Individual Frequency Bands Research Articles

Information redundancy across spectral regions for sentence recognition in noise

Two approaches are examined to quantify the synergetic and redundant interactions across spectral regions for sentence recognition in noise. In one approach, the Speech Intelligibility Index is extended such that speech intelligibility is not only influenced by the speech audibility metrics in individual frequency bands but also their pairwise products to capture the synergetic and redundant interactions between pairs of bands. In a second approach, it is assumed that successful keyword recognition is determined by how many independent channels of information is received by the listener. Each channel of information may be uniquely carried by a frequency band, representing the independent contribution of that band, or shared among multiple bands, representing information redundancy. A previously collected dataset was re-analyzed using the two quantification approaches. For each of the 30 listeners in the dataset, correctness in keyword recognition in the IEEE sentences was available for 600 trials. From trial to trial, the sentence-in-noise stimuli varied in terms of signal-to-noise ratio and which frequency bands were removed from the spectrum via filtering. Both approaches provided consistent depictions of how information is shared across spectral regions for the IEEE sentences. Moreover, the information redundancies across frequency regions did not equally impact all listeners.

A Biologically Inspired Attention Network for EEG-Based Auditory Attention Detection

Decoding auditory attention in a cocktail party from neural activities is crucial in the brain-computer interfaces (BCIs). Given that the speech-electroencephalography (EEG) relationships are informative about attentional focus, we propose a novel framework called the biologically inspired attention network (BIAnet) to capture the interactions between EEG and speech. With the neural attention mechanism, the BIAnet can model how each EEG frequency band is related to the subband envelopes of speech by dynamically assigning weights to individual frequency bands at run-time. Results show that the proposed BIAnet outperforms state-of-the-art AAD methods on two publicly available datasets. We also analyze how the BIAnet works and the frequency-specific interactions between EEG and speech signals through data visualization. Overall, the proposed BIAnet provides an accurate, low-latency, and interpretable AAD approach, which has the potential to be extended to general problems in BCIs.

The solid state VCD of a novel N-acylhydrazone trifluoroacetate

A novel N-acylhydrazone with pharmaceutical importance was subject of structural and IR/VCD investigations in the solid state. In the crystal structure, dimers of anion-cation pairs are stabilized by H-bonding and ionic interactions. Some less common interaction types, like C=N···C-NH3+ (σ-hole) interactions, hydrazone–aromatic interactions and dispersive contacts of the CF3 groups are also present in the crystal. Satisfactory reproduction of the solid state IR and VCD spectra required that quantum-chemical calculations be done on a tetramer (four cation–anion pairs) cut out from the crystal structure, exhibiting key intermolecular interactions. Ten DFT functionals were assessed as to the agreement between the calculated and experimental spectra. Various approaches to scaling of the calculated frequencies were applied. The best results were yielded with individual (optimized) frequency scaling factors (FSFs) and band half-widths at half maximum-(HWHM) for four separate spectral subregions. The best matching between the experimental and theoretical spectra (according to SimIR, SimVCD and SimVDF indices) was found for the B3PW91 functional, however, a few other functionals follow closely in the ranking. Based on the quantum chemical calculations, spectral assignments have been made.

Perspective of Direct Search for Dark Components in the Universe with Multi-Wavelengths Stimulated Resonant Photon-Photon Colliders

We explore a possibility to detect dark components in the Universe via stimulated photon–photon collisions by focusing two-frequency coherent electromagnetic fields in a vacuum. Those fields are assumed to be pulsed reaching Fourier transform limits in near-infrared, THz, and GHz frequency bands, respectively. The numbers of signal photons as a result of exchange of a pseudoscalar-type pseudo Nambu–Goldstone boson have been evaluated in the individual frequency bands. Within presently available beam intensities, we found that the QCD axion scenarios are thoroughly testable in the mass range 10−6–100 eV based on the common method. Furthermore, we show a possibility to reach the weak coupling domain even beyond the gravitationally weak coupling strength if pulse compression in the GHz band is realized in the near future development.

Relationship between resting state alpha electroencephalographic rhythms and aging in cognitively unimpaired seniors and patients with mild cognitive impairment due to Alzheimer’s disease and amnestic mild cognitive impairment

AbstractBackgroundThe rsEEG alpha rhythms reflect cortical neural synchronization mechanisms underpinning the inhibition of sensory, cognitive, and motor areas in parietal, temporal, and occipital cortex during a condition of low vigilance. Here we tested the hypothesis that age may diversely affect rsEEG alpha (8‐12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with Alzheimer’s disease and mild cognitive impairment (ADMCI).MethodClinical and rsEEG datasets in 63 ADMCI and 60 Nold individuals ‐ matched as demography, education, and gender ‐ were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands as well as fixed beta (14‐30 Hz) and gamma (30‐40 Hz) bands. Each group was stratified into three subgroups based on age ranges (i.e., tertiles).ResultAs compared to the younger Nold subgroups, the older one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions (Figure 1 top). On the contrary, in relation to the younger ADMCI subgroups, the older one displayed lesser reduction in those rhythms (Figure 1 bottom). Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions as revealed by neuroimaging, and clinical and neuropsychological scores.ConclusionThe present results suggest that age may represent a deranging factor for dominant rsEEG alpha rhythms in Nold seniors, while rsEEG alpha rhythms in ADMCI patients may be more affected by the disease variants related to earlier vs. later onset of the AD.

