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Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network Gundlapalli Venkata Sai Karthik, Student Member, IEEE, Shaik Yasmin Fathima, Muhammad Zia Ur Rahman, Member, IEEE, Shaik Rafi Ahamed, Member, IEEE, and Aimé Lay-Ekuakille, Senior Member, IEEE
Abstract— This paper proposes several efficient and less complex signal conditioning algorithms for brain signal enhancement in remote healthcare monitoring applications. In clinical environment during electroencephalogram (EEG) recording, several artifacts encounter and mask tiny features underlying brain wave activity. Especially in remote clinical monitoring, low computational complexity filters are desirable. Hence, in our paper, we propose various efficient and computationally simple adaptive noise cancelers for EEG enhancement. These schemes mostly employ simple addition and shift operations, and achieve considerable speed over the other conventional realizations. We have tested the proposed implementations on real brain waves recorded using emotive EEG system. Our experiments show that the proposed realization gives better performance compared with existing realizations in terms of signal to noise ratio, computational complexity, convergence rate, excess mean square error, misadjustment, and coherence. Index Terms— Adaptive noise cancelers, artifact, brain wave, signal conditioning, remote health monitoring, LMF algorithm.
I. I NTRODUCTION
T
HE electroencephalography (EEG) is a tool to study the brain activity dynamics non-invasively. However during acquisition the EEG signal encounters various artifacts, which degrades the feature resolutions of the signal. The predominant artifacts are Power Line Noise (PLN), Eye Blink Artifact (EBA), ElectroMioGram (EMG), Cardiac Signal Artifact (CSA), Respiration Artifact (RA) and Electrode Motion Artifact (EMA). In this aspect to facilitate the neurologist for accurate diagnosis these artifacts have to be eliminated. Therefore extraction of high-resolution EEG signals from the background contaminations is an important
Manuscript received January 15, 2013; revised April 30, 2013; accepted June 17, 2013. Date of publication June 26, 2013; date of current version July 30, 2013. The associate editor coordinating the review of this paper and approving it for publication was Dr. Soo-Young Lee. G. V. S. Karthik is with the Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641013, India (e-mail:
[email protected]). S. Y. Fathima is with the Department of Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur 522002, India (e-mail:
[email protected]). M. Z. Ur Rahman is with the Department of Electronics and Communication Engineering, K. L. University, Guntur 522502, India (e-mail:
[email protected]). S. R. Ahamed is with the Department of Electronics and Communication Engineering, Indian Institute of Technology, Guwahati 781039, India (e-mail:
[email protected]). A. Lay-Ekuakille is with Department of Innovating Engineering, University of Salento, Lecce 73100, Italy (e-mail:
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2013.2271042
issue to investigate. The goal of brain signal enhancement is to separate the valid signal components from the undesired artifacts and to present an EEG that facilitates easy and accurate interpretation. As the statistical nature of the artifacts is random, fixed coefficient filters are not suitable. The filter coefficients need to be adjusted automatically based on the noise component. So we need to develop efficient adaptive noise cancelers. However in practical scenarios, when the patient is in remote location where a specialist of neurology is not available, biotelemetry based remote acquisition systems plays a vital role in health care monitoring. In a typical biotelemetry system the EEG recorder is interfaced with a computer to establish Brain Computer Interface (BCI). Several BCI systems are presented in literature [1]–[4]. Therefore the acquisition system, biotelemetry link, BCI, control station at the hospital establish a remote health monitoring network. In literature several contributions on EEG enhancement using both adaptive and non-adaptive techniques are presented [5]–[16]. Less computational complexity is desirable for a noise cancelation system, particularly in applications such as wireless biotelemetry system, has remained a topic of intense research. As the EEG data transmission rate increases, the receiver filter’s impulse response length increases and thus the order of the filter increases. The resulting increase in complexity makes the real time operation of the biotelemetry system difficult, especially in view of simultaneous shortening of the symbol period, which means that lesser time will be available to carry out the computations while the volume of the computations goes on increasing. Thus far, to the best of the authors knowledge, no effort has been made to reduce the computational complexity of the ANC in the context of brain signal enhancement. Recently Rahman et al. [17]–[18] used less computational complexity techniques with a combination of Least Mean Square (LMS) algorithm to enhance cardiac signal. The computational complexity can be reduced by using the sign based algorithms, namely, the signed clipped algorithm, the sign error algorithm and the sign-sign algorithm [19]. All these three require only half as many multiplications as the LMS algorithm, thus making them attractive from practical implementation point of view. However in gaussian environment, when the signals are transmitted through free space, adaptive filters with higher orders perform better than LMS category [20]. One such technique is Least Mean Fourth (LMF) algorithm, which exhibits lower steady state error than the conventional LMS algorithm in noise reduction applications [21]. The hybrid version of
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becomes e(n) = [E E G 1 (n) + N1 (n)] − y(n). Where, y(n) is the adapted FIR filter output and it is given by, y(n) = wt (n)r(n),
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The mean-squared error (MSE) is calculated as, E[e2 (n)] = E{[E E G 1(n) − y(n)]2 } + E[N12 (n)]
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Structure of adaptive noise canceler.
