquisition electrode, EEG signals reference electrode and signal processor, and described harvester is sampled to the simulation brain wave that people's cerebration produces, quantized, and becomes discrete digital signal, carries out follow-up process;
Described IC circuit comprises signal amplifier and signal processor, and described signal amplifier is signal preamplifier, is amplified by the digital signal of quantification, the capacity of resisting disturbance in enhancement process and transmitting procedure;
Described signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and therefrom extracts the parameter of reflection people cognitive state change, the state of assessment user;
Described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit;
Described terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back.
Further, described IC circuit is built in medicated cap inside, is integrated with battery compartment and on and off switch further.
Further, described eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
Further, described signal transmitting apparatus is wireless signal transmission.
Based on a medicated cap for the measurement Vigilance of brain electricity frequency domain character indexing algorithm, it is characterized in that, described method, described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) eeg data is gathered;
(3) to the data filtering gathered, denoising, time frequency analysis, calculating brain electricity index;
(4) brain of calculating electricity index is transferred to terminal and shows;
(5) judge whether to meet acceptable conditions, and then judge whether user is in fatigue state, if it is determined that the fatigue state of being in, then provide warning; If it is determined that be not in fatigue state, then again carry out correlation step to step (2).
6, method according to claim 5, is characterized in that, described indexing algorithm is specific as follows:
(1) pretreatment: carry out digital filtering to the brain wave quantized, removes the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter;
(2) feature representation and extraction: the basic index extracting reflection general cognitive state from the EEG signals after pretreatment, specifically comprise alpha ripple (8-13Hz), beta ripple (13-20Hz), delta (1-4Hz), theta (4-7Hz); Use time-frequency analysis technology these indexs to be extracted from original time domain signal, frequency domain is expressed with the time series form of energy or power;
(3) indexing represents: above-mentioned basic index is carried out standardization, makes the same index of different users and same user different time have identical physical meaning; Described algorithm exports Vigilance level and the horizontal two indices of tensity, the horizontal index of described Vigilance and and the horizontal index of tensity specific as follows:
A (t), b (t) and c (t) represent the clock signal of alpha, beta and theta tri-frequency ranges respectively, and they are respectively by the energy accumulation realization also selecting special frequency channel via time frequency analysis of original EEG signals;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represents the energy of alpha and theta respectively;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represents the energy of beta and theta respectively;
(4) judgement of attention level: namely with normal user when not having sleepy, fatigue state to occur, continue to keep the horizontal index of the attention Vigilance of 2 minutes and and the horizontal index of tensity two index series sequential average 60% as decision threshold, be tired generation lower than this threshold judgement.
Further, in described step (1), the low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
Further, the specific algorithm of described step (2) feature representation submodule is as follows:
Adopt Morlat function to be mother wavelet function, continuous wavelet transform is carried out to brain electricity time-domain signal; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, after converting, multiple time series signal and the wavelet coefficient of a series of different frequency range is obtained with above mother wavelet function convolution, wherein time and input signal length are consistent, frequency range to being 1-35Hz, retain wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, namely represents with power; According to band limits height, extract alpha ripple (8-13Hz), beta ripple (13-20Hz) respectively, delta (1-4Hz), the time series of the mould of the Phase information coefficient of theta (4-7Hz) corresponding frequency band, namely power represents the timing variations of band energy.
Further, described step (3) indexing represents employing feature normalization algorithm, that is:
A certain band energy is accounted for the ratio of gross energy as index:
Wherein, t express time, f represents frequency, and P represents power, thus P
ft () represents the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula then represents the energy accumulation summation in 1 to 35Hz frequency range; According to above model by P
ft (), divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, namely uses P
f' (t) expression.
Beneficial effect of the present invention is:
The present invention is based on brain electricity frequency domain character indexing algorithm measurement Vigilance, certainty of measurement is high, can extract EEG signals accurately and process, and based on brain electricity frequency domain character indexing algorithm, objectively can judge the mental status that user is current, and provide and point out timely.Various working environment can be applied to.
Accompanying drawing explanation
Fig. 1 is device structure schematic diagram of the present invention.
Fig. 2 is method flow schematic diagram of the present invention.
Wherein, 1-eeg signal acquisition electrode, 2-IC circuit, 3-EEG signals reference electrode, 4-medicated cap.
Detailed description of the invention
As shown in Figure 1, it is cap sequence schematic diagram of the present invention, comprise medicated cap, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, eeg signal acquisition device comprises eeg signal acquisition electrode and EEG signals reference electrode, described harvester is sampled to the brain wave that people's cerebration produces, is quantized, become discrete digital signal, carry out follow-up process; These two electrodes are integrated mutually with medicated cap, and in order to gather EEG signals more accurately, eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
IC circuit comprises signal amplifier and signal processor, and described signal amplifier is signal preamplifier, is amplified by the digital signal of quantification, the capacity of resisting disturbance in enhancement process and transmitting procedure; IC circuit is also be built in medicated cap.Signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and therefrom extracts the parameter of reflection people cognitive state change, the state of assessment user; Signal transmitting apparatus connects IC circuit and terminal unit, and the parameter of IC circuit extraction is transferred to terminal unit.Medicated cap is also integrated with battery compartment and on and off switch further.
Terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back.In general, transmission of wireless signals is adopted between terminal unit and IC circuit.
The medicated cap of a kind of measurement Vigilance based on brain electricity frequency domain character indexing algorithm of the present invention, described method step is as follows:
(1) initialization apparatus hardware, setting alert if;
(2) gather eeg data, general collection 1 second simultaneously;
(3) to the data filtering gathered, denoising, time frequency analysis, calculating brain electricity index;
(4) brain of calculating electricity index is transferred to terminal and shows;
(5) judge whether to meet alert if, and then judge whether user is in fatigue state, if it is determined that the fatigue state of being in, then provide warning; If it is determined that be not in fatigue state, then again carry out correlation step to step (2).
Wherein, indexing algorithm is specific as follows:
(1) pretreatment: carry out digital filtering to the brain wave quantized, removes the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter; The low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
(2) feature representation and extraction: the basic index extracting reflection general cognitive state from the EEG signals after pretreatment, specifically comprise alpha ripple (8-13Hz), beta ripple (13-20Hz), delta (1-4Hz), theta (4-7Hz); Use time-frequency analysis technology these indexs to be extracted from original time domain signal, frequency domain is expressed with the time series form of energy or power;
(3) indexing represents: above-mentioned basic index is carried out standardization, makes the same index of different users and same user different time have identical physical meaning; Described algorithm exports Vigilance level and the horizontal two indices of tensity, the horizontal index of described Vigilance and and the horizontal index of tensity specific as follows:
A (t), b (t) and c (t) represent the clock signal of alpha, beta and theta tri-frequency ranges respectively, and they are respectively by the energy accumulation realization also selecting special frequency channel via time frequency analysis of original EEG signals;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represents the energy of alpha and theta respectively;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represents the energy of beta and theta respectively;
(4) judgement of attention level: namely with normal user when not having sleepy, fatigue state to occur, continue to keep the horizontal index of the attention Vigilance of 2 minutes and and the horizontal index of tensity two index series sequential average 60% as decision threshold, be tired generation lower than this threshold judgement.
Wherein, the specific algorithm of feature representation submodule is as follows:
Adopt Morlat function to be mother wavelet function, continuous wavelet transform is carried out to brain electricity time-domain signal; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, after converting, multiple time series signal and the wavelet coefficient of a series of different frequency range is obtained with above mother wavelet function convolution, wherein time and input signal length are consistent, frequency range to being 1-35Hz, retain wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, namely represents with power; According to band limits height, extract alpha ripple (8-13Hz), beta ripple (13-20Hz) respectively, delta (1-4Hz), the time series of the mould of the Phase information coefficient of theta (4-7Hz) corresponding frequency band, namely power represents the timing variations of band energy.
Wherein, described step (3) indexing represents employing feature normalization algorithm, that is:
A certain band energy is accounted for the ratio of gross energy as index:
Wherein, t express time, f represents frequency, and P represents power, thus Pf (t) represents the time dependent function of energy within the scope of a certain frequency f, and the denominator part of formula then represents the energy accumulation summation in 1 to 35Hz frequency range; According to above model by P
ft (), divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, namely uses P
f' (t) expression.
Brain is worked in coordination with level index and is represented:
On the basis of above two basic indexs, we are again according to inherent physiology, psychological pattern that the different rhythm and pace of moving things represents, the brain proposing to utilize the synchronicity between circadian signal to carry out concentrated expression user works in coordination with state, thus whether the state of comprehensive representation cerebral activity is applicable to work.
Index calculate flow process is as follows:
From primary signal, extract alpha and the theta ripple of 8-13Hz and 4-7Hz band limits respectively, represent with a (t) and c (t), wherein t express time.
Respectively Hilbert conversion is carried out to brain electricity range signal a (t) and b (t), obtains its phase signals, phi a (t) and φ b (t), represent the time dependent situation of signal phase;
Calculate the synchronicity index S between alpha and theta energy time sequence, represent in a period of time (representing with Δ t), the phase difference value that two frequency band signals are overall, the i.e. quality of synchronicity, participate in the degree of sustained attention level for weighing full brain, synchronicity is better, the factors such as the cognitive resources more transferring full brain maintains higher attention level, can ensure Vigilance, and customer service is tired, improve the working ability stimulated to external world, thus keep good duty.
The computation model of index S is as follows:
Wherein, S represents the synchronicity index intending calculating, and wherein Δ t represents selected a period of time length, and signal goes out according to the length of this time period step by step calculation from primary signal, and e represents natural constant, and its value is about 2.71828; T represents a certain moment in section seclected time; φ (t) represents the phase information of the rhythm and pace of moving things; Carry out adding up to the difference of the phase place in a period of time and can calculate overall phase synchronism, represent with natural logrithm form and can ensure index between zero and one.
