Zero Crossings Research Articles

PS-SiZer map to investigate significant features of body-weight profile changes in HIV infected patients in the IeDEA Collaboration.

We extend the method of Significant Zero Crossings of Derivatives (SiZer) to address within-subject correlations of repeatedly collected longitudinal biomarker data and the computational aspects of the methodology when analyzing massive biomarker databases. SiZer is a powerful visualization tool for exploring structures in curves by mapping areas where the first derivative is increasing, decreasing or does not change (plateau) thus exploring changes and normalization of biomarkers in the presence of therapy. We propose a penalized spline SiZer (PS-SiZer) which can be expressed as a linear mixed model of the longitudinal biomarker process to account for irregularly collected data and within-subject correlations. Through simulations we show how sensitive PS-SiZer is in detecting existing features in longitudinal data versus existing versions of SiZer. In a real-world data analysis PS-SiZer maps are used to map areas where the first derivative of weight change after antiretroviral therapy (ART) start is significantly increasing, decreasing or does not change, thus exploring the durability of weight increase after the start of therapy. We use weight data repeatedly collected from persons living with HIV initiating ART in five regions in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) worldwide collaboration and compare the durability of weight gain between ART regimens containing and not containing the drug stavudine (d4T), which has been associated with shorter durability of weight gain. Through simulations we show that the PS-SiZer is more accurate in detecting relevant features in longitudinal data than existing SiZer variants such as the local linear smoother (LL) SiZer and the SiZer with smoothing splines (SS-SiZer). In the illustration we include data from 185,010 persons living with HIV who started ART with a d4T (53.1%) versus non-d4T (46.9%) containing regimen. The largest difference in durability of weight gain identified by the SiZer maps was observed in Southern Africa where weight gain in patients treated with d4T-containing regimens lasted 59.9 weeks compared to 133.8 weeks for those with non-d4T-containing regimens. In the other regions, persons receiving d4T-containing regimens experienced weight gains lasting 38-62 weeks versus 55-93 weeks in those receiving non-d4T-based regimens. PS-SiZer, a SiZer variant, can handle irregularly collected longitudinal data and within-subject correlations and is sensitive in detecting even subtle features in biomarker curves.

Analysis of a series resonant AC/DC converter with integral cycle mode control for high frequency AC distribution systems

The study of a resonant AC/DC converter, thought for high frequency sinusoidal AC power distribution systems, is performed in this article. The control switch is commuted at the resonant current zero crossings, staying closed (or open) for a certain number of complete cycles. This commutation strategy, known as “integral cycle mode control”, gives soft-switching which improves efficiency, minimizes EMI and increases reliability and converter life span. Also, the input current has a low THD and a good power factor. A suitable scheme to control the converter is proposed. It consists of an internal loop based on sliding mode techniques, for regulating the amplitude of the resonant current, and an external PI loop with an added feed-forward action, that sets the DC output voltage. The main objective is minimizing the disturbing effect that variations in the AC bus voltage and load consumption have over the DC output voltage. For the design of the control laws, a model expressed in terms of rectified and averaged sinusoidal variables is employed. Computer simulations have been performed, considering typical surrounding conditions for this kind of application. The obtained results show that the converter DC output voltage stay always close to its reference value, presenting good rejection against the aforementioned disturbances

A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

Zero crossings are a practical and efficient feature to approximate the frequency of a sampled series of data. Some research describes in different ways how to compute the zero crossings feature starting from its definition, and in some of them, a threshold is included as part of it. This research compiles a comprehensive list of description methods for zero crossings, both with or without threshold. In addition, an improvement of one method is proposed, mainly to save time resources. Moreover, it increases the precision when the objective is to perform some classification. This feature is often used as a vector of a matrix of features in signal classification. To test the different variations of the zero crossings methods, a classification of electromyographic signals was performed using support vector machines. The results obtained by the proposed method threw near to a 40% improvement in the classification compared to those approaches that do not consider a threshold and more than 7% compared to those with a threshold. The processing time of this work is shortened compared to others that also take into account a threshold.