LMF and sign algorithms results Sign Clipped LMF (SCLMF), Sign Error LMF (SELMF) and Sign Sign LMF (SSLMF) [22]. In order to cope with both the complexity and convergence issues without any restrictive tradeoff we propose various adaptive filter structures based on normalization of data vector and block based approach. These combinations result in six simplified adaptive algorithms namely, Normalized Clipped LMF (NCLMF), Block Based NCLMF (BBNCLMF), Normalized Sign Error LMF (NSELMF), Block Based NSELMF (BBNSELMF), Normalized Sign Sign LMF (NSSLMF) and Block Based NSSLMF (BBNSSLMF). To study the performance of the filter structures which effectively remove the artifacts from the brain signals we carried out experiments on real EEG signals recorded from humans. The theory of various techniques and experimental results are presented in the next sections. II. N ORMALIZED S IGN BASED A DAPTIVE N OISE C ANCELERS FOR EEG T ELEMETRY Let us consider a FIR filter with L taps. We choose LMF algorithm for the adaptation of filter weight coefficients. Using this LMF based adaptive filter we constructed an ANC associated with an Emotive EEG recording system interfaced with a computer, the ANC structure is shown in Fig. 1. The input sequence to the adaptive filter is r (n) based on which the filter coefficients should be adjusted, desired signal is x(n) which is recorded from patient, the weight update recursion is given as, (1) w(n + 1) = w(n) + µ r(n) e(n)3, where, w(n) = [w0 (n) w1 (n) · · · w L−1 (n)]t is the tap weight vector at the nth index, r(n) = [r (n) r (n−1) · · · r (n−L +1)]t , error signal e(n) = x(n)−wt (n) r(n) and µ denoting step-size parameter. In order to remove the noise from the brain signal, the EEG signal E E G 1 (n) corrupted with noise signal N1 (n) is applied as the desired sequence x(n) to the adaptive filter shown in Fig. 1. The reference signal r (n) is N2 (n) is a noise component and is correlated in some way with N1 (n). Now the filter error
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Since E E G 1 (n) and N1 (n) are uncorrelated, similarly N1 (n) and y(n) are uncorrelated the last two expectations are zero. Minimizing the MSE results in a filter output which is the best least-squares estimate of the signal E E G 1 (n) [23]. The new proposed ANCs make use of the signum of either the error or the input data vector, or both [19], [22], have been derived from the LMF algorithm for the simplicity of implementation, enabling a significant reduction in number of multiplications and additions. The weight update relations of SCLMF, SELMF and SSLMF algorithms are given as follows, w(n + 1) = w(n) + µ SG N{r(n)}{e(n)3 }, w(n + 1) = w(n) + µ {r(n)}SG N{e(n)3 },
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and w(n + 1) = w(n) + µ SG N{r(n)}SG N{e(n)3 }. Where SG N{.} is the well known signum function, ie., ⎧ ⎫ ⎨ 1 : e(n) > 0 ⎬ SG N{e(n)} = 0 : e(n) = 0 ⎩ ⎭ −1 : e(n) < 0
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Among the adaptive algorithms presented above, the SCLMF, SELMF and SSLMF has a convergence rate and a steady-state error that are slightly inferior to those of the LMF algorithm. This can be explained as follows, Consider the SELMF algorithm with recursion equation, (8) w(n + 1) = w(n) + µ{r(n)}{e(n)3/ e(n)3 }, Since SG N[e(n)] = e(n)/|e(n)|. This is rearranged as,
µ w(n + 1) = w(n) + (9) r(n) e(n)3, |e(n)|3 From the above equation it is clear that LMF algorithm with sign error resembles a variable step size algorithm, µ (n) = {µ/e(n)} [18]. The µ (n) increases, on an average, as the sign algorithm converges, since e(n) decreases in magnitude. But as the filter converges and e(n) becomes smaller in magni tude, µ (n) becomes larger and increases convergence rate. By setting µ to a value of power of two makes the hardware circuit simplified with only shift, addition and subtraction operations [19]. Normalized LMF (NLMF) algorithm is a fast convergent adaptive algorithm in which the step size parameter is normalized with respect to input data vector of noisy signal [24]–[28]. The weight update relation for NLMF algorithm is as follows,
µ (10) r(n) e(n)3 , w(n + 1) = w(n) + c + rt (n) r(n) where the variable step size parameter can be written as, µ (11) µ(n) = t c + r (n) r(n)
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w(n + 1) = w(n) + µ(n) SG N{r(n)}{e(n) } , w(n + 1) = w(n) + µ(n) {r(n)}SG N{e(n)3 } ,
