發(fā)布時(shí)間：2018-06-05 14:11

  本文選題：腦電信號(hào) + 腦力疲勞��； 參考：《廣西大學(xué)》2014年碩士論文

【摘要】：腦力疲勞是由于長(zhǎng)時(shí)間工作或?qū)W習(xí)造成的一種常見生理現(xiàn)象,不及時(shí)調(diào)整和恢復(fù)會(huì)降低人們工作和學(xué)習(xí)的效率,嚴(yán)重的會(huì)威脅到人們的健康與生命。腦電(Electroencephalogram,簡(jiǎn)稱EEG)信號(hào)直接反映了大腦組織的電活動(dòng),利用其來(lái)評(píng)估腦力疲勞已經(jīng)成為腦力疲勞檢測(cè)的研究熱點(diǎn)。 目前,基于EEG信號(hào)腦力疲勞檢測(cè)中都采用多通道腦電采集設(shè)備,由于該類設(shè)備的局限性導(dǎo)致基于EEG信號(hào)腦力疲勞檢測(cè)只能在實(shí)驗(yàn)室進(jìn)行為了克服該類設(shè)備在實(shí)際應(yīng)用中攜帶不便、操作復(fù)雜成本高等局限性,本文探討了使用便攜式腦電采集設(shè)備采集單通道EEG信號(hào)對(duì)腦力疲勞進(jìn)行檢測(cè)的方法。 在前人研究的基礎(chǔ)上,主要完成了以下工作： 1、對(duì)基于EEG信號(hào)腦力疲勞檢測(cè)研究中所使用的腦電采集設(shè)備、電極選取以及特征提取方法的國(guó)內(nèi)外研究現(xiàn)狀作了較全面的介紹。 2、針對(duì)小波包快速算法固有的頻率混淆缺陷,提出了一種單子帶重構(gòu)小波包改進(jìn)算法(Improved Single Sub-band Reconstruction of Wavelet Packet Algorithm,簡(jiǎn)稱ISSBR-WPA),該算法的思路是引入兩個(gè)算子來(lái)消除小波包分解過(guò)程與重構(gòu)過(guò)程中各子帶多余的頻率成分,從而有效地克服頻率混淆現(xiàn)象的產(chǎn)生。實(shí)驗(yàn)結(jié)果表明,與小波包快速算法相比,ISSBR-WPA算法較為準(zhǔn)確地提取EEG信號(hào)中的δ、θ、αα和p四個(gè)節(jié)律,為準(zhǔn)確計(jì)算腦力疲勞特征參數(shù)提供了保障。 3、采用兩種特征參數(shù)評(píng)估大腦是否處于疲勞狀態(tài)。兩種特征參數(shù)分別為基于EEG信號(hào)四個(gè)節(jié)律相關(guān)能量比的8個(gè)特征F1～F8和基于各子帶小波包系數(shù)的方差。對(duì)便攜式設(shè)備采集大腦FP1處的EEG信號(hào)進(jìn)行特征提取與分析,實(shí)驗(yàn)結(jié)果表明,8個(gè)能量比中特征F2、F3、F4、F6和F7可作為評(píng)估腦力疲勞的有效指標(biāo),其中特征F2更為有效；低頻部分子帶的小波包系數(shù)方差能有效地區(qū)分清醒和腦力疲勞兩種精神狀態(tài)。 4、為了驗(yàn)證基于便攜式腦電采集設(shè)備的特征提取結(jié)果的有效性,利用多通道腦電采集設(shè)備采集得到FP1和01導(dǎo)聯(lián)處的EEG信號(hào)分別進(jìn)行相同的特征提取和分析。實(shí)驗(yàn)結(jié)果表明,多通道腦電采集設(shè)備采集FP1處EEG信號(hào)所提取的特征參數(shù)表征的腦力疲勞狀態(tài)和便攜式設(shè)備所表征的腦力疲勞狀態(tài)一致。說(shuō)明采用便攜式腦電采集設(shè)備采集大腦FP1處的EEG信號(hào)能檢測(cè)大腦的疲勞狀態(tài)。實(shí)驗(yàn)結(jié)果還表明,多通道設(shè)備采集大腦O1處的EEG信號(hào)也可以作為分析大腦疲勞狀態(tài)的信號(hào)。
[Abstract]:Mental fatigue is a common physiological phenomenon caused by long hours of work or study. Not adjusting and recovering in time will reduce the efficiency of work and study, and will seriously threaten people's health and life. Electroencephalograms (EEGG) signals directly reflect the electrical activity of brain tissue, and the use of these signals to assess brain fatigue has become a research hotspot in the detection of brain fatigue. At present, multichannel EEG acquisition devices are used in EEG signal brainpower fatigue detection. Because of the limitation of this kind of equipment, the brainpower fatigue detection based on EEG signal can only be carried out in the laboratory in order to overcome the limitation of the equipment carrying inconvenience in practical application and the high cost of complex operation, etc. In this paper, the method of detecting mental fatigue by using portable EEG acquisition equipment to collect single channel EEG signals is discussed. The main works are as follows: 1. The EEG acquisition equipment which is used in the research of EEG signal brain fatigue detection is studied. The research status of electrode selection and feature extraction methods at home and abroad is introduced. 2. Aiming at the inherent frequency confusion defect of wavelet packet fast algorithm, In this paper, an improved single Sub-band Reconstruction of Wavelet packet algorithm (ISSBR-WPAA) is proposed. The idea of this algorithm is to introduce two operators to eliminate the redundant frequency components of each sub-band in the process of wavelet packet decomposition and reconstruction. Thus the frequency confusion can be effectively overcome. The experimental results show that the ISSBR-WPA algorithm is more accurate than the wavelet packet algorithm in extracting the 未, 胃, 偽 and p rhythms of EEG signals. It provides a guarantee for calculating the characteristic parameters of mental fatigue. 3. Two characteristic parameters are used to evaluate whether the brain is in a fatigue state. The two characteristic parameters are eight feature F1F8 based on the four rhythmic correlation energy ratio of EEG signals and the variance of wavelet packet coefficients based on each sub-band. The EEG signals collected from FP1 of the brain by portable equipment were extracted and analyzed. The experimental results show that the characteristics F2F3F4F4F6 and F7 can be used as effective indexes for evaluating mental fatigue, among which feature F2 is more effective. The variance of wavelet packet coefficients of the low frequency subbands can effectively distinguish two mental states: awake and mental fatigue. 4. In order to verify the validity of feature extraction based on portable EEG acquisition equipment, The EEG signals in leads FP1 and 01 were extracted and analyzed respectively by using multi-channel EEG acquisition equipment. The experimental results show that the characteristic parameters extracted from EEG signals collected from FP1 by multi-channel EEG acquisition equipment are consistent with the mental fatigue state represented by portable devices. It is concluded that EEG signals collected from FP1 can be used to detect the fatigue state of the brain. The experimental results also show that EEG signals at O1 can also be used to analyze the fatigue state of the brain.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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本文編號(hào)：1982257