A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform

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1 A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform Bruno Azzerboni 1, Giovanni Finocchio 1, Maurizio Ipsale 1, FabioLaForesta 1, and Francesco Carlo Morabito 2 1 DFMTFA, Universitá degli Studi Di Messina salita Sperone, 31 C.P. 57, Messina, Italy {azzerboni,finocchio,ipsale,laforesta}@singegneria.unime.it 2 DIMET, Universitá Mediterranea via Graziella Loc. Feo di Vito, Reggio Calabria, Italy morabito@ing.unirc.it Abstract. Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (semg) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw semg recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of semg (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals. Keywords: Surface EMG,ICA. 1 Introduction Monitoring the electrical activity of the muscle with Electromyography (EMG) can be used for exploring neuroscience questions about motor control and control of rehabili-tation devices. Clinically, needle EMG is used extensively for assessment of diseases of the peripheral nerves and muscle. However, since groups of muscles tend to be controlled by neural systems, multiple simultaneous recordings from several muscles are desirable. Surface EMG recordings provide a practical means to record from sev-eral muscles simultaneously but tend to be unreliable, i.e. recordings from a subject performing the same movement repetitively tend to have considerable trial-to-trial variability. SEMG recordings are also affected by cross-talk whereby several mus-cles may contribute to the recording of a given electrode, making the source of the signal difficult to be identified. Recently, Independent Component Analysis (ICA) has been proposed as a method to analyze semg recordings, which addresses many of these concerns. SEMG ICs have been shown to be more reliable that raw semg re-cordings [1] and M. Marinaro and R. Tagliaferri (Eds.): WIRN VIETRI 2002, LNCS 2486, pp , c Springer-Verlag Berlin Heidelberg 2002

2 110 Bruno Azzerboni et al. correspond better with ongoing brain-wave activity (measured with the EEG) than the individual semg recordings [2]. 2 Independent Component Analysis Independent Component Analisys (ICA) is a new statistical technique that aims at trasforming an input vector into a signal space in which the signals are statistically in-dependent. The drawback of ICA, namely the need of high order statistics in order to determine ICA expansion, is counterbalanced by its performances, which are more meaningful compared with other methods like PCA - Principal Component Analysis (see figure 1) Let x k =[x k (1),...,x k (M)] T T be a set of km-dimensional data vector corresponding to the electrode signals. We can write the ICA signal model in the vector form: x k = As k (1) Here s k is the source vector consisting of the independent signal components, s k (i), i =1,...,N, A =[a(1),...,a(n)] is a constant M N mixing matrix whose columns a(i) are the basis vectors of ICA. The aim of source separation is to determine s k, knowing only x k. Tipically, the basis vectors a(i) are normalized to unit length and they are not mutually orthogonal. The complete procedure is implemented by using a feed forward scheme. The Neural Network (NN) inputs are the M components of vector x. ThereareN nodes in the hidden layer. The first layer of weights carries out a M N whitening and compression of the input vector. The sources are then separated by means of an orthonormal Fig. 1. The effect of projecting measurement data on the PCs and on the ICs. We can notify that PCA detect only orthogonal directions, ICA is able to capture the directions of maximum variance

3 A New Approach to Detection of Muscle Activation 111 Fig. 2. Topology of feed forward Neural Network that approaches ICA matrix (WTW=IN) that the NN should learn. The ICA network, first proposed by Karhunen (1997), is showed in figure 2. Nonlinearity (i.e., hyperbolic tangent function) is used in learning the separating matrix. The learning algorithm can be summarized as follows: whitening the original data by v = D 1/2 E T x, where E is the matrix of the eigenvectors of the original data x and D is the diagonal matrix of eigenvalues that produces a starting point for an iterative process that finds vector W. The learning rule is: W (k +1)=E{vg(W (k) T v) g(w (k) T v)w (k)} (2) where vg( ) is the hyperbolic tangent. After finding W, the IC s are found using the linear transformation WTv and the mixing matrix A by A = ED 1/2 W.By this procedure, the ICA network allows us to determine the separating matrix. 3 Surface EMG Processing The knowledge of individual muscles activity is important to detect muscle activation intervals. We propose to use multivariate signal processing techniques like Independent Component Analysis (ICA), in order to estimate the information content in the semg signals. SEMG was recorded from a single subject performing a reaching task. The subject faced a computer screen, with their right hand in a supinated position in front of them. The subject was then asked to point to the left side of the screen and return, and then point the right side of the screen, and so on. Up to 50 complete cycles were performed. Visual cues in the form of laterally-moving and shrinking circles were used to pace the movements and provide a target at the edges of the screen. SEMG signals were recorded from 16 electrodes distributed over the chest, shoulder arm and forearm (figure 3), amplified, and sampled at 1kHz. The Independent Components of the