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Here µ is fixed step size as in LMF filter. The parameter c is set to avoid denominator becoming too small and step size parameter too big. From the weight update recursions of both LMF and NLMF given in (1) and (10), the update equation of NLMF is a scaled version of LMF algorithm. The change in w(n) is inversely proportional to the norm of input data vector r (n). The input r (n) with a large normalized data quantity will cause a little change to w(n) than a small normalization quantity. This data normalization results smaller µ values than LMF. The normalized filter usually converges rapid than LMF filter, since it utilizes a variable convergence factor aiming at the minimization of the instantaneous output error [20], [21]. To achieve less computational complexity we combine NLMF algorithm and sign based strategies to obtain NCLMF, NSELMF and NSSLMF algorithms. The weight update recursions are written as,
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The additional computations required to compute µ(n) in equations (12)-(14) can be further reduced by using block based approach, in which the input data is partitioned into blocks and the maximum magnitude within each block is used to compute µ(n). With this, the weight update relations in (12)-(14) for r L i = 0 and c = 0 takes the following form, µ (15) w(n + 1) = w(n) + 2 SG N{r(n)}{e(n)3 } , rLi µ w(n + 1) = w(n) + 2 {r(n)}SG N{e(n)3 } , (16) rLi and
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Convergence characteristics for various versions of LMF algorithm.
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where, r L i = max{|rk |, k ∈ Z i }, Z i = {i L, i L + 1, . . . , i L + L − 1}, i ∈ Z . And for r L i = 0 and c = 0 the equations (9)-(11) become w(n+1) = w(n). These algorithms are known as BBNCLMF, BBNSELMF and BBNSSLMF respectively. Fig. 2 shows the convergence behavior of various algorithms discussed above. From these characteristics it is clear that NCLMF is a little bit inferior to NLMF. The computational complexity of various algorithms is given in Table I.
evaluate w(n + 1) from w(n) using equation (4), only L add with sign check (ASC) operations are required. But the rate of convergence of this algorithm is very slow. Hence, the SSLMF algorithm alone will not be a suitable technique for the removal of noise from the EEG signal. The NSSLMF algorithm, which is the combination of SSLMF and NLMF is very much suitable as this algorithm requires L shift L ASC operations in case of block based realization or if we choose the value of µ(n) as a power of two. From the Table I it is also clear that the number of computations required for the proposed block based NCLMF is independent of filter length L. Note that ASC and shift operations requires less logic circuitry when compared with MAC operations. Using a technique called distributed arithmetic (DA) we can compute the norm factor with zero multiplications [29]. III. S IMULATION R ESULTS
A. Computational Complexity Issues The sign based algorithms need little computations in terms of multiplications and additions. With less computational burden the proposed ANCs provide elegant means for enhancing brain activity signals. Table I provides the computational complexity of various adaptive algorithms. Among these algorithms NLMF is more complex, it requires 2L + 3 MACs and 1 division. The conventional LMF algorithm requires L + 3 MAC operations to implement the weight updating equation (1) on DSP processor. For SSLMF algorithm, to
To show that the proposed algorithms are really effective in clinical situations, the method has been validated using several EEG recordings with a wide variety of wave morphologies recorded using the Emotive EPOC headset [30] which has 14 data-collecting electrodes and 2 reference electrodes. The electrodes are placed in roughly the international 10-20 system and are labeled as such [31]. The headset transmits encrypted data wirelessly to a Windows-based machine; the wireless chip is proprietary and operates in the same frequency range as 802.11 (2.4GHz). Using this BCI we have recorded brain signals
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LMF NLMF NCLMF NSELMF NSSLMF BBNLMF BBNCLMF BBNSELMF BBNSSLMF
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Fig. 3. A typical brain waves recorded using Emotive EEG system with various artifacts: 1-blink of eye, 2-left side movement of eye, 3-top side movement of eye, 4-right side movement of eye, 5-bottom side movement of eye.
