Introduction [1]
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Wavelet transform and independent component analysis are two kinds of signal processing methods which have been studied in recent years. They are applied in the preprocessing and feature extraction of EEG signals. However, in the measurement process of EEG signals, different signal processing methods are better in processing, and what are their respective application conditions, all need to be analyzed in actual experiments. In this paper, in the research of brain electric characteristics of automobile drivers, two kinds of common EEG interference signals in driving are analyzed and removed by two methods. The advantages and disadvantages of the two methods in EEG signal interference removal are analyzed. Their respective application conditions.
1 Wavelet transform and independent component analysis
Wavelet transform [1,2] (Wavelet Transform) is a typical time-frequency analysis method. The basic idea is to use a set of wavelet functions to represent or approximate signals. This set of wavelet functions is a translation and expansion of a wavelet basis function. The obtained analysis window area is fixed, but its shape can be changed, that is, the time window and the frequency window can be changed. In the low frequency part of the signal, the wavelet transform has higher frequency resolution and lower time resolution in the signal. The high frequency part, wavelet transform has higher time resolution and lower frequency resolution. In recent years, wavelet transform has been widely used in the analysis of non-stationary signals such as EEG.
Independent Component Analysis [3, 4, 5] (ICA) is a new blind source separation method (BlindSource Separation BSS) developed in recent years. This method has great application potential in many fields of signal processing, and its theoretical development can be traced back to the early 1980s. However, it was not until the mid-1990s that the research on ICA theory and algorithms was really developed and received extensive attention from the international signal processing community, and has also been achieved in biomedical signal processing, mixed speech signal separation, and image denoising. Good application effect. The object handled by ICA is a set of mixed signals generated by linear combination of mutually independent statistical sources. The ultimate goal is to extract the individual signal components from the mixed signal.
In the recording process of EEG signals, signals caused by non-EEG activities are often mixed, and these signals are called artifacts. There are many types of artifacts in EEG. The more common ones are ECG, EMG, Blink, Eye Movement, Sweating, and Power Frequency Interference. Because EEG signals are generated by cerebral cortical neuron activity, eye activity, muscle activity, power frequency interference, and ECG signals are usually not restricted by brain activity, so ICA separable conditions are met, and for EEG signals, The conduction from the inside of the cortex to the scalp electrode is considered to be approximately linear, so it is also considered that EEG is a linear combination of individual independent source signals to the recording electrode. At this time, the application of the ICA method can separate the interference components from other bioelectric signals and the interference of other external environments from the multi-channel EEG signals, thereby achieving the purpose of noise cancellation.
2 EEG interference when driving a car
EEG measurement in a driving environment is different from EEG measurement in a hospital and laboratory environment. Traditional EEG measurements are generally performed in a special electromagnetic shielding environment, and the environment is required to be quiet, requiring the subject to maintain Still. However, in the actual car driving EEG measurement can not meet such requirements, the actual EEG interference during driving mainly has electromagnetic interference in the surrounding environment, such as cell phone interference, as well as the driver's own interference, such as ocular interference.
In this paper, the interfering EEG signals obtained in the actual driving EEG experiment are taken as the analysis object, and the processing effects and application range of the two signal processing methods are explained. (a) and (b) of Fig. 1 show EEG signals that are interfered by mobile phones and ocular electricity, respectively.
(a) EEG signals interfered by mobile phones (b) EEG signals interfered with by electro-optical signals
Figure 1 Interfering with EEG signals
The signals of 10 EEG channels are given in the figure, each channel is 6 seconds of data, the sampling frequency is 500Hz, a total of 3000 data points. The abscissa is the time (s) of EEG sampling, and the ordinate is the EEG amplitude (μv) of each EEG channel.
The following two methods of wavelet transform and independent component analysis are used to analyze and remove the above two interference signals.
3 wavelet transform to remove interference
Wavelet transform first involves the selection of wavelets. The most used in EEG signal processing is the db (Daubechies) wavelet. Usually, the wavelet base in the Daubechies system is recorded as dbN, N is the serial number, N = 1, 2, ⋯, 10. In the dbN series wavelet, the smaller the N, that is, the smaller the support length, the smaller the scale that can be detected. Therefore, it is necessary to select the appropriate scale for the actual frequency range of the EEG signal for detection. By contrast, this paper uses the db5 wavelet to the brain. The electrical signal is analyzed.
Then there is the determination of the number of wavelet decomposition layers. The acquisition frequency of the EEG signal is 500 Hz. According to the sampling theorem, the representation range of the EEG signal is 0-250 Hz. Therefore, 6 layers of EEG decomposition, 7 decomposed signals can be obtained, in order of frequency from high to low, in order: cd1:125 ~ 250Hz; cd2: 64 ~ 125Hz; cd3: 32 ~ 64Hz; cd4: 16~32Hz; cd5: 8~16Hz; cd6: 4~8Hz; ca6: 0~4Hz.
