CHAPTER 6 INTERFERENCE CANCELLATION IN EEG SIGNAL

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1 116 CHAPTER 6 INTERFERENCE CANCELLATION IN EEG SIGNAL 6.1 INTRODUCTION Electrical impulses generated by nerve firings in the brain pass through the head and represent the electroencephalogram (EEG). Electrical activity from the brain consists of rhythms and these rhythms are named according to their frequency range. Delta rhythm ranges from Hz, Theta ranges from 4-8 Hz, Alpha ranges from 8-13 Hz, Beta ranges from Hz, and Gamma rhythm is > 30 Hz. EEG is measured by electrodes placed on the scalp. Even after many years of research, contamination of EEG activity by eye movements, blinks, cardiac signals, muscle signal and line noise pose serious problem for the extraction of EEG. The analysis of EEG data and the extraction of information from this data are difficult due to the presence of both biologically generated and externally generated artifacts. The artifacts present in the EEG signal cannot be filtered directly because they turn into an immeasurable interference signal when they pass through the human body. Also, the spectrum of the EEG signal and the interference signal overlap each other. This chapter focuses on three topics, namely the significance of EEG signal, the artifacts in EEG signal and interference cancellation in EEG signal using artificial intelligence techniques. It also presents a performance comparison of these techniques.

2 NEED FOR EEG SIGNAL The EEG is a noninvasive method of determining cerebral function. EEG measurements are required to monitor alertness, coma, and brain death, to locate areas of damage following head injury, stroke, and tumor, to test afferent pathways for controlling anesthesia depth and to investigate epilepsy and locate seizure origin. Normally, EEG signals are measured from electrodes placed on the scalp. The amplitude of EEG signal is very small in the order of microvolts. 6.3 ARTIFACTS IN EEG SIGNAL The EEG data are contaminated with various artifacts, both from the patient and equipment interferences, which reduce their clinical applications and present serious problems for electroencephalographic interpretation and analysis. Externally generated artifacts can be decreased by using appropriate techniques, but biologically generated artifacts must be removed after the recording process. The amplitude of normal EEG is in the order of µv. However, artifacts such as eye movements and eye blinks tend to be in the range of mv. Artifacts in EEG are commonly handled by discarding the affected segments of EEG. The simplest approach is to discard a fixed length segment, for about one second, from the time an artifact is detected. Discarding segments of EEG data with artifacts greatly decrease the amount of data available for analysis. EEG data collected from children are especially problematic in this respect. Artifacts are categorized into physiological and non-physiological artifacts. Non physiological artifacts like movement artifact, sweat artifact, and power line interference arise due to activities outside the body. An EEG technician reduces such artifacts to the extent possible before he starts

3 118 recording. Artifacts that originate from sources inside the body are called physiological artifacts. They include the normal electrical activity of the heart, muscles, and the eyes. This work mainly concentrates on the cancellation of physiological artifacts in EEG signal Motion Artifact Movements of the patient generate signals called motion artifact. It may be as large as 14 mv and are usually contained within the frequency range of 1 to 10 Hz. The nature and the localization of the artifact on the scalp electrodes depend on the movement of the body part, the strength of the movement, and the relative location of the electrode wires. It mainly occurs due to electrode metal-to-solution interface and skin-stretch. Motion artifact due to electrode metal-to-solution interface is negligible with paste-filled recessed Ag / AgCl electrodes. A majority of the motion artifacts occurs due to skin stretch. It can be avoided by approaches like abrading the skin at the electrode site and using electrodes that puncture the skin. However, skin abrasion requires some experience on the part of the technician as too much abrasion can lead to skin irritation and too little abrasion will not reduce the noise significantly Sweat Artifact Sweat is a common cause of skin artifact. Sodium chloride and lactic acid caused due to sweating react with metals of the electrodes and produce huge slow baseline sways. It is an extremely low frequency signal typically in the range of 0.25 to 0.5 Hz. They can be easily removed with a HPF.