Application of Seismic Attribute and Spectral Decomposition: Example of Fluvial System During Miocene in Field A, Malay Basin

Stratigraphic channels in field A Malay Basin including those with reservoir potential have been defined seismically. There were two exploration wells in field a however, the wells were abandoned due to low structural relief and volume were below economic criteria. This study discusses how seismic attribute and spectral decomposition give geomorphological information in different stratigraphic levels of Miocene age at Top F, H, I, Group I-50 and I-60. Facies analysis, seismic attribute, spectral decomposition and colour blending were implemented to identify hydrocarbon potential in stratigraphic traps and to enhance seismic resolution. Channel anomalies can be identified in seismic reflections characterized by high-amplitude events with chaotic reflections. Numerous sub-parallel reflection with high amplitude is observed in the upper formation. Several seismic attributes were used from the 3D seismic dataset to identify the geomorphologic features, potential reservoir variations, besides distribution and to extract the anomalies. Physical attributes which are related to amplitude, frequency, phase and their derivative were used including variance, instantaneous frequency, sweetness and spectral decomposition. Sweetness is the combination of lateral continuity and reflection strength. High reflection strength indicates maximum amplitude of the largest peak or through of real trace. This is especially, true when an event is a composite reflection. Amplitude attribute provides better spatial definition of events and bright spots could be enhanced. Instantaneous frequency shows composite frequency of the individual reflections contributing to a particular reflector. This composite is a useful correlation tool. Rapid changes in frequency are sometimes indicator of pinchouts such as oil/gas and water contacts. Low frequency anomalies are observed in zones below hydrocarbon reservoirs. Meanwhile, instantaneous phase is useful in stratigraphic analysis of certain sections because the sharp zero crossings that results may emphasize lateral discontinuities, pinchouts and angularities. The combination of attribute analysis and spectral decomposition aids the interpretation and identification of some channel patterns and channel distribution. Two methods of spectral decomposition are used Short Time Fourier Transform (STFT) and S-transform which focus on spatial and temporal range, respectively. Red Green Blue (RGB) colour blending represents different frequency ranges of low, mid and high frequency bands and display as a single image. This method reveals additional information such as depositional elements, anomaly identification, bed thickness, presence of fluids and help to resolve petrophysical heterogeneity. This study reveals that there are strong evidences of hydrocarbon potential in stratigraphic traps for further exploration within the study area. © Medwell Journals, 2019.

Evaluation of time domain features on detection of epileptic seizure from EEG signals

In the study of detection of an epileptic seizure using Electroencephalogram (EEG), pattern recognition has been recognised as a valued tool. In this pattern recognition study, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived directly from filtered EEG data and from discrete wavelet transform (DWT) of filtered EEG data for the detection of an epileptic seizure. Further, the authors attempted to study the performance of other time domain features such as mean absolute value (MAV), standard deviation (SD), average power (AVP) which had been attempted by other researchers. The performance of the TD features is studied using naive Bayes (NB) and support vector machines (SVM) classifiers for University of Bonn database with fourteen different combinations of set E with set A to D and clinically inferred with Christian Medical College, Vellore database. The proposed scheme was also compared with other existing scheme in the literature. The implementation results showed that the proposed scheme could attain the highest accuracy of 100% for normal eyes open and epileptic data set with direct as well as DWT based TD features. For other data sets, the highest accuracy are obtained with DWT based TD features using SVM.

DDJ-Adaptive SAR TDC-Based Timing Recovery for Multilevel Signaling

This paper describes a low-latency and low-power bimodal non-return-to-zero (NRZ) and pulse-amplitude modulation (PAM)-4 timing recovery circuit. This architecture reduces latency and power consumption by eliminating the need for data equalization in the timing recovery path in intersymbol interference (ISI)-limited channels. It directly equalizes data-dependent jitter (DDJ) by adaptively shifting the ISI-affected zero crossings. The implemented prototype in 65-nm CMOS supports both 10-Gb/s NRZ and 20-Gb/s PAM-4 while consuming only 23 mW. The clock and data recovery (CDR) achieves more than 20 MHz of peaking-free tracking bandwidth (BW) and adapts to optimized jitter tolerance for both PAM-4 and NRZ data eyes.