with various artifacts from 5 subjects. The headset samples all channels at 128 samples/second, each of which is a 4-byte floating-point number corresponding to the voltage of a single electrode. The data rate of the EEG data streamed from the relay laptop to the mobile phone is 4kbps per channel. In our simulation we have recorded 50,000 samples of brain activity signal from a male person aged 41. But due to space constraint, to show high resolution signal we have only used first 500 samples. Fig. 3 shows the BCI front view of the Emotive EEG recording system with various artifacts. For evaluating the performance of proposed filter structures we have measured Signal to Noi se Rati o I mpr ovement (S N R I ), E xcess Mean Squar e Err or (E M S E), Mi sad j ustment (M S D), Coher ence(C H O) in ten experiments [23]–[25], averaged and compared with conventional LMF based ANC. For all the figures number o f samples is taken on x-axis and ampli tude on y-axis, unless stated. A Gaussian noise with 0.01 variance from the mean of the EEG signal was added to resemble the channel noise while transmitting through free space. The EMSE behavior of various algorithms in PLN removal is shown in Fig. 4. Table II gives the SNRI contrast of various artifact elimination. Table III gives the contrast of all algorithms in terms of EMSE, MSD and CHO. In our experiments we have considered a dataset of five EEG records: Record 1, Record 2, Record 3, Record 4 and Record 5 to ensure the consistency of results. Various ANCs are implemented using LMF, NLMF, BBNLMF, NCLMF, BBNCLMF, NSELMF, BBNSELMF, NSSLMF and BBNSSLMF algorithms. Our simulation model consists of a noise generator, which produces a noise reference signal. This reference signal is a combination of random noise, impulsive noise, PLN, EBA, EMG, RA, CA and EMA artifacts. For all ANCs we give this signal as reference signal. The adaptive algorithm trains the filter coefficients such that the power spectral distribution of the reference matches the noise component present in the contaminated EEG signal. Various experiments were performed to remove several artifacts from the recorded brain signals. These results are shown in Fig. 5 to Fig. 10, presented as follows.
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with PLN of frequency 50Hz and sampled at 160Hz recorded from a male person aged 41 (This signal is considered as Record 5). The reference signal is synthesized signal taken from noise generator. The output of the filter is recovered signal. The EMSE behavior of various ANCs based on sign LMF algorithm are shown in Fig. 4. We have performed this experiment on five EEG records for ten times and averaged. Various performance measures like, SNRI, EMSE, MSD and CHO are tabulated in Tables II and III. Due to space constraint only filtering results of record 5 are shown in this paper, it is shown in Fig. 5. In SNRI measurements it is found that NLMF algorithm gets SNRI of 18.7054dB, BBNLMF gets 17.4961dB, NCLMF gets 17.0053dB, BBNCLMF gets 16.9429dB, NSELMF gets 14.8836dB, BBNSELMF gets 14.6718dB, NSSLMF gets 14.3015dB and BBNSSLMF gets 13.9052dB , where as the conventional LMF algorithm improves to 8.5572dB. B. Adaptive Cancelation of Eye Blink Artifact (EBA) In this experiment the contaminated brain activity signal is applied as primary input to the adaptive filter of Fig. 1, reference signal is taken from our noise generator. This noise generator design synthesizes various artifacts which are some what correlated with the power spectral distribution of noise component present in the input signal. The Simulation results for record 5 are plotted in Fig. 6. From the performance measure tabulated in Tables II and III it is clear that NLMF based noise canceler performs better than other algorithms. However NCLMF based ANC is little inferior to NLMF with zero multiplications. In SNRI measurements it is found that NLMF algorithm gets SNRI of 14.5476dB, BBNLMF gets 13.9453dB, NCLMF gets 13.1549dB, BBNCLMF gets 12.7436dB, NSELMF gets 11.9753dB, BBNSELMF gets 11.5372dB, NSSLMF gets 9.9885dB and BBNSSLMF gets 9.9885dB, where as the conventional LMF algorithm improves to 6.2509dB.
