Artifact Detection and Correction for Operator Functional State Estimation

Transcription

1 Artifact Detection and Correction for Operator Functional State Estimation Chris A. Russell Air Force Research Laboratory Human Effectiveness Directorate 2255 H Street Wright-Patterson AFB, OH christopher.russell@wpafb.af.mil Ping He Department of Biomedical, Industrial, and Human Factors Engineering Wright State University Dayton, OH phe@cs.wright.edu Glenn F. Wilson Air Force Research Laboratory Human Effectiveness Directorate 2255 H Street Wright-Patterson AFB, OH glenn.wilson@wpafb.af.mil Abstract Determining operator functional state is a critical component of adaptively aiding closed human-in-the-loop systems. The input measures or features used to define the operator functional state (OFS) model must be free of artifacts to ensure accurate classification of operator functional state. Electroencephalography (EEG) is a major component of the OFS model. Since the EEG magnitude is small relative to other psychophysiological measures it is most susceptible to contamination or artifact. Eye blinks and body movements are the usual culprit of artifact in EEG. This paper discusses an adaptive filter algorithm for removal of both horizontal and vertical eye movements including blinks. Muscle activation is also a major source of EEG contamination. A discussion of a promising muscle artifact removal using independent component analysis is presented. Additional artifacts can be detected but not removed using a simple statistical technique which is presented here. 1 Introduction Operator functional state (OFS) estimation is an important component of a closed humanin-the-loop automated system. The estimates of the operator state provided to the system for adaptive automation must be as accurate as possible to ensure system reliability and operator acceptance. Possible deterrents to accurate estimates are artifacts in the psychophysiological signals used to classify the operator state. Filtering is the most obvious choice for removal of artifact. But in many cases, specifically muscle and eye artifacts, filtering the signals using bandpass filters removes the information in the psychophysiological signals that is of interest to the investigator as well as the artifact. Other methods must be considered to remove the artifact and retain the critical information in the signal. Many artifact correction algorithms are available and exhibit differing levels of success in the removal and detection of artifacts. Both spatial (frequency domain) and temporal (time domain) algorithms exist and both have advantages and disadvantages. Adaptive filters and regression methods are examples of algorithms implemented in the temporal

2 domain while independent component analysis, wavelet techniques, and fast Fourier methods are examples of spatial domain algorithms. Much of the research has focused on post processing of contaminated signals. This is unacceptable for real-time implementation in a closed loop system. The algorithms must be able to remove or detect and label the artifacts in near real-time to enable accurate ontime estimation of the operator s functional state. Several algorithms present themselves as better candidates for real-time applications. Adaptive filters are used successfully at the Air Force Research Laboratory (AFRL) for the removal of horizontal and vertical eye movement in EEG. Muscle artifacts pose a unique problem since there are multiple sources all around the skull unlike the eye artifact which comes from two localized sources. Algorithms for removing muscle artifacts from EEG have been pursued with limited success. Independent component analysis (ICA) and principle component analysis (PCA) techniques have been investigated and results suggest while these methods can extract sources of muscle artifact, however, real-time implementation of these algorithms may be difficult. 2 Methods 2.1 Eye Artifact Correction of EEG Two methods for correcting eye blink and eye movement artifact were compared. The first technique is based on frequency-domain methods (Gasser, Sroka, and Möcks, 1985). This regression method uses a frequency dependent scaling factor to remove the artifact attributed to the EOG signal. The EOG artifact in the contaminated EEG is removed using EEG( ω) = EEGartifact ( ω) + K( ω) EOG( ω) (1) where K(ω) is the scaling factor and ω denoted frequency. Note the scaling factor is frequency dependent which indicates each frequency component has a different scale factor. The regression requires training to determine the coefficients of the EOG transfer function K. The frequency dependent scaling factor represents an improvement over a constant scaling factor. A constant scaling factor removes the same power from the contaminated EEG signal regardless of frequency. The artifact produced in the EEG signal is frequency dependent. More power is in the theta band (4-8 Hz) than is represented in the alpha band (8-12 Hz). The second approach is a time-domain method based on adaptive filtering (Widrow and Stearns, 1985). Figure 1 shows the two channel adaptive noise canceller with two reference inputs (vertical, VEOG, and horizontal, HEOG, EOG). This method does not