A 5800-$\mu$ m2 Resistor-Based Temperature Sensor With a One-Point Trimmed Inaccuracy of ±1.2 °C ($3\sigma$ ) From −50 °C to 105 °C in 65-nm CMOS

This letter describes a compact resistor-based temperature sensor intended for the thermal monitoring of microprocessors and DRAMs. It consists of an RC poly phase filter (PPF) that is read out by a frequency-locked loop (FLL) based on a dual zero-crossing (ZC) detection scheme. The sensor, fabricated in 65-nm CMOS, occupies 5800 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and achieves moderate accuracy [±1.2 °C (3σ)] over a wide temperature range (-50 °C to 105 °C) after a one-point trim. This is 2x better than the previous compact resistor-based sensors. Operating from 0.85 to 1.3-V supplies, it consumes 32.5-μA and achieves 2.8-mK resolution in a 1-ms conversion time, which corresponds to a resolution FoM of 0.26 pJ·K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Alteration of Particle Drift Resonance Dynamics Near Poloidal Mode Field Line Resonance Structures

AbstractWe examine the effect on drift‐resonant particle dynamics of a strongly peaked internally driven poloidal mode field line resonance (FLR; with specified frequency ω in the Pc5 range and azimuthal mode number m≫1). Using an analytic magneto‐hydrodynamic model in a dipole field to describe the ultra low frequency wave mode, we use the bounce‐averaged formalism of Northrop (1963) to obtain equations of motion for charged particles in the wave frame and find an analytic solution for the case of a temporally constant ultra low frequency wave amplitude profile. Focusing on equatorially mirroring electrons in this study, we demonstrate that, for sufficiently peaked radial profiles, multiple drift resonances appear that are associated with the FLR peak. These are in addition to the well‐known zeroth‐order drift resonance location, occurring when the unperturbed drift speed satisfies the resonance condition ( ). The additional resonances arise because the strongly peaked FLR wave field components provide sufficiently strong perturbations to the azimuthal drift speed to cause multiple zero crossings in the resonance condition. These additional resonances have trapping periods much lower than that of the zeroth‐order resonance and considerably complicate the electron dynamics. Further properties of these resonances and their measurable effect on electron dynamics are discussed. For example, their effect on observations of energetic electron flux on board satellites in the vicinity of an FLR is calculated and shown to significantly distort the typical signature associated with drift resonance (modulations in electron flux at the wave frequency with a 180° phase change across the resonant energy).

Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

Fault diagnosis of rotor asymmetries of induction machines (IMs) using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental’s leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the extended current Park’s vector approach (EPVA). In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.

Output-only modal identification by in-operation modal appropriation for use with enhanced frequency domain decomposition method

Output-only modal identification or operational modal analysis (OMA) has gained increasing popularity in many fields of engineering. In the context of OMA there is no need to measure the input. Modal parameters of dynamic systems are estimated just based on the output responses. One of the most robust and popular frequency domain methods of OMA is enhanced frequency domain decomposition (EFDD) method. EFDD is widely used as an interesting solution for OMA in a large number of researches, projects, and commercial software. We first assessed the EFDD features considering the design parameters selection and then introduced our recently proposed algorithm named in-operation modal appropriation (INOPMA) for use with EFDD method to improve the identification of modal frequencies and damping ratios and overcome some of the existing drawbacks. Modal identification process starts with EFDD and then INOPMA is applied on derived normalized auto-correlation functions (NACF) to estimate modal damping ratios and natural frequencies. Typical EFDD takes advantage of logarithmic decrement (LD) algorithm and zero crossing (ZC) method at this stage. A simulated four-story shear frame has been employed to perform the evaluation of the proposed method on output-only prediction of ideal natural frequencies and modal damping ratios. The outcomes show that the modal parameters are estimated with much less bias error and variance compared to the typical EFDD procedure. The real data of the heritage court tower ambient vibration test is also used to perform sensitivity analysis of EFDD-INOPMA modal parameter estimation in the presence of measurement noise, and favorable results have been obtained.

Teleoperated robotic arm movement using electromyography signal with wearable Myo armband

The primary purpose of this research is to move a 5-DoF Aideepen ROT3U robotic arm in real-time based on the surface Electromyography (sEMG) signal obtained from a wireless Myo gesture armband to distinguish seven hand movements. The pattern recognition system is employed to analyze these gestures and consists of three main parts: segmentation, feature extraction, and classification. Overlap technique is chosen for segmenting portion of the signal. Six-time domain features, namely, Mean Absolute Value (MAV), Waveform Length (WL), Root Mean Square (RMS), Autoregressive Coefficients (AR), Zero Crossings (ZC), Slope Sign Changes (SSC) are extracted from each segment. While the Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (K-NN) classifiers are employed in the classification of the seven hand movements. Moreover, a comparison between their performance is carried out to obtain optimum accuracy. The proposed system is tested on datasets extracted from six healthy subjects and the results showed that the SVM achieved higher system accuracy with 95.26% compared to LDA with an accuracy of 92.58%, and 86.41% accuracy achieved by K-NN.