A. Adaptive Cancelation of Power Line Noise (PLN)
C. Adaptive Cancelation of ElectroMioGram (EMG)
This experiment demonstrates Power Line Noise (PLN) cancelation. The input to the filter is EEG signal corrupted
The contaminated brain activity signal is applied as primary input to the adaptive filter of Fig. 1, reference signal
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TABLE II SNRI C ONTRAST OF VARIOUS ANC S FOR EEG S IGNAL E NHANCEMENT (A LL VALUES IN DBS )
TABLE III P ERFORMANCE C ONTRAST OF VARIOUS ANC S FOR EEG S IGNAL E NHANCEMENT
is taken from our noise generator. The Simulation results for record 5 are plotted in Fig. 7. From the performance measure tabulated in Tables II and III it is clear that NLMF based noise canceler performs better than other algorithms. In SNRI measurements it is found that NLMF algorithm gets SNRI of 12.9927dB, BBNLMF gets 12.5429dB, NCLMF gets 11.5303dB, BBNCLMF gets 11.2953dB, NSELMF gets
10.4264dB, BBNSELMF gets 10.0962dB, NSSLMF gets 9.4937dB and BBNSSLMF gets 8.7763dB , where as the conventional LMF algorithm improves to 6.9541dB. D. Adaptive Cancelation of Cardiac Signal Artifact (CSA) During acquisition of EEG signal, cardiac signal contaminates the brain potential. This results in high
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Fig. 6. Typical brain signal enhancement results of EBA Cancelation (a). EEG Signal with EBA, (b). Filtered signal with LMF based ANC, (c). Filtered signal with NLMF based ANC, (d). Filtered signal with BBNLMF based ANC, (e). Filtered signal with NCLMF based ANC, (f). Filtered signal with BBNCLMF based ANC, (g). Filtered signal with NSELMF based ANC, (h). Filtered signal with BBNSELMF based ANC, (i). Filtered signal with NSSLMF based ANC, (j). Filtered signal with BBNSSLMF based ANC.
amplitude cardiac activity in EEG signal. For better analysis of EEG signal we apply EEG with CSA to various ANCs designed based on mean fourth approach. The filtered signals are shown in Fig. 8. Various performance measuring characteristics are tabulated in Table II and Table III. In SNRI measurements it is found that NLMF algorithm gets SNRI of 14.5396dB, BBNLMF gets 14.0529dB, NCLMF gets 13.6384dB, BBNCLMF gets 13.2475dB, NSELMF gets 12.4752dB, BBNSELMF gets 12.1742dB, NSSLMF gets 11.8472dB and BBNSSLMF gets 11.3503dB, where as the conventional LMF algorithm improves to 7.2964dB.
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Fig. 8. Typical brain signal enhancement results of CSA Cancelation (a). EEG Signal with CSA, (b). Filtered signal with LMF based ANC, (c). Filtered signal with NLMF based ANC, (d). Filtered signal with BBNLMF based ANC, (e). Filtered signal with NCLMF based ANC, (f). Filtered signal with BBNCLMF based ANC, (g). Filtered signal with NSELMF based ANC, (h). Filtered signal with BBNSELMF based ANC, (i). Filtered signal with NSSLMF based ANC, (j). Filtered signal with BBNSSLMF based ANC.
