草原公路駕駛員腦電信號(hào)的降噪處理研究
發(fā)布時(shí)間：2019-03-04 09:45
【摘要】：內(nèi)蒙古自治區(qū)草原公路路側(cè)景觀環(huán)境單調(diào),道路線形長(zhǎng)直線多曲線半徑大,駕駛員在草原公路上行車很容易產(chǎn)生疲勞和注意力下降等情況,對(duì)整個(gè)道路交通安全造成嚴(yán)重隱患。腦電波作為評(píng)定中樞神經(jīng)系統(tǒng)變化的指標(biāo)之一,可以比較靈敏得反映駕駛員在行車過程中的精神狀態(tài)。目前,駕駛員腦電信號(hào)的采集方式為多以靜態(tài)采集方式為參考,由于顱骨和頭皮組織等對(duì)腦電信號(hào)的衰減作用,加之駕駛環(huán)境極其復(fù)雜并呈現(xiàn)動(dòng)態(tài)變化特征,腦電信號(hào)很容易被無關(guān)偽跡干擾。因此在分析腦電信號(hào)時(shí),必須剔除來自腦電活動(dòng)以外的偽跡,對(duì)獲得逼近駕駛員駕駛真實(shí)狀態(tài)的腦電信號(hào)很有必要。本文從駕駛員腦電信號(hào)偽跡的來源著手,對(duì)偽跡特征進(jìn)行逐個(gè)分析并進(jìn)行綜合分類,確定腦電信號(hào)中的偽跡主要由腦電信號(hào)頻帶外的高頻噪聲和頻帶內(nèi)的眼電偽跡組成,通過行車試驗(yàn)設(shè)計(jì)采集到駕駛員的腦電信號(hào)數(shù)據(jù)。對(duì)于高頻噪聲的去除本文設(shè)計(jì)了 Kaiser窗濾波器和等波紋優(yōu)化濾波器,通過對(duì)指標(biāo)比較判斷,選擇最優(yōu)濾波器。對(duì)于頻帶內(nèi)部的眼電偽跡,在選擇合適的小波基之后,通過對(duì)比固定閾值法和改進(jìn)的各級(jí)獨(dú)立閾值分段閾值法,比較信號(hào)在降噪之后的時(shí)域和頻域圖譜分析降噪前后的變化情況。本文研究得到以下結(jié)論:(1)通過對(duì)比兩種方法設(shè)計(jì)的濾波器對(duì)腦電降噪之后的時(shí)頻圖得出:兩種方法在濾波之后對(duì)腦電信號(hào)造成一定程度的延遲效應(yīng),但Kaiser窗濾波器對(duì)信號(hào)在時(shí)域造成的時(shí)間延遲更長(zhǎng),而且在對(duì)高頻段信號(hào)的濾波達(dá)到規(guī)定的要求時(shí),Kaiser窗濾波器具有更大的階數(shù)N,造成整個(gè)濾波過程計(jì)算量變大,增加了數(shù)據(jù)處理的時(shí)間。(2)Kaiser窗濾波器在阻帶波紋的不唯一性導(dǎo)致旁瓣泄露明顯,等波紋法卻在阻帶內(nèi)將第一旁瓣的波動(dòng)轉(zhuǎn)移到高頻部位去,使整個(gè)阻帶的波動(dòng)更加均勻,通帶內(nèi)等波紋法使得濾波后的信號(hào)與原信號(hào)具有更優(yōu)的逼近程度。(3)通過對(duì)比傳統(tǒng)的固定閾值法和改進(jìn)的各級(jí)獨(dú)立閾值分段法處理腦電信號(hào),固定閾值法降噪之后腦電信號(hào)喪失了原來信號(hào)的細(xì)節(jié)特征,使得信號(hào)過于平滑,并且在眼電偽跡出現(xiàn)奇異點(diǎn)處,波峰存在顯著,降噪效果不佳。改進(jìn)的獨(dú)立閾值分析能在每級(jí)分解中采用多段閾值,降噪后的信號(hào)能更好的保留了腦電信號(hào)的細(xì)節(jié)特征,在眼電偽跡處能更好的消除眼電偽跡波形的特征,從頻域分析也驗(yàn)證了眼電偽跡的頻率被消除。
[Abstract]:The landscape environment on the road side of the grassland highway in Inner Mongolia Autonomous region is monotonous, the long linear and multi-curve radius of the road is large, the driver is easy to lead to fatigue and decrease of attention on the grassland road, which causes serious hidden trouble to the traffic safety of the whole road. As one of the indexes to evaluate the changes of the central nervous system, brain waves can reflect the driver's mental state in the course of driving more sensitively. At present, the acquisition mode of driver's EEG signal is mostly based on static acquisition mode. Due to the attenuation effect of skull and scalp tissue on EEG signal, plus the driving environment is extremely complex and presents dynamic change characteristics. EEG signals are easily interfered with by unrelated artifacts. Therefore, when analyzing EEG signals, it is necessary to remove the artifacts from the EEG activities, which is necessary to obtain EEG signals approaching the driver's real driving state. Starting from the source of EEG artifacts in drivers, this paper analyzes and classifies the artifact features one by one, and determines that the artifacts in EEG signals are mainly composed of high-frequency noise outside the band of EEG signals and eye artifacts in the frequency band, and that the artifacts in EEG signals are mainly composed of high-frequency noise outside the band and eye artifacts in the frequency band. The data of electroencephalogram (EEG) of drivers were collected by driving test design. In this paper, the Kaiser window filter and the equiripple optimization filter are designed to remove the high frequency noise. The optimal filter is selected by comparing the indexes. After choosing the appropriate wavelet basis, the fixed threshold method and the improved independent threshold segmentation method are compared to each other after selecting the proper wavelet basis for the eye electrical artifact in the frequency band. The time domain and frequency domain spectra of the signal after denoising are compared to analyze the changes of the signal before and after de-noising. The conclusions of this paper are as follows: (1) by comparing the time-frequency images of EEG de-noising with the filters designed by the two methods, the results show that the two methods have a certain degree of delay effect on EEG signal after filtering. However, the time delay caused by the Kaiser window filter in the time domain is longer, and when the filtering of the high frequency signal reaches the required requirements, the Kaiser window filter has a larger order N, resulting in a larger amount of computation in the whole filtering process. The time of data processing is increased. (2) the non-uniqueness of the Kaiser window filter in the blocking band leads to obvious side lobe leakage, but the first side lobe fluctuation is transferred to the high frequency region in the blocking band by the equal ripple method, which makes the fluctuation of the whole stop band more uniform. The passband isoripple method makes the filtered signal have a better approximation with the original signal. (3) the EEG signal is processed by comparing the traditional fixed threshold method and the improved independent threshold segmentation method. After noise reduction by the fixed threshold method, the EEG signal lost the detail characteristics of the original signal, which made the signal too smooth, and at the singular point of the false trace of the eye, the wave peak was significant, and the noise reduction effect was not good. The improved independent threshold analysis can adopt multi-segment threshold in each decomposition, and the de-noised signal can retain the details of EEG signal better, and can eliminate the characteristic of eye artifact waveform better at eye artifact. Frequency domain analysis also verifies that the frequency of eye artifacts is eliminated.