In this patent, seclected time, segment length was 1s, and namely every 1s exports above index S once, to follow the tracks of the change of attention index in real time, was transferred to terminal and was pointed out.
Flow chart can be undertaken by following flow process:
Pretreatment-> feature representation and extraction-> feature normalization-> characteristic index represent
Alpha
Theta
Beta
The first two synthesis A: Alertness;
Latter two synthesizes B: tensity;
First and the 3rd degree of depth synthesis C: concertedness index;
Then threshold discrimination and index output is pointed to.
Claims (4)
1., based on a method for the measurement Vigilance of brain electricity frequency domain character indexing algorithm, it is characterized in that, described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) eeg data is gathered;
(3) to the data filtering gathered, denoising, time frequency analysis, calculating brain electricity index;
(4) brain of calculating electricity index is transferred to terminal and shows;
(5) judge whether to meet acceptable conditions, and then judge whether user is in fatigue state, if it is determined that the fatigue state of being in, then provide warning; If it is determined that be not in fatigue state, then again carry out correlation step to step (2);
Described brain electricity index algorithm is specific as follows:
A () pretreatment: carry out digital filtering to the brain wave quantized, removes the interfering noises such as myoelectricity; Wave filter is infinite-duration impulse response (IIR) band filter;
(b) feature representation and extraction: the basic index extracting reflection general cognitive state from the EEG signals after pretreatment, specifically comprises alpha ripple 8-13Hz, beta ripple 13-20Hz, delta1-4Hz, theta4-7Hz; Use time-frequency analysis technology these indexs to be extracted from original time domain signal, frequency domain is expressed with the time series form of energy or power;
C () indexing represents: above-mentioned basic index is carried out standardization, makes the same index of different users and same user different time have identical physical meaning; Described algorithm exports Vigilance level and the horizontal two indices of tensity, the horizontal index of described Vigilance and the horizontal index of tensity specific as follows:
A (t), b (t) and c (t) represent the clock signal of alpha, beta and theta tri-frequency ranges respectively, and they are respectively by the energy accumulation realization also selecting special frequency channel via time frequency analysis of original EEG signals;
The horizontal index of Vigilance:
S1 (t)=c (t)/a (t), wherein t express time, a and c represents the energy of alpha and theta respectively;
The horizontal index of tensity:
S2 (t)=b (t) * c (t), wherein t express time, b and c represents the energy of beta and theta respectively;
The judgement of (d) attention level: namely with normal user when not having sleepy, fatigue state to occur, continue to keep the horizontal index of the attention Vigilance of 2 minutes and and the horizontal index of tensity two index series sequential average 60% as decision threshold, be tired generation lower than this threshold judgement.
2. method according to claim 1, is characterized in that: in described step (a), and the low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
3. method according to claim 1, is characterized in that:
The specific algorithm of described step (b) feature representation submodule is as follows:
Adopt Morlat function to be mother wavelet function, continuous wavelet transform is carried out to brain electricity time-domain signal; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, after converting, multiple time series signal and the wavelet coefficient of a series of different frequency range is obtained with above mother wavelet function convolution, wherein time and input signal length are consistent, frequency range to being 1-35Hz, retain wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, namely represents with power; According to band limits height, extract alpha ripple 8-13Hz, beta ripple 13-20Hz respectively, the time series of the mould of the Phase information coefficient of delta1-4Hz, theta4-7Hz corresponding frequency band, namely power represents the timing variations of band energy.
4. method according to claim 1, is characterized in that:
Described step (c) indexing represents employing feature normalization algorithm, that is:
A certain band energy is accounted for the ratio of gross energy as index:
Wherein, t express time, f represents frequency, and P represents power, thus P
ft () represents the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula then represents the energy accumulation summation in 1 to 35Hz frequency range; According to above model by P
ft (), divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, namely uses P
f 't () represents.
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| CN104605866A (en) * | 2015-01-21 | 2015-05-13 | 中煤科工集团西安研究院有限公司 | Miner physiological and psychological fatigue monitoring method based on electroencephalogram detection |
| CN104814735A (en) | 2015-05-22 | 2015-08-05 | 京东方科技集团股份有限公司 | Method and device for judging whether brain is tired |
| CN105212924A (en) * | 2015-10-10 | 2016-01-06 | 安徽尚舟电子科技有限公司 | A kind of based on brain wave method for detecting fatigue driving and device thereof |
| CN108577865B (en) * | 2018-03-14 | 2022-02-22 | 天使智心（北京）科技有限公司 | Psychological state determination method and device |
| CN111345818A (en) * | 2018-12-20 | 2020-06-30 | 香港城市大学深圳研究院 | Electroencephalogram signal processing system, engineering safety helmet and method |
| CN116458882B (en) * | 2023-02-09 | 2024-03-12 | 清华大学 | Construction worker attention level calculating method and device |
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