4 Power Line Interference The power line interference is one of the major artifacts in the EEG signal. It is due to the activity of nearby electrical equipment that operates at 50 Hz. The frequency range of the EEG signal is typically well below the 50 Hz. Hence, a LPF is used to eliminate this unwanted interference ECG Artifact People with short and wide necks have the largest ECG artifacts on their EEGs. These are more prominent in obese patients, and babies who have their heads close to the thorax EMG Artifact It is caused by muscle activity in different muscle groups including neck and facial muscles. Clenching of jaw muscles is a common cause of EMG in EEG signal. The potentials generated in the muscles are of shorter duration than those generated in the brain and are identified easily on the basis of duration, morphology, and frequency. It can be reduced by ensuring that the patient is relaxed EOG Artifacts Human eye contains an electrical dipole formed by a positive cornea and a negative retina, and there is a potential difference of about 100 mv between these two opposite charges. Eye blinking or movement artifacts are caused by the reorientation of the retinocorneal dipole and they produce large electrical potential around the eyes known as EOG. The shape of the EOG waveform depends on factors such as the mechanism of origin and the

5 120 direction of eye movements. Vertical, horizontal and round eye movements produce square shaped EOG waveforms while blinks produce spikes. EOG makes it difficult to distinguish normal brain activities from abnormal ones. It produces a high amplitude signal of mv that can be many times greater than the EEG signals of interest. Eye blink corrupts the EEG signal on all electrodes, even those at the back of the head because of its high amplitude Glossokinetic Artifact The artifact produced by the tongue is called glossokinetic artifact. It has a broad potential field that drops from frontal to occipital areas. The frequency is variable but is usually in the delta range. Chewing and sucking can also produce similar artifacts. These are observed commonly in young patients. However, they can also be observed in patients with dementia or those who are uncooperative Respiration Artifact Respiration produces artifact in the form of slow and rhythmic activity, synchronous with the body movements of respiration and mechanically affecting the impedance of one electrode. It can also produce slow or sharp waves that occur synchronously with inhalation or exhalation. Several commercially available devices to monitor respiration are coupled to the EEG machine in order to reduce the effect of respiration artifact. 6.4 SELECTION OF SIGNALS EOG, EMG, and ECG signals are the artifacts considered to perform interference cancellation in the measured EEG signal. The characteristics of the signals chosen are given below.

6 121 C3/A2 and C4/A1 EEGs, sampled at 125 Hz. This is assumed to be the measured signal. Right and left electrooculograms (EOG-R, EOG-L), sampled at 50 Hz. A bipolar submental EMG, sampled at 125 Hz. These signals are taken from physiobank data base. EEG contaminated with artifacts (such as EOG-R, EOG-L, and EMG) is shown in Figure 6.1. The proposed AI techniques are applied to cancel the interferences from this data and the results obtained are discussed in the next section. Figure 6.1 EEG signal 6.5 EOG-RIGHT ARTIFACT CANCELLATION IN EEG SIGNAL The major source of noise in EEG signal is the Electrooculogram (EOG) which is contributed by left and right eye. The EOG-Right signal used for AIC is shown in Figure 6.2. The cancellation of EOG-Right artifact in EEG signal (shown in Figure 6.1) using four AI techniques is explained in the following sections.

7 122 Figure 6.2 EOG-Right signal AIC using BPN The flowchart given in Figure 5.2 is again used for the cancellation of EOG-Right interference in EEG. The parameters used for training BPN to cancel the EOG-Right artifact are epochs = 30, goal =0.65, momentum=0.9, show = 5, time = infinity, and learning rate = 0.5. The BPN architecture has two neurons in the input layer, 53 neurons in the only hidden layer and one neuron in the output layer. The EOG-Right artifact and delayed EOG-Right artifacts are given as two inputs. The measured EEG signal is the target in the training process. The training stops as soon as the performance goal (Mean Square value of the estimated EEG) reaches a minimum or the maximum number of epochs is reached. The result of AIC using BPN is shown in Figure 6.3. The EOG- Right signal recorded using appropriate electrodes near the right eye is shown in Figure 6.3(b). The estimated EOG-Right interference in EEG using BPN is shown in Figure 6.3(c). Estimated EEG signal using AIC is shown in Figure 6.3(d). The estimated EEG signal is passed through a Butterworth filter of

8 123 order 5 and normalized frequency of 0.9.The filtered output is subtracted from the estimated EEG to get the noise, which is shown in Figure 6.3 (e). Figure 6.3 Results of AIC in EEG using BPN (a)contaminated EEG (b) EOG-Right (c) Estimated EOG-Right in EEG (d) Estimated EEG (e) Noise after AIC AIC using CCN The parameter values used for training CCN are same as that used for training BPN. The CCN architecture has two input nodes and one output node. It is assumed that 45 hidden nodes (arranged in to three sets with each set containing 15 nodes) are available for selection. Then, the set which has the highest covariance is selected and the other sets are rejected. Since the net has to adjust the weight values only for 15 nodes, the convergence time is less than that with BPN. The result of AIC using CCN is shown in Figure 6.4. It is

9 124 noted from Figure 6.4(e) that the noise generated using CCN is slightly less than that with BPN. Figure 6.4 Results of AIC in EEG using CCN (a) Contaminated EEG (b) EOG - Right (c) Estimated EOG - Right (d) Estimated EEG (e) Noise after AIC AIC using ANFIS The parameters used for ANFIS training are, number of nodes = 53, number of linear parameters = 48, number of nonlinear parameters = 24, total number of parameters = 72, number of training data pairs = 450 and number of fuzzy rules =16. The results of AIC using ANFIS are shown in Figure 6.5. It may be observed from Figure 6.5(e) that the noise generated using ANFIS is slightly less than that with CCN.