4 112 Bruno Azzerboni et al. Fig. 3. SEMG signals. Mapping of electrodes is shown on top of the figure. In the bottom, each row represents the output of each electrode semg have been shown to better correspond with brain activity compared to the activity from individual muscles. Further they are able to distinguish between similar motor movements, and allow for a straightforward computation of information content [1]. The first step of our procedure consists in a reduction of data dimensionality, achieved by Principal Components Analysis to capture an arbitrary percentage (e.g., > 95) of the variance of the data [1],[2]. Subsequently ICA application [2] allows the EKG artefacts to be removed and the individual muscles activity to be identified. The ICs from a 10-sec portion of recordings are shown in figure 4. Note the isolation of obvious semg activity (ICs [1],[3],[4],[5],[?]) from artifact (ICs [2], [6] and[?]). Although the first IC was clearly modulated by the pointing task (figure 4), it was also different from trial to trial. As such, this IC was selected for further processing. Then, wavelet analysis is used to detect the activation instant of single muscles. Fig. 4. Individual muscles activity detected by ICA. The rows represent the ICs of semg. The ICs 2,6 and 8 are artifacts, the others ICs are the individual muscles activity

5 A New Approach to Detection of Muscle Activation Independent Components Processing: Time-Scale Decomposition The most common method to detect the muscle activation is visual inspection of EMG signal (off line condition). Another approach is a single threshold method which compare the EMG signal to affixed threshold. Recently, doublethreshold detector has been proposed to improve the detection [5]. We propose a new method in which the activation instant of single muscles can be determined by means of time-scale decomposition of ICs, calculated from non-invasive technique semg. To performe this decomposition, the discrete wavelet transform (dwt) is applied; this method is proposed to overcome the limitations of the traditional time-frequency methods. The wavelet transform acts as a mathematical microscope in which one can observe different parts of the signal by adjusting the focus. This allows the detection of short-lived time components in the signal. This adapted method is logical since high-frequency components such as short burst need high time resolution as compared with low-frequency components, where a detailed frequency analysis is often desired. In the next subsections we will show how the dwt allows the determination of that portion of the ICs that are highly reliable (we named this wave envelope); subsequently the algorithm to detect the muscle activations will be showed. 4.1 Wave Envelope Extraction In order to extract the wave envelope, we implemented an algorithm that optimize the choice of approximation level to fit muscle activation cycles. The first step of optimization procedure consists in a ninth level decomposition of the first IC; for example, the figure 5a shows a third level decomposition: if S was an Independent Component, we can apply a bank filter to extrapolate a low pass approximation A3 and three high pass details D1, D2, D3. This decomposition is performed by means of the Daubechies mother wavelet (see figure 5b). Fig. 5. Wavelet decomposition (a) and the mother wavelet Daubechies (b)

signal, A9 (see figure 6). The figure 7 show the obtained signal; it represents the low frequency portion of the ICs that can be used to detect the information of cyclical repetition of the movement.