Multispectral, multiazimuth, and multioffset coherence attribute applications

The coherence attribute computation is typically carried out as a poststack application on 3D prestack migrated seismic data volumes. However, since its inception, interpreters have applied coherence to band-pass-filtered data, azimuthally limited stacks, and offset-limited stacks to enhance discontinuities seen at specific frequencies, azimuths, and offsets. The limitation of this approach is the multiplicity of coherence volumes. Of the various coherence algorithms that have evolved over the past 25 years, the energy ratio coherence computation stands apart from the others, being more sensitive to the seismic waveform changes rather than changes in their amplitude. The energy ratio algorithm is based on the crosscorrelation of five or more adjacent traces to form a symmetric covariance matrix that can then be decomposed into eigenvalues and eigenvectors. The first eigenvector represents a vertically variable, laterally consistent pattern that best represents the data in the analysis window. The first eigenvalue represents the energy of the data represented by this pattern. Coherence is then defined as the ratio of the energy represented by the first eigenvalue to the sum of the energy of the original data. An early generalization of this algorithm was to compute the sum of two covariance matrices, one from the original data and the other from the 90° phase rotated data, thereby eliminating artifacts about low-amplitude zero crossings. More recently, this concept has been further generalized by computing a sum of covariance matrices of traces represented by multiple spectral components, by their azimuthally limited stacks, and by their offset-limited stacks. These more recently developed algorithms capture many of the benefits of discontinuities seen at specific frequencies, azimuths, and offsets, but they present the interpreter with a single volume. We compare the results of multispectral, multiazimuth, and multioffset coherence volumes with the traditional coherence computation, and we find that these newer coherence computation procedures produce superior results.

Robustness of Zero Crossing Estimator

Zero crossing (ZC) statistic is the number of zero crossings observed in a time series. The expected value of the ZC specifies the first‐order autocorrelation of the processes. Hence, we can estimate the autocorrelation by using the ZC estimator. The asymptotic consistency and normality of the ZC estimator for scalar Gaussian processes are already discussed in 1980. In this article, first, we derive the joint asymptotic distribution of the ZC estimator for ellipsoidal processes. Next, we show the variance of the ZC estimator does not attain the Cramer–Rao lower bound (CRLB). However, it is shown that the ZC estimator has robustness when the process is contaminated by an outlier. In contrast with this, we observe that the quasi‐maximum likelihood estimator (QMLE) attains the CRLB. However, we can see that QMLE is sensitive for the outlier.

Feshbach resonances in Na23+K39 mixtures and refined molecular potentials for the NaK molecule

We present a detailed study of interspecies Feshbach resonances of the bosonic $^{23}\mathrm{Na}+$$^{39}\mathrm{K}$ mixture for magnetic fields up to $750 \, \mathrm{G}$ in various collision channels. A total of fourteen Feshbach resonances are reported, as well as four zero crossings of the scattering length and three inelastic two-body loss features. We use the observed magnetic field locations of the resonant features together with the known data on $^{23}\mathrm{Na}+$$^{40}\mathrm{K}$ to refine the singlet and triplet ground state potentials of NaK and achieve a consistent description of Feshbach resonances for both, the Bose-Bose mixture of $^{23}\mathrm{Na}+$$^{39}\mathrm{K}$ as well as the Bose-Fermi mixture of $^{23}\mathrm{Na}+$$^{40}\mathrm{K}$.

A novel feature extraction method for machine learning based on surface electromyography from healthy brain

Feature extraction is one of most important steps in the control of multifunctional prosthesis based on surface electromyography (sEMG) pattern recognition. In this paper, a new sEMG feature extraction method based on muscle active region is proposed. This paper designs an experiment to classify four hand motions using different features. This experiment is used to prove that new features have better classification performance. The experimental results show that the new feature, active muscle regions (AMR), has better classification performance than other traditional features, mean absolute value (MAV), waveform length (WL), zero crossing (ZC) and slope sign changes (SSC). The average classification errors of AMR, MAV, WL, ZC and SSC are 13%, 19%, 26%, 24% and 22%, respectively. The new EMG features are based on the mapping relationship between hand movements and forearm active muscle regions. This mapping relationship has been confirmed in medicine. We obtain the active muscle regions data from the original EMG signal by the new feature extraction algorithm. The results obtained from this algorithm can well represent hand motions. On the other hand, the new feature vector size is much smaller than other features. The new feature can narrow the computational cost. This proves that the AMR can improve sEMG pattern recognition accuracy rate.

