ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 08, Issue 01, January 2023 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 136 EFFICIENT DIGITAL FILTER FOR PREPROCESSING OF EEG SIGNALS
Mohammad Firoj Khan M.Tech- VLSI Design
Guide Name- Mrs. Ruhee Matolya
Infinity management & Engineering College, Sagar
Abstract - Designing energy-efficient filters for on-chip signal and data processing devices is crucial for reducing power consumption in these systems. A number of strategies can be employed to achieve this goal, including the use of low-power filter architectures, reducing the sampling rate and bit-width of signals, using approximation techniques, implementing power gating, and designing low-power specialized circuits. By employing these strategies, it is possible to design energy-efficient filters that can significantly reduce the power consumption of on-chip signal and data processing devices. The EEG signals acquired from human scalp are contaminated with the distinct set of artifacts, most of the time. These artifacts affect the uniqueness of the signal due to which medical psychoanalysis and information retrieval may be difficult. Therefore, EEGs are first preprocessed to eradicate the present artifacts to make these signals ready for further signal processing. In this paper, digital infinite impulse response and finite impulse response filter are implemented to identify an efficient filter structure for preprocessing of acquired EEG signals. The performance analysis has been done by calculating the signal to noise ratio and cross- correlation.
Keywords: Cognitive workload; Electroencephalography; FIR Filter, IIR Filter, Preprocessing, noise.
1 INTRODUCTION
Designing energy-efficient filters for on- chip signal and data processing devices is an important task in the field of VLSI (Very Large Scale Integration) design. This is because filters consume a significant amount of power in these systems. The following are some strategies for designing energy-efficient filters:
1. Low-power architectures: The design of the filter should be optimized for low power consumption by using architectures such as feed forward, ladder, or switched-capacitor filters.
2. Sampling rate reduction: Lowering the sampling rate of the signal can reduce the power consumption of the filter.
3. Bit-width reduction: Reducing the bit-width of the signals and coefficients can reduce the power consumption of the filter.
4. Use of approximation techniques:
Approximation techniques such as Chebyshev, Butterworth, or Cauer can be used to reduce the complexity of the filter and its power consumption.
5. Power gating: Power gating, also known as dynamic power management, can be used to reduce the power consumption of the filter
by turning off the power when it is not needed.
6. Use of specialized circuits:
Specialized circuits such as adders, multipliers, and integrators can be designed to be low power.
By implementing these strategies, energy- efficient filters can be designed for on- chip signal and data processing devices.
2 REGENERATE RESPONSE
Now a days, research on automated analysis of the EEG to assess mental states of human-like the cognitive workload is progressively emerging day by day[1], [2]. Cognitive workload indicates the capacity of mental demand carried out through some task [3], [4]. EEG signals are classified on the basis of frequency:
alpha waves (frequency range 8 to 13 Hz), beta waves (frequency greater than 13 Hz), theta waves (frequency range 3.5 to 7.5 Hz), delta waves ( frequency 3 Hz or less) [5], [6]. While acquiring EEG signals, artifacts are also present. The unwanted noise generated during the recording of the brain signals is known as artifacts.
[7], [8]. In the encephalogram signal, the removals of artifacts are critical. Artifacts are of two kinds: Physiological and NonPhysiological. The physiological artifacts occur due to the movement of
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
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Vol. 08, Issue 01, January 2023 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 137 the head, blinking of an eye, rotation of
eyeball, sweating, heartbeat and contraction of muscle and nonphysiological artifacts occur due to the external faults like a failure of the electrode, power supply and ventilation [9], [10] Many techniques are applied by researchers for artifact removal in EEG signal like independent component analysis, Recursive Least Squares adaptive filter, Spatially Constrained Independent Component Analysis(SCICA), average artifact subtraction(AAS), Blind Source Separation(BSS), and Wavelet Denoising [11], [12], [13], [14]. An Independent Component Analysis (ICA) and Recursive Least Squares (RLS) are employed to get rid of eye movement artifacts. A separate electrode is situated near the eyes for the excerpt of a reference signal. This electrode records the horizontal and vertical movements of eyes. Every reference input is envisaged into the ICA field and after that the interference is assessed by the RLS algorithm and then the assessed interference is deducted from the EEG signal in the ICA domain [12]. The artifacts are affected by the characterization of spatiotemporal- frequency. An improved spatiotemporal frequency performance was proposed which provide a superior signal-to-noise ratio and clutter-to-signal ratio score. An approach of template matching along with manifold tools of signal-processing was assessed and verified on real EEG signals.
