CN108492643A - A kind of English learning machine - Google Patents
A kind of English learning machine Download PDFInfo
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- CN108492643A CN108492643A CN201810320211.1A CN201810320211A CN108492643A CN 108492643 A CN108492643 A CN 108492643A CN 201810320211 A CN201810320211 A CN 201810320211A CN 108492643 A CN108492643 A CN 108492643A
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- english
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- tbr
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/06—Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
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- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention discloses a kind of English learning machines comprising：Brain wave acquisition processing module, processor, intelligent terminal, memory module, power module；In learning English, acquisition top occipital region brain electricity, calculates using IAF as the TBR of foundation, parameter is in real time in real time：The long 2s of window, overlaps 60%, and the transformation of 1024 point quick Fouriers reduces spectral leakage using Hamming window；Feedback control strategy is：Compared to tranquillization state baseline, TBR reduces by 25%.
Description
Technical field
The present invention relates to a kind of English learning machines to realize attention particularly in conjunction with neural feedback training technique
The effect of English study.
Background technology
Cranial nerve feedback training (also known as：Learn energy training for promotion) it is newest a kind of comprehensive to brain progress in the world at present
The technology of skill upgrading, many developed countries are such as in the world：The U.S., Britain, South Korea, Singapore etc. all using this technology come
The brain of people is adjusted, is especially widely used in external many schools.Operation principle：Human brain is in operation
Micro-current is will produce, neural feedback instrument for training with computer interconnection by that can detect the brain wave activity shape of trainer instantly
State, and the actual conditions of brain are combined, assist people to take exercise with specified computer game for the region of brain weakness
Cerebral nerve, to achieve the purpose that promoted brain attention, and then make memory, concentration, concentrated force, self-contr ol power,
IQ etc. is significantly improved, and makes brain sensitiveer.
English learning machine may be implemented self-service using modern scientific application technology in conjunction with the learning characteristic of English language
The English language of formula learns.English learning machine have the function of it is abundant, can be many-sided right from grammer, language, hearing, read-write etc.
Learner is trained.Also have the function of dictionary, translation, data check etc., it can be as the auxiliary tool of language learning.
It was noticed that the phenomenon that English study generally existing can not be concentrated, learner's frontal lobe encephalomere Lv Fashengte at this time
Fixed variation, based on the specific change of this encephalomere rule, we can be adjusted using cranial nerve feedback technique, improve English study
Attention.
The present invention, which puts forth effort on original creation algorithm for design, can adaptively obtain the true frequency range division of learner, to obtain
More effective feedback index, targetedly promotes neural feedback training effect, to achieve the effect that promote English study.
Invention content
Technical problems based on background technology, the present invention for background technology there are the problem of, a kind of original creation is provided
Algorithm for design can adaptively obtain the true frequency range of learner and divide, to obtain more effective feedback index, targetedly
Neural feedback training effect is promoted, to achieve the effect that promote English study.
The purpose of the present invention is achieved through the following technical solutions：
A kind of English learning machine comprising：
Brain wave acquisition processing module, processor, intelligent terminal, memory module, power module；
The workflow of the brain wave acquisition processing module is as follows：
Resting electroencephalogramidentification is acquired before English study, and reference, recording electrode P1, P2, Pz, O1, O2 are connected to ears
Brain electricity；Each three minutes of eye opening condition EO and eye closing condition EC, the data of record will divide the length signals of 3s, respectively divide intersegmental
60% overlaps；
Processing is removed to the artefact in the brain electricity of segmentation, the artefact in EEG includes eye movement, blink, power frequency, myoelectricity,
Each segmentation is detected, once artefact, beyond given threshold value, the signal of this section of segmentation just abandons；
The tranquillization state brain electricity calculated separately under two kinds of conditions (EO and EC) respectively obtains power spectral density plot：
It is compared to EC conditions hypencephalons electricity, it is more than that 25% frequency band is considered as personalization that power spectral energies, which reduce, under the conditions of EO
Alpha frequency bands divide (IAF)；
According to the division of IAF, using 2Hz to IAF lower boundaries as Theta wave bands, using the coboundaries IAF to 15Hz as Beta
Wave band；
Low frequency/high-frequency energy ratio of i-th of segmentation is calculated by the segment data of tranquillization state brain electricity under the conditions of EO, i.e.,
Theta/Beta ratio(TBR)：
Using tranquillization state brain electricity TBR as training baseline；
In learning English, acquisition top occipital region brain electricity, calculates using IAF as the TBR of foundation, parameter is in real time in real time：Window
Long 2s, overlaps 60%, and the transformation of 1024 point quick Fouriers reduces spectral leakage using Hamming window；
Feedback control strategy is：Compared to tranquillization state baseline, TBR reduces by 25% and awards, and otherwise gives and punishes.