The prevalence of nonlinearity and detection of ecological breakpoints across a land use gradient in streams

Human activities can alter aquatic ecosystems through the input of nutrients and carbon, but there is increasing evidence that these pressures induce nonlinear ecological responses. Nonlinear relationships can contain breakpoints where there is an unexpected change in an ecological response to an environmental driver, which may result in ecological regime shifts. We investigated the occurrence of nonlinearity and breakpoints in relationships between total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), and total dissolved carbon (DOC) concentrations and ecological responses in streams with varying land uses. We calculated breakpoints using piecewise regression, two dimensional Kolmogorov-Smirnov (2DKS), and significant zero crossings (SiZer) methods. We found nonlinearity was common, occurring in half of all analyses, with some evidence of multiple breakpoints. Linearity, by contrast, occurred in less than 14% of cases, on average. Breakpoints were related to land use gradients, with 34–43% agricultural cover associated with DOC and TDN breakpoints, and 15% wetland and 9.5% urban land associated with DOC and nutrient breakpoints, respectively. While these breakpoints are likely specific to our study area, our study contributes to the growing literature of the prevalence and location of ecological breakpoints in streams, providing watershed managers potential criteria for catchment land use thresholds.

Recognition of walking environments and gait period by surface electromyography

Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic (EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography (sEMG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human-robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis (VM) and gastrocnemius (GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact (preHC) and 71.7% in pre-toe-off (preTO) for environment classification, and 68.0% for gait period classification, when using only the sEMG sensor. The two effective sEMG feature combinations were “mean absolute value (MAV), zero crossings (ZC)” and “MAV, waveform length (WL)”, and the “MAV, ZC” results were 80.0%, 77.1%, and 75.5%. These results suggest that the sEMG signal can be effectively used to control an exoskeleton robot.

A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

On reconstruction of bandlimited signals from purely timing information

Abstract The well known Logan’s theorem asserts that, under some assumptions, a one octave bandpass signal is recoverable, modulo a multiplicative constant, from its zero crossings only. However, such recovery is numerically problematic and the theorem is not applicable to general bandlimited signals. In this paper, we demonstrate that the additional timing information sufficient for recovery of general band limited signals can be provided in the form of the zero crossings of several of its derivatives and propose novel numerically robust algorithms for such timing extraction and for signal reconstruction, both with high fidelity. We tested the proposed algorithms extensively, with both synthetic and audio signals. In particular, we numerically demonstrate that the timing of the zero crossings of a typical speech signal and of its first two derivatives are not sufficient for a numerically robust recovery of such a signal, but that a robust recovery becomes possible by adding the timing of the zero crossings of the third order derivative.

Analysis and Implementation of Brain Waves Feature Extraction and Classification to Control Robotic Hand

In this paper, (i) time domain, frequency domain and spatial domain feature extraction methods were investigated. (ii) Two dimensionality reduction methods were proposed, implemented and compared. (iii) The method pair (feature extraction + dimensionality reduction) that owns the lowest classification error rate will be used to learn a machine learning algorithm to control robotic hand in offline mode. Two classes EEG dataset of three bipolar channels was used. The extracted feature vectors were fed into Support Vector Machine with Radial Basis Function kernel (SVM-RBF) to train the classifier. The experimented time domain feature extraction methods were: Mean Absolute Value (MAV), integrated Absolute Value (IAV), Zero Crossing (ZC), Root Mean Square (RMS), Waveform Length (WL) and Slope Sign Change (SSC). Frequency domain feature was the Autoregressive Feature (AR). Finally, the spatial domain feature was the Common Spatial Patterns (CSP). Matlab codes for Principal Component Analysis (PCA) and channel selection algorithm were designed and used to reduce the dimensionality of the features vector. Results showed that CSP features got the lowest error rate for both dimensionality reduction technique with 2.14%. Results recommends to use channel selection algorithm over PCA since it owns the lowest processing time of 8.2s over 8.5s for PCA.