It has been observed that this method has enhanced competency in EEG studies and analysis [15]. The consequences of all types of artifact removal are combined and their overall performances were 88.09% specificity and 89.438%
sensitivity. But the deficiency of this research is that the recital of the algorithm was assessed on limited data sets only. An amalgam of independent component analysis and regression with high-order data was used for elimination of artifacts from EEG record [16]. The obtained results are compared with another accessible method of ICA, wavelet ICA, regression analysis and regression ICA and the analysis expose that mixing of ICA with regression conserves the neuronal linked activity with EEG signals and eliminate ocular artifacts. Other method does not dependent on a number
of EEG channels used is carried out for each EEG channel locations. This method is universal as it does not depend on a number of EEG channels used and this technique is not restricted to the artifact type which makes it competent to apply online on diverse experiments and subjects [17].
A. Loading of Data
As literature findings indicate the elimination of artifact in encephalogram is crucial and various techniques are used for eliminating artifacts. In the proposed work, different filter structures are designed in MATLAB (R2016a, 64bit) to identify the efficient filter structure for the elimination of artifacts. The EEG signals can be load from online database Physiobank ATM to Matlab workspace using „load‟ command for further processing. EEG signal dataset in the desired format is introduced from the Neuroelectric and Myoelectric database of Physiobank ATM. In this database EEG signals from 11 fit participators at frequency of 5, 6, and 10 Hz are acquired.
These EEG signals were acquired during a rapid serial visual presentation (RSVP) task using eight different channels of 10- 20 EEG signal acquisition system (PO8, PO7, PO3, PO4, P7, P8, O1, and O2).
Here, „P‟ represents parietal region and
„O‟ represents occipital region. Four different EEG signals of frequency 10Hz from the PO7 channel is considered in the proposed work. The loaded EEG signals are continuous signal and amplitudes are
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
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Vol. 08, Issue 01, January 2023 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 138 of the order of microvolt with the
sampling rate of 2048 Hz [31], [32].
B. Preprocessing
The preprocessing step involves the addition of random noise to the loaded EEG signals and implementation of digital filter structures (IIR and FIR) to identify the best filter structure. Noise is defined as a random signal that occurs naturally.
There are numerous kinds of random noise like pink noise, square root noise, white noise, blue noise, proportional noise and thermal noise. Random noise occurs due to the random motion of electrons in any conducting medium. Whenever signals of random noise are combined with electronic circuits, the ensuing noise is equivalent to the collective power of individual signals. In the proposed work white noise is consisting of normally distributed random numbers is used. The MATLAB function “randn” is used to create a 1-by-N vector of random numbers with standard deviation of 0.1.
The standard deviation is a way to calculate how extreme the signal oscillates from the mean. There is no precise peak to peak value in the random noise. The peak to peak value in the random noise is around 6 to 8 times the standard deviation so random noise of the peak to peak 0.6 to 0.8 is generated in present research. A random noise generated by randn function generates a sequence of numbers which have not generated any definite pattern. This implies that the same sequence of numbers cannot be reproduced [33].
C. Identification of Efficient Filter Structure
This section proposed the parameters used to evaluate the performance of implemented filter structures to reject artifacts from EEG signals. The parameters selected are signal to noise ratio (SNR) and cross correlation (CC) to evaluate the performance of the proposed filters in this research. The filter which provides the higher signal to noise ratio and higher cross-correlation is considered as an appropriate filter for artifact removal from EEG signals. The signal to noise ratio is defined as the ratio of signal power to the noise power and is usually expressed in decibels. SNR is calculated in Matlab by computing the ratio of the
signal summed squared magnitude to that of the noise. The other parameter cross correlation determines the correlation between the original and reconstructed (filtered) EEG signal. It is calculated by finding the coefficient of the correlation between the original and reconstructed signal. The loading of EEG signals to MATLAB workspace, preprocessing of EEG signals and identification of more efficient filter structure can be summed up in the form of an efficient MATLAB algorithm.
3 CONCLUSION
In this paper, an accurate and efficient digital filter implementation algorithm to eliminate noise/artifacts from EEG signals has been presented. The performance parameters (signal to noise ratio and cross correlation) have been calculated to assess the recital of designed filter structures. The results show that a FIR filter with order 4 to 5 possesses better filtering capacity with higher SNR and CC than the IIR filter in denoising different EEG signals loaded from the Physiobank ATM database. A more efficient filter structure is identified to remove noise from input noisy EEG signals. The future scope of this research is to apply designed filter structures on realtime EEG signals. It shall be tested while adding different types of noises to real time EEG signals during the preprocessing phase. These preprocessed EEG signals will further be used for the identification of the associated cognitive workload by developing efficient signal processing algorithms.
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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 08, Issue 01, January 2023 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 139 IEEE Transactions on Cognitive and
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