3 Primary input (EEG) Reference 1 (VEOG) Reference 2 (HEOG) s(n) = x(n) + z(n) r v (n) r h (n) Adaptive filter h v (m) Adaptive filter h h (m) + r ) v (n) - r ) h (n) Σ - (clean EEG) ) ) e( n) = s( n) r ( n) r ( n) where ) r ( n) = v ) r ( n) = h M m= 1 M m= 1 v h v h ( m) r ( n + 1 m) v h ( m) r ( n + 1 m) h h Figure 1. Block diagram of EOG noise canceller using adaptive filtering require calibration and can artifacts can be remove online and in real-time (He, Wilson, and Russell, 24) The primary input to the noise canceller is the contaminated EEG signal and can be modeled as EEG signal plus noise (VEOG and HEOG). The adaptive algorithm used by this model is the recursive least squares algorithm. The recursive least squares algorithm was chosen for its superior stability and fast convergence. The regression algorithm and the adaptive filter algorithms were compared using EEG artificially contaminated with VEOG and HEOG. The simulated noisy EEG signals were generated using a model as shown in Figure 2. An autoregressive (AR) model using the Burg method (Kay, 1988) was used to generate simulated EEG. This signal was summed with a generated EOG signal using an infinite impulse response (IIR) filter. white noise AR model Simulated pure EEG + Simulated noisy EEG EOG samples obtained from real data IIR filter Transferred EOG Figure 2. Block diagram of EEG and EOG simulation for verifying correction algorithms.

4 2.2 EMG Artifact Removal for EEG The removal of muscle artifact (EMG) from the EEG signal is not as straight forward as the removal of ocular artifacts. Unlike the eye artifact, the EMG artifact does not come from a single source. The artifact caused by muscle has many sources distributed over the scalp and can be caused by small muscle activations producing muscle tension as well as larger muscle movements such as head movements. The main difficulty in removing EMG noise is due to it s the widespread locations of the generating sources. An EMG signal recorded at any specific site, e.g. F7, is a superposition of action potentials produced by many motor units after traveling to the measurement site. Due the natural neural regulation, these motor units are activated alternately rather than in exact synchronization. In other words, the EMG signal is associated with a group of spatially-distributed sources rather than a single equivalent source. An important consequence of this model is the low coherence between a pair of EMG signals recorded at two different locations. This explains why the EMG noise can t be removed using either the regression method or the adaptive filtering method which require a high degree of correlation between the noise at the recording site and the noise at the reference site. Two popular methods, PCA and ICA, have been shown to be effective in removing EOG noise (Lagerlund, Sharbrough, and Busacker, 1997, Jung, Makeig, Humphreis, Lee, McKeown, Iragui, and Sejnowski, 2). An investigation of these two methods for removal of EMG noise from EEG was conducted. A ten second sample of 19 channels of EEG containing visible EMG noise was selected for the evaluation. The PCA and ICA analysis was conducted on this contaminated sample. The ICA or PCA components contributing to the EMG contamination were visually selected for removal from spatial and time domain plots of the components. Using the remained components, the EEG signal was recreated using a reconstruction or spatial filter (Lagerlund, Sharbrough, and Busacker, 1997). Figure 3 shows a block diagram of the EMG removal process. Recorded multi-channel noisy EEG Artifactcorrected EEG unmixing mixing Principal or independent components Remaining components Remove selected components Figure 3. Block diagram of EMG artifact removal using ICA or PCA.

5 2.3 Artifact Detection in EEG Using Sample Statistics A method for detecting other types of artifacts in the EEG using simple statistics is being investigated. A sample set of EEG was recorded and the means and standard deviations of each EEG channel were computed. As new data are collected these means and standard deviations were used to detect artifacts. The artifacts that can be detected are the result of muscle activity, movement and other spikes in the signal as well as railed amplifiers. The principle being used with this method is the assumption of Gaussian distribution of the data. The data within three standard deviations of the mean account for 99.7 percent of the data collected. The assumption is that data outside this range are considered outliers and are associated with artifact. A sample or training set was processed to extract the features, e.g., power of F7 alpha band, and averaged over a one second interval. The means and standard deviations of the averaged features were computed as baseline statistics for subsequent data. The values of the subsequent or new data which lie outside the range of three standard deviations from the mean were tagged as artifact. The data tagged as artifact were visually inspected for accuracy. 3 Results and Discussion 3.1 Eye Artifact Correction of EEG The ocular artifact correction algorithms were compared using the simulated EEG signal contaminated with EOG noise. Analysis was conducted in both time domain and frequency domain. For the time domain analysis, the total squared error between each true EEG waveform and the corrected EEG waveform, either by using the regression method or the adaptive filter method, is first calculated. The results from 2 simulated waveforms are then averaged to produce the mean square error (MSE). For the adaptive filter method, the results (Table 1) are shown for filter length M = 1, forgetting factor λ = 1 and for M = 3 and λ =.999. The adaptive filter method produces the best results with a 63 percent reduction in MSE over the regression method. Table 1. Time Domain Comparison M λ MSE Regression Method Adaptive Filter Method For the frequency domain analysis, the mean spectral difference (MSD) between the true EEG and the corrected EEG are computed for three frequency bands: theta band ( Hz), alpha band ( Hz), and beta band ( Hz). The results are shown in Table 2. The adaptive filter method shows a 66 percent reduction in error for the theta frequency band, a 74 percent reduction for the alpha band, and a 9 percent reduction for the beta band. An example of the signal processing results using the adaptive filter method is shown in Figure 4.