Finally, the decomposed wavelet components are analyzed to remove the interference. Because the acquisition frequency set by the EEG instrument in the subject experiment is 0.53 to 60 Hz, the cd1 and cd2 components are false components and can be directly removed. The interference of cell phone signals to EEG is mainly high-frequency interference, which is concentrated on the cd3 component, but the data in this band may have EEG information that needs to be used. Therefore, appropriate thresholds should be selected for processing. Comparing the mean value of the cd3 component of the wavelet decomposition of EEG without interference, the threshold hcd3 is determined, which is considered to be high frequency interference by hcd3. The commonly used threshold functions are hard threshold and soft threshold [6]. The hard threshold method is adopted in this paper. Directly set to zero, therefore, the EEG signal after removing the interference of the mobile phone is y=cd4+cd5+cd6+ca6+cd3, where the absolute value of cd3 is zero when it exceeds hcd3. The interference frequency of ocular electricity is generally about 4 Hz, which is generally in the ca6 component. However, if the blink speed is relatively fast, it may be greater than 4 Hz, so it may be in the cd6 component. Therefore, the interference removal by using the wavelet transform for the blinking requires two ca6 and cd6. The threshold is set for the component, and the thresholds are determined to be hca6 and hcd6, respectively, based on the mean values ​​of the ca6 and cd6 components of the EEG wavelet decomposition without interference. Similarly, the hard threshold method is adopted to remove the electroencephalogram signal after ocular electrical interference as y=cd3+cd4+cd5+cd6+ca6, where ca6 and cd6 are zero when they exceed the thresholds hca6 and hcd6, respectively.
There are 10 channels of EEG signals in the experimental signal. The wavelet transform method is the same for each channel. For convenience, only the wavelet removal results of the F4 channel signal are given. Figure 2 (a) and (b) show the results of wavelet transform for cell phone interference and EEG interference EEG signals.
(a) Mobile phone interference removal results
(b) EEG interference removal results
Figure 2 Wavelet transform removal results
4 independent component analysis to remove interference
The independent component analysis method requires multiple channels of EEG signals, and then separates the signals of each channel into separate signals. According to the characteristics of the interference, the independent components of the interference signals are removed and the remaining independent components are recombined. Remove the signal after the interference. The results of the removal of electromagnetic interference and ocular electrical interference EEG signals by independent component analysis are given in (a) and (b) of Fig. 3, respectively.
(a) Electromagnetic interference removal result (b) EEG interference removal result
Figure 3 Independent component analysis removal results
5 two methods to remove results comparison and analysis
Comparing the removal effect of mobile phone interference: Compared with Figure 2(a) and Figure 3(a), the wavelet transform method is better than the independent component analysis method. The reason is that mobile phone interference is a high-frequency interference, and the wavelet transform method can relatively accurately extract the high-frequency part of the brain electricity and then remove it. Among the independent components obtained by the independent component analysis method, it is not possible to clearly determine which component is caused by the interference of the mobile phone, so the effect is not ideal.
Comparing the removal effect of ocular electrical interference: As seen in Fig. 2(b), the wavelet transform removes some of the original low-frequency waves of the EEG during the removal of ocular electrical interference. Obviously these are not ocular electrical interference; As seen in 3(b), the independent component analysis method better removes the ocular electrical interference of each channel and does not affect other EEG. The reason is that the frequency of ocular electrical interference is not fixed, and it is coincident with the useful frequency band of EEG. Therefore, in the case of removal, the phenomenon of erroneous removal will inevitably occur, and the EOG is decomposed in the independent component analysis method. The component is a very clear component, so the ocular electrical interference can be removed very accurately.
Through analysis, the following conclusions can be drawn:
(1) Both wavelet analysis and independent component analysis can be applied to the removal of EEG signal interference.
(2) Wavelet transform is better than the independent component analysis method for the removal of electromagnetic interference such as mobile phones, and the effect of independent component analysis is better than the wavelet transform for the removal of EO.
(3) The advantage of wavelet analysis is that the algorithm is mature and the calculation amount is small, but each EEG channel data needs to be processed separately. It is suitable for situations where the required EEG channels are small and the real-time requirements are high.
(4) The advantage of independent component analysis is that the spatial information of EEG is utilized, and the interference of multiple channels can be removed at the same time, but the calculation amount is large. Therefore, it is suitable for the case where there are many EEG channels required.
6 Conclusion
In this paper, the characteristics of the two methods in the removal of EEG during driving and the application conditions are obtained by analyzing the mobile phone interference and the EEG interference signal while driving. It lays the foundation for the extraction and further application of driver's EEG information.
The author of this article is innovative:
By comparing the effects of wavelet analysis and independent component analysis on the removal of EEG interference signals during driving, the advantages and disadvantages of the two methods in EEG signal preprocessing and their respective application ranges are clarified.
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