10 AIC using ANFIS-FCM Four clusters are used in ANFIS-FCM in addition to the parameter values used for ANFIS. The number of samples in each cluster varies based on the cluster centroid and the nature of the signal characteristics. The results of AIC using ANFIS-FCM are shown in Figure 6.6. The arrows in Figure 6.6(a) and 6.6 (c) show the presence of EOG-Right artifact. But the arrows in Figure 6.6(d) clearly indicate the removal of EOG-Right artifact after AIC. Further, the magnitude of the noise is very much reduced as seen from Figure 6.6 (e). Figure 6.5 Results of AIC in EEG with ANFIS (a) Contaminated EEG (b) EOG - Right (c) Estimated EOG - Right (d) Estimated EEG (e) Noise after AIC

11 126 Figure 6.6 Results of AIC in EEG using ANFIS-FCM (a) Contaminated EEG (b) EOG-Right (c) Estimated EOG-Right (d) Estimated EEG (e) Noise after AIC Performance Comparison Quantitative analysis of the different AI techniques used for EOG - Right interference cancellation in EEG signal is given in Table 6.1. It shows that Mean Square value of the estimated EEG signal and convergence time are less when ANFIS-FCM technique is used. Also SNR is maximal for the same technique.

12 127 Table 6.1 Quantitative analysis of various AI techniques used for AIC in EEG Mean Square value of SNR Convergence Sl.No Technique the estimated EEG signal (db) Time (s) 1 BPN CCN ANFIS ANFIS-FCM Since ANFIS-FCM produces better results for cancelling EOG-Right artifact, the same technique is used for removing other artifacts like EOG-Left, ECG and EMG in the EEG signal. 6.6 EOG-LEFT ARTIFACT CANCELLATION IN EEG SIGNAL The EOG-Left signal is recorded with appropriate electrodes near the left eye and is shown in Figure 6.7. The method used for the cancellation of EOG-Left is same as that used for the cancellation of EOG-Right. The parameter values used for training ANFIS-FCM for the cancellation of EOG-Left are again the same as that used for training ANFIS-FCM for the cancellation of EOG-Right. The results of cancellation of EOG-Left using ANFIS-FCM are shown in Figure 6.8. It is inferred from Figure 6.8 that ANFIS-FCM is able to cancel the EOG-Left successfully.

13 128 Figure 6.7 EOG-Left signal Figure 6.8 Result of AIC of EOG-Left in EEG using ANFIS-FCM (a) Contaminated EEG (b) EOG-Left (c) Estimated EOG-Left (d) Estimated EEG (e) Noise after AIC

14 EMG INTERFERENCE CANCELLATION IN EEG The EEG and EMG signals are recorded simultaneously for the same patient in order to cancel EMG interference in EEG signal. The recorded EEG and EMG signals are shown in Figure 6.9(a) and 6.9 (b) respectively. The EMG signal and its delayed version are given as inputs to the ANFIS- FCM for training. The parameters used for ANFIS-FCM training are the number of nodes = 35, number of linear parameters = 27, number of nonlinear parameters =18, number of training data pairs = 450, and number of fuzzy rules used = 9. The results are shown in Figure 6.9. It is concluded that the ANFIS-FCM is able to cancel the EMG interference to an acceptable level. Figure 6.9 Results of AIC of EMG in EEG using ANFIS-FCM (a) Contaminated EEG (b) EMG artifact (c) Estimated EMG in EEG (d) Estimated EEG (e) Noise after AIC

15 CONCLUSION The artifacts in EEG signal cannot be filtered directly because these artifacts pass through the human body and turn into an interference component, which is mixed with the EEG signal. This interference component cannot be estimated directly because the spectrum of the EEG signal and the interference signal overlap each other. Four AI techniques are used to estimate the unknown interference and to retrieve the required EEG signal. The performance comparison of BPN, CCN, ANFIS and ANFIS-FCM techniques has been made by visual inspection and quantitative analysis for the cancellation of EOG-Right artifact. The results obtained indicate that ANFIS-FCM gives better performance in terms of minimum Mean Square value of the estimated EEG, less convergence time and high SNR.