6 114 Bruno Azzerboni et al. Fig. 6. Wavelet decomposition and wave envelope extraction by means of threshold algorithm Next the algorithm adds details, with information content higher than a fixed threshold, to the ninth approximation signal, A9 (see figure 6). The figure 7 show the obtained signal; it represents the low frequency portion of the ICs that can be used to detect the information of cyclical repetition of the movement. 4.2 Muscle Activation Detection The ICs of semg signals contains many bursts of high frequency. In our analysis each burst could be erroneously interpreted as the beginning or the end of muscle contraction. For these reasons we can notice the usefullness of wave envelope in which the bursts are a lot less than before. In this signal, the muscle activation intervals can be detect by means of a threshold processing. It is important to observe that an elaboration of this type couldn t be implemented directly in Fig. 7. The wave envelope of IC1 calculated by mean of wavelet decomposition

7 A New Approach to Detection of Muscle Activation 115 Fig. 8. Muscle activation intervals algorithm. The figure shows the three levels of threshold: max level, threshold and min level ICs just because of its high frequency content. Let us describe our threshold algorithm. It starts with a training procedure in order to calculate the thresholds that it will utilise in a second phase. In particulary, three levels are detected: a threshold, a max level and a min level (see figure 8). The threshold level represents the boundary between the beginning and the end of contracion. To avoid oscillations around the threshold not wished, that don t correspond to an effective activation (or deactivation), we introduced the other levels. When the muscle is active, it can t deactivate if it didn t exceed max level. Similary, when the muscle isn t active, it can t activate if it didn t come down under min level. These levels are calculated as follows. The threshold is related to mean value of wave envelope. Max level is the smallest of the maximum values of the wave envelope signal. Min level is the largest of the minimum values of the signal. These levels are recalculated every prefixed number of contractions, in order to obtain a real time performance very reliable. The figure 9 shows the obtained results by means of algorithm application to first IC after the training phase applied on five contractions. It determines that first contraction starts at 0.32 seconds and it ends at 1.83 seconds. Second contraction starts at 2.00 seconds and it ends at 3.54 seconds. Obviously, these informations are sufficient Fig. 9. The activation intervals of IC1. First contraction starts at 0.32 seconds and it ends at 1.83 seconds. Second contraction starts at 2.00 seconds and it ends at 3.54 seconds

8 116 Bruno Azzerboni et al. to calculate the duration of contraction that, in our example, is equal to 1.51 seconds in first contraction and 1.54 seconds in second contraction. 5 Conclusions A fast and simple algorithm based on ICA and wavelet decomposition is used to process semg signal, in order to determine indivual muscles activity and activation intervals. The application of discrete wavelet transform (dwt) to the ICsallowsthewaveenvelopetobecalculated(seefigure7). Further, dwt allows us to isolate the wavelet approximation that characterize the arm movement. Muscle activation intervals can be identified by detecting the presence of the wavelet approximation related to the arm movement (see figure 9). References [1] McKeown, M. J., Torpey, D. C., Gehm W. C.: Non-Invasive Monitoring of Functionally Distinct Muscle Activations during Swallowing. Clinical Neurophysiology (2002). 109, 112 [2] McKeown, M. J.:Cortical activation related to arm movement combinations. Muscle Nerve. 9:19-25 (2000). 110, 112 [3] Jung T. P., Makeig S., McKeown M. J., Bell A. J., Lee T. W., Sejnowski T. J.: Imaging bra-indynamics using independent component analysis. Proc. IEEE. 89(7): , (2001). 112 [4] Bell A. J., Sejnowski T. J.: An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7: , (1995). 112 [5] Micera S.,Vannozzi G., Sabatini A. M., Dario P.: Improving Detection of Muscle Activation intervals, IEEE Engineering in Medicine and Biology, vol. 20 n.6:38-46 (2001). 112, 113 [6] Karhunen J., Oja E.: A Class of Neural Networks for Independent Component Analysis, IEEE Transactions on Neural Network, vol. 8 n. 3: , (1997). 112

INDEPENDENT COMPONENT ANALYSIS AND DISCRETE WAVELET TRANSFORM FOR ARTIFACT REMOVAL IN BIOMEDICAL SIGNAL PROCESSING

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