This technology core is to study the brain electricity in learning English, and designing unique algorithm will be calculated
Forehead myoelectric index obtains accurate English study cerebral nervous system state index, is then controlled to nervous feedback system system
System ensures that the audio and video feedback of intelligent terminal is coincide with current cranial nerve feedback training requirement, to improve the effect of English study
Rate.
The memory module is used to record the data of English study information and brain electricity feedback training process.
The invention has the beneficial effects that：
This technology core is to study the brain electricity in learning English, and designing unique algorithm will be calculated
Forehead myoelectric index obtains accurate English study cerebral nervous system state index, is then controlled to nervous feedback system system
System ensures that the audio and video feedback of intelligent terminal is coincide with current cranial nerve feedback training requirement, to improve the effect of English study
Rate.
Specific implementation mode
Embodiment 1
A kind of English learning machine comprising：
Brain wave acquisition processing module, processor, intelligent terminal, memory module, power module；
The workflow of the brain wave acquisition processing module is as follows：
Resting electroencephalogramidentification is acquired before English study, and reference, recording electrode P1, P2, Pz, O1, O2 are connected to ears
Brain electricity；Each three minutes of eye opening condition EO and eye closing condition EC, the data of record will divide the length signals of 3s, respectively divide intersegmental
60% overlaps；
Processing is removed to the artefact in the brain electricity of segmentation, the artefact in EEG includes eye movement, blink, power frequency, myoelectricity,
Each segmentation is detected, once artefact, beyond given threshold value, the signal of this section of segmentation just abandons；
The tranquillization state brain electricity calculated separately under two kinds of conditions (EO and EC) respectively obtains power spectral density plot：
It is compared to EC conditions hypencephalons electricity, it is more than that 25% frequency band is considered as personalization that power spectral energies, which reduce, under the conditions of EO
Alpha frequency bands divide (IAF)；
According to the division of IAF, using 2Hz to IAF lower boundaries as Theta wave bands, using the coboundaries IAF to 15Hz as Beta
Wave band；
Low frequency/high-frequency energy ratio of i-th of segmentation is calculated by the segment data of tranquillization state brain electricity under the conditions of EO, i.e.,
Theta/Beta ratio(TBR)：
Using tranquillization state brain electricity TBR as training baseline；
In learning English, acquisition top occipital region brain electricity, calculates using IAF as the TBR of foundation, parameter is in real time in real time：Window
Long 2s, overlaps 60%, and the transformation of 1024 point quick Fouriers reduces spectral leakage using Hamming window；
Feedback control strategy is：Compared to tranquillization state baseline, TBR reduces by 25% and awards, and otherwise gives and punishes.
The memory module is used to record the data of English study information and brain electricity feedback training process.
This technology core is to study the brain electricity in learning English, and designing unique algorithm will be calculated
Forehead myoelectric index obtains accurate English study cerebral nervous system state index, is then controlled to nervous feedback system system
System ensures that the audio and video feedback of intelligent terminal is coincide with current cranial nerve feedback training requirement, to improve the effect of English study
Rate specially promotes memory, forgets 100% or more time lengthening after learning for the first time.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (5)
1. a kind of English learning machine comprising：
Brain wave acquisition processing module, processor, intelligent terminal, memory module, power module.