6 Table 2. Frequency Domain Comparison M λ MSE Theta Alpha Beta Regression Method Adaptive Filter Method The adaptive filter method is superior to the regression method for the following reasons. The method does not need prior calibration. The method is extremely stable. The only two parameters, M and λ, can be chosen within wide ranges (M: 1 15, λ:.99 1.) without compromising the performance of the method. The method is fast converging and is suitable for real-time application The method is inherently adaptable to the changing measurement condition, e.g. the change of the contact impedance between the tissue and the electrode, while maintaining its effectiveness in removing EOG noise original F7 3-2 corrected F HEOG 25 VEOG Figure 4. Demonstration of EOG removal using adaptive filtering.

7 Figure 5. Original EEG recording containing visible EMG noise in Fp1, Fp2, F7, and T EMG Artifact Removal for EEG Figure 5 shows the original EEG signal containing visible EMG contamination. The EEG channels containing the most significant contamination are Fp1, Fp2, F7, and T3. The results of PCA are reported in Figures 6 and 7. Figure 6(a) shows the spatial map of the 19 principal components, and Figure 6(b) is a plot of the first 1 principal components. Figure 7 is the reconstructed EEG waveforms for select channels. The spatial filter is constructed from the remaining principal components after removing components 1, 5, and 7 which contain the signal components of eye and muscle artifact. (a) (b) Figure 6. PCA - Scalp map of the 19 principal components (a) and waveforms of the first 1 components (b).

8 Figure 7. Reduction of EMG noise using PCA by removing components 1, 5, & 7. The results of ICA are displayed in Figures 8 and 9. Figure 8(a) shows the spatial map of the 19 independent components, and Figure 8(b) is a plot of the components. Figure 9 is the reconstructed EEG waveforms for select channels. The spatial filter is constructed from the remaining components after removing components 1, 5, and 9 which contain the signal components the artifact. (a) Figure 8. ICA - Scalp map of the 19 independent components (a) and the actual waveforms (b). (b)

9 Figure 9. Reduction of EMG noise using ICA by eliminating components 1, 5, & 9. Both ICA and PCA require offline process and a high degree of human involvement in selecting the components for removal. Both methods reduce EMG but cannot completely remove it. Additionally, both methods will remove components of the EEG signal as well as the artifact. More research is needed to develop a method for automatic online removal of EMG noise. 3.3 Artifact Detection in EEG Using Sample Statistics Figure 9 displays the output of the sample statistic artifact detection for electrode F7. The one second power average for each frequency band are displayed in the left column, the raw signal is at the top of the display, and the plots in the right column are the filtered raw EEG for each frequency band. The one second power average plots are bound by a dashed line indicated three standard deviations from the mean for each frequency band. The data outside this range are considered artifact. The raw signal indicates a spike in the signal possibly due to movement followed by muscle activity. The one second power average at time 127 for the beta and gamma bands are outside the range and tagged as artifact. The corresponding filtered signals are also tagged for that one second of data. This technique is presented for consideration and a thorough evaluation must be conducted to determine the utility of this method. Using the statistics of the signal is more advantageous than simply determining an arbitrary threshold since psychophysiological signals may tend to drift over time and can vary from day to day. This technique also has the advantage of simplicity. Artifacts are determined for each feature based on their statistics, therefore, only the contaminated frequency bands are tagged. Other frequency bands are available for use in classification. A disadvantage to this technique is the necessity for calibration, i.e., data must be recorded to determine the statistics.

10 Raw Data F7 delta F7 theta F7 alpha F7 beta F7 gamma Figure 9. Sample artifact detection using means and standard deviations. 4 References Gasser, T., Sroka, L., & Möcks, J. (1985). The transfer of EOG activity into the EEG for eyes open and closed. Electroencephalography and Clinical Neurophysiology, 61, He, P., Wilson, G., & Russell, C. (24). Removal of ocular artifacts from electroencephalogram by adaptive filtering. Medical & Biological Engineering & Computing, 42, Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., McKeown, M. J., Iragui, V., & Sejnowski, T. J. (2). Removing electroencephalographic artifacts buy blind source separation. Psychophysiology, 37, Kay, S. (1988). Modern spectral estimation: theory and application. Prentice-Hall, Englewood Cliffs, NJ. Lagerlund, T. D., Sharbrough, F. W., & Busacker, N. E. (1997). Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Journal of Clinical Neurophysiology, 14, Widrow, B. and Stearns, S. D. (1985). Adaptive signal processing, Prentice-Hall, Englewood Cliffs, NJ.