【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.25
本文編號(hào)：2434166
[Abstract]:The landscape environment on the road side of the grassland highway in Inner Mongolia Autonomous region is monotonous, the long linear and multi-curve radius of the road is large, the driver is easy to lead to fatigue and decrease of attention on the grassland road, which causes serious hidden trouble to the traffic safety of the whole road. As one of the indexes to evaluate the changes of the central nervous system, brain waves can reflect the driver's mental state in the course of driving more sensitively. At present, the acquisition mode of driver's EEG signal is mostly based on static acquisition mode. Due to the attenuation effect of skull and scalp tissue on EEG signal, plus the driving environment is extremely complex and presents dynamic change characteristics. EEG signals are easily interfered with by unrelated artifacts. Therefore, when analyzing EEG signals, it is necessary to remove the artifacts from the EEG activities, which is necessary to obtain EEG signals approaching the driver's real driving state. Starting from the source of EEG artifacts in drivers, this paper analyzes and classifies the artifact features one by one, and determines that the artifacts in EEG signals are mainly composed of high-frequency noise outside the band of EEG signals and eye artifacts in the frequency band, and that the artifacts in EEG signals are mainly composed of high-frequency noise outside the band and eye artifacts in the frequency band. The data of electroencephalogram (EEG) of drivers were collected by driving test design. In this paper, the Kaiser window filter and the equiripple optimization filter are designed to remove the high frequency noise. The optimal filter is selected by comparing the indexes. After choosing the appropriate wavelet basis, the fixed threshold method and the improved independent threshold segmentation method are compared to each other after selecting the proper wavelet basis for the eye electrical artifact in the frequency band. The time domain and frequency domain spectra of the signal after denoising are compared to analyze the changes of the signal before and after de-noising. The conclusions of this paper are as follows: (1) by comparing the time-frequency images of EEG de-noising with the filters designed by the two methods, the results show that the two methods have a certain degree of delay effect on EEG signal after filtering. However, the time delay caused by the Kaiser window filter in the time domain is longer, and when the filtering of the high frequency signal reaches the required requirements, the Kaiser window filter has a larger order N, resulting in a larger amount of computation in the whole filtering process. The time of data processing is increased. (2) the non-uniqueness of the Kaiser window filter in the blocking band leads to obvious side lobe leakage, but the first side lobe fluctuation is transferred to the high frequency region in the blocking band by the equal ripple method, which makes the fluctuation of the whole stop band more uniform. The passband isoripple method makes the filtered signal have a better approximation with the original signal. (3) the EEG signal is processed by comparing the traditional fixed threshold method and the improved independent threshold segmentation method. After noise reduction by the fixed threshold method, the EEG signal lost the detail characteristics of the original signal, which made the signal too smooth, and at the singular point of the false trace of the eye, the wave peak was significant, and the noise reduction effect was not good. The improved independent threshold analysis can adopt multi-segment threshold in each decomposition, and the de-noised signal can retain the details of EEG signal better, and can eliminate the characteristic of eye artifact waveform better at eye artifact. Frequency domain analysis also verifies that the frequency of eye artifacts is eliminated.
【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.25
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