2. English learning machine described in claim 1, it is characterised in that：
The workflow of the brain wave acquisition processing module is as follows：
Resting electroencephalogramidentification is acquired before English study, and reference, the brain of recording electrode P1, P2, Pz, O1, O2 are connected to ears
Electricity；Each three minutes of eye opening condition EO and eye closing condition EC, the data of record will divide the length signals of 3s, respectively divide intersegmental 60%
It overlaps；
Processing is removed to the artefact in the brain electricity of segmentation, the artefact in EEG includes eye movement, blink, power frequency, myoelectricity, to every
A segmentation is detected, once artefact, beyond given threshold value, the signal of this section of segmentation just abandons；
The tranquillization state brain electricity calculated separately under two kinds of conditions (EO and EC) respectively obtains power spectral density plot：
It is compared to EC conditions hypencephalons electricity, it is more than that 25% frequency band is considered as personalization that power spectral energies, which reduce, under the conditions of EO
Alpha frequency bands divide (IAF)；
According to the division of IAF, using 2Hz to IAF lower boundaries as Theta wave bands, using the coboundaries IAF to 15Hz as Beta waves
Section；
Low frequency/high-frequency energy ratio of i-th of segmentation, i.e. Theta/ are calculated by the segment data of tranquillization state brain electricity under the conditions of EO
Beta ratio(TBR)：
3. the English learning machine described in claim 2, it is characterised in that：
Feedback control strategy is：Compared to tranquillization state baseline, TBR reduces by 25% and awards, and otherwise gives and punishes.
4. claim 1-3 any one of them English learning machines, it is characterised in that：
In learning English, acquisition top occipital region brain electricity, calculates using IAF as the TBR of foundation, parameter is in real time in real time：The long 2s of window,
60% is overlapped, the transformation of 1024 point quick Fouriers reduces spectral leakage using Hamming window.
5. the English learning machine described in claim 4, it is characterised in that：
Using tranquillization state brain electricity TBR as training baseline.
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Citations (6)
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| CN105559779A (en) * | 2016-03-01 | 2016-05-11 | 夏鹏 | Method for carrying out cognitive evaluation through electroencephalo-graph frequency spectrum |
| JP2017074356A (en) * | 2015-10-16 | 2017-04-20 | 国立大学法人広島大学 | Sensitivity evaluation method |
| CN107595302A (en) * | 2017-09-06 | 2018-01-19 | 西安交通大学 | A kind of device and method that user's state of mind is monitored using EEG signals |
| US20180055402A1 (en) * | 2015-02-11 | 2018-03-01 | Biosensor, Inc. | Methods and systems for therapeutic neuromodulation |
-
2018
- 2018-04-11 CN CN201810320211.1A patent/CN108492643A/en active Pending
Patent Citations (6)
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|---|---|---|---|---|
| CN102319067A (en) * | 2011-05-10 | 2012-01-18 | 北京师范大学 | Nerve feedback training instrument used for brain memory function improvement on basis of electroencephalogram |
| CN102715903A (en) * | 2012-07-09 | 2012-10-10 | 天津市人民医院 | Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram |
| US20180055402A1 (en) * | 2015-02-11 | 2018-03-01 | Biosensor, Inc. | Methods and systems for therapeutic neuromodulation |
| JP2017074356A (en) * | 2015-10-16 | 2017-04-20 | 国立大学法人広島大学 | Sensitivity evaluation method |
| CN105559779A (en) * | 2016-03-01 | 2016-05-11 | 夏鹏 | Method for carrying out cognitive evaluation through electroencephalo-graph frequency spectrum |
| CN107595302A (en) * | 2017-09-06 | 2018-01-19 | 西安交通大学 | A kind of device and method that user's state of mind is monitored using EEG signals |
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| 周鹏 等: "《双耳差频声刺激下的脑电特征》", 《天津大学学报（自然科学与工程技术版）》 * |
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Application publication date: 20180904 |