E. Adaptive Cancelation of Respiration Artifact (RA) Because of the patients breathing activity the base line of the EEG signal wanders causing some physiological artifact in the EEG signal. In our experiments we performed the cancelation of such artifact from brain activity. The output signals from various ANCs based on mean fourth approach are shown in Fig. 9. Various performance measuring characteristics are tabulated in Table II and Table III. In SNRI measurements it is found that NLMF algorithm gets SNRI of 11.9437dB, BBNLMF gets 11.3752dB, NCLMF gets 10.8403dB, BBNCLMF gets 10.1963dB, NSELMF
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Fig. 9. Typical brain signal enhancement results of RA Cancelation (a). EEG Signal with RA, (b). Filtered signal with LMF based ANC, (c). Filtered signal with NLMF based ANC, (d). Filtered signal with BBNLMF based ANC, (e). Filtered signal with NCLMF based ANC, (f). Filtered signal with BBNCLMF based ANC, (g). Filtered signal with NSELMF based ANC, (h). Filtered signal with BBNSELMF based ANC, (i). Filtered signal with NSSLMF based ANC, (j). Filtered signal with BBNSSLMF based ANC. 10 0 −10 10 0 −10 10 0 −10 10 0 −10 10 0 −10
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Fig. 10. Typical brain signal enhancement results of EMA Cancelation (a). EEG Signal with EMA, (b). Filtered signal with LMF based ANC, (c). Filtered signal with NLMF based ANC, (d). Filtered signal with BBNLMF based ANC, (e). Filtered signal with NCLMF based ANC, (f). Filtered signal with BBNCLMF based ANC, (g). Filtered signal with NSELMF based ANC, (h). Filtered signal with BBNSELMF based ANC, (i). Filtered signal with NSSLMF based ANC, (j). Filtered signal with BBNSSLMF based ANC.
gets 9.3772dB, BBNSELMF gets 8.8475dB, NSSLMF gets 7.4063dB and BBNSSLMF gets 7.0539dB, where as the conventional LMF algorithm improves to 5.3729dB. F. Adaptive Cancelation of Electrode Motion Artifact (EMA) In this experiment the noisy EEG signal is given to ANC structure shown in Fig.1, the reference is taken from noise generator. Noise free brain signals after the elimination of EMA are shown in Fig. 10. Various performance measuring characteristics are tabulated in Table II and Table III. In SNRI measurements it is found that NLMF algorithm gets SNRI of 10.4729dB, BBNLMF gets 10.3520dB,
NCLMF gets 9.2846dB, BBNCLMF gets 9.2541dB, NSELMF gets 8.6207dB, BBNSELMF gets 8.6196dB, NSSLMF gets 8.1059dB and BBNSSLMF gets 8.0093dB, where as the conventional LMF algorithm improves to 6.3957dB. IV. C ONCLUSION In this paper some efficient ANCs for wireless embedded BCI system are proposed. In order to improve the ability of ANCs several variants are adapted in the weight update recursion of filtering section. The proposed ANC structure is a fourteen channel EEG acquisition and signal conditioning unit. To ensure stability, convergence, filtering and less computational complexity we have combined the characteristics like mean fourth error, normalization and signum in a single ANC. Various EEG signals with several artifacts are recorded and tested with proposed ANCs. In all the cases the proposed ANCs outperforms the LMF based ANC. Among the eight proposed noise cancelers NLMF based ANC performs better than others but at the rate of high computational complexity. This complexity is reduced by applying signum in NCLMF based ANC; its performance is nearly equal to NLMF with a great reduction in computational complexity. The filtering results were presented in Fig.5 to Fig. 10. In these figures, sub plots (c) and (d) are more enhanced than other plots. However (e) and (f) are little bit inferior than (c) and (d), these are data clipped versions of NLMF and BBNLMF based ANCs. The remaining plots (g) and (i) are error clipped, data and error clipped. Because of these, the filtering capability slightly degrades than NCLMF, but higher than LMF due to normalization. In our experiments we have used the block size as 5. If the block size increases the filtering speed increases, but residual noise remains in the output signal. From the performance analysis (Tables I, II and III) it is clear that the proposed adaptive algorithms are superior to conventional LMF. Hence these are more suitable for remote health monitoring EEG system. R EFERENCES [1] A. Nijholt and D. Tan, “Brain-computer interfacing for intelligent systems,” IEEE Intell. Syst., vol. 23, no. 3, pp. 72–79, May 2008. [2] B. J. Lance, S. E. Kerick, A. J. Ries, K. S. Oie, and K. McDowell, “Brain computer interface technologies in the coming decades,” Proc. IEEE, vol. 100, no. 13, pp. 1585–1599, May 2012. [3] J. B. F. Van Erp, F. Lotte, and M. Tangermann, “Brain computer interfaces: Beyond medical applications,” Computer, vol. 45, no. 4, pp. 26–34, Apr. 2012. [4] G. Schalk and E. C. Leuthardt, “Brain-computer interfaces using electrocorticographic signals,” IEEE Rev. Biomed. Eng., vol. 4, pp. 140–154, Oct. 2011. [5] K. T. Sweeney, H. Ayaz, T. E. Ward, M. Izzetoglu, S. F. McLoone, and B. Onaral, “A methodology for validating artifact removal techniques for physiological signals,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 5, pp. 918–926, Sep. 2012. [6] N. Mammone, D. Labate, A. Lay Ekuakille, and F. C. Morabito, “Analysis of absence seizure generation using EEG spatial temporal regularity measures,” Int. J. Neural Syst., vol. 22, no. 6, pp. 1250024-1–1250024-17, 2012. [7] F. Morbidi, A. Garulli, D. Prattichizzo, C. Rizzo, and S. Rossi, “Application of Kalman filter to remove TMS-induced artifacts from EEG recordings,” IEEE Trans. Control Syst. Technol., vol. 16, no. 6, pp. 1360–1366, Nov. 2008. [8] N. Mammone, F. Foresta, and F. C. Morabito, “Automatic artifact rejection from multichannel scalp EEG by wavelet ICA,” IEEE Sensors J., vol. 12, no. 3, pp. 533–542, Mar. 2012.
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Gundlapalli Venkata Sai Karthik is with the Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India. His current research interests include biomedical signal processing and design of portable embedded healthcare devices.
Shaik Yasmin Fathima is with the Department of Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, India. Her current research interests include signal processing applications healthcare devices and biotelemetry.
Muhammad Zia Ur Rahman (M’09) received the M.Sc. degree from Nagarjuna University, Guntur, India, and the M.Tech. and Ph.D. degrees from Andhra University, Visakhapatnam, India. Currently, he is a Professor with the Department of Electronics and Communication Engineering, K. L. University, Guntur. His current research interests include adaptive signal processing, biomedical signal processing and array signal processing.
Shaik Rafi Ahamed (M’09) received the B.Tech. and M.Tech. degrees in electronics and communication engineering from Sri Venkateswara University, Tirupati, India, and the Ph.D. degree from the Indian Institute of Technology, Khargpur, India, in 1991, 1993, and 2008, respectively. He is currently an Assistant Professor with the Department of Electronics and Communication Engineering, Indian Institute of Technology Guwahati, Guwahati, India. His current research interests include digital and adaptive signal processing, biomedical signal processing, and VLSI signal processing.
Aimé Lay-Ekuakille is with the University of Salento, Lecce, Italy, in electronic engineering, the M.D. degree in clinical engineering, the Ph.D. degree in electronic engineering, and the post Degree in environmental impact assessment. He is the Director of Instrumentation and Measurement Laboratory, Univerity of Salento. He is a former Technical Manager of different private and public institutions. He is currently a Scientific Advisor of Italian National Committee for IPPC (Integrated Pollution Prevention and Control, and health issues) and a Senior Advisor of the Italian Ministry of Environment. Since January 2010, he has been serves as an untenured Associate Professor at MMC of Kenya, Nairobi, Kenya, in the area of measurements and instrumentation, and a Guest Professor of sensor signal processing with Chemnitz Technical University, Chemnitz, Germany. He is in the boards of different international journals, namely, an Associate Editor of the IEEE S ENSORS J OURNAL. He is the founder and the Editor-in-Chief of the I NTERNATIONAL J OURNAL OF M EASUREMENT T ECHNOLOGIES AND I NSTRUMENTATION E NGINEERING (US IGI Global) in 2010. His current research interests include environmental, industrial and biomedical instrumentation and measurements even using nanotechnology devices, and renewable energy. He has authored or co-authored more than 150 papers on international journals and proceedings. He has co-edited three international books and has authored or co-authored two international books.