Resting‐state alpha electroencephalographic rhythms are differently related to gender in cognitively unimpaired seniors and in patients with amnestic mild cognitive impairment due to Alzheimer’s disease

AbstractBackgroundHere we tested the hypothesis that gender may diversely affect resting state eyes‐closed electroencephalographic (rsEEG) alpha (8‐12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with Alzheimer’s disease and mild cognitive impairment (ADMCI).MethodClinical and rsEEG datasets in 69 ADMCI and 57 Nold individuals ‐ matched as demography, education, and gender ‐ were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands as well as fixed beta (14‐30 Hz) and gamma (30‐40 Hz) bands. Each group was stratified into two subgroups based on gender (i.e., female and male).ResultAs compared to the Nold female subgroup, the male one showed greater reductions in the rsEEG alpha rhythms topographically widespread. In relation to the ADMCI female subgroup, the male one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions. Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions as revealed by neuroimaging, and clinical and neuropsychological scores.ConclusionThe present results suggest that gender may represent a deranging factor for dominant rsEEG alpha rhythms topographically widespread in Nold seniors and topographically posterior in ADMCI patients.

Resting State Alpha Electroencephalographic Rhythms Are Affected by Sex in Cognitively Unimpaired Seniors and Patients with Alzheimer's Disease and Amnesic Mild Cognitive Impairment: A Retrospective and Exploratory Study.

In the present retrospective and exploratory study, we tested the hypothesis that sex may affect cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms recorded in normal elderly (Nold) seniors and patients with Alzheimer's disease and mild cognitive impairment (ADMCI). Datasets in 69 ADMCI and 57 Nold individuals were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands and fixed beta (14-30 Hz) and gamma (30-40 Hz) bands. Each group was stratified into matched females and males. The sex factor affected the magnitude of rsEEG source activities in the Nold seniors. Compared with the males, the females were characterized by greater alpha source activities in all cortical regions. Similarly, the parietal, temporal, and occipital alpha source activities were greater in the ADMCI-females than the males. Notably, the present sex effects did not depend on core genetic (APOE4), neuropathological (Aβ42/phospho-tau ratio in the cerebrospinal fluid), structural neurodegenerative and cerebrovascular (MRI) variables characterizing sporadic AD-related processes in ADMCI seniors. These results suggest the sex factor may significantly affect neurophysiological brain neural oscillatory synchronization mechanisms underpinning the generation of dominant rsEEG alpha rhythms to regulate cortical arousal during quiet vigilance.

Generative synthesis of logos across DCT domain

Generative learning in pixel domain has achieved great success in exploiting their correlations in processing images towards desired objectives, yet learning in frequency domain could provide added benefits in exploiting pixel correlations without worrying about their spatial locations and increasing their modeling costs. In this paper, we analyze the spectral bias from a frequency perspective to overcome such limitations and hence propose a dynamic self-adaptive optimization on GAN-based generative learning, leading to a dynamic and generative logo synthesis in DCT domain. To achieve exploitation of all the pixel correlations inside the whole image regardless of their spatial locations, we introduce an approximated DCT transformation and decompose both the input images and the generated images into relatively independent DCT frequency bands. As a result, a new channel of DCT domain generative learning can be established to support the existing pixel domain learning towards improved logo synthesis. Since learning across different frequency band constantly varies, we further propose a dynamic optimization scheme to maximize the effectiveness of contributions from each individual DCT frequency band. Extensive experiments are carried out and the results in comparison with the existing state of the arts illustrate that our proposed achieves significant superiority in terms of both synthesized logo quality, integrity and variety.

Resting State Alpha Electroencephalographic Rhythms Are Differently Related to Aging in Cognitively Unimpaired Seniors and Patients with Alzheimer's Disease and Amnesic Mild Cognitive Impairment.

In relaxed adults, staying in quiet wakefulness at eyes closed is related to the so-called resting state electroencephalographic (rsEEG) rhythms, showing the highest amplitude in posterior areas at alpha frequencies (8-13 Hz). Here we tested the hypothesis that age may affect rsEEG alpha (8-12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with mild cognitive impairment due to Alzheimer's disease (ADMCI). Clinical and rsEEG datasets in 63 ADMCI and 60 Nold individuals (matched for demography, education, and gender) were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands, as well as fixed beta (14-30 Hz) and gamma (30-40 Hz) bands. Each group was stratified into three subgroups based on age ranges (i.e., tertiles). As compared to the younger Nold subgroups, the older one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions. On the contrary, in relation to the younger ADMCI subgroups, the older one displayed a lesser reduction in those rhythms. Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions revealed by neuroimaging, and clinical and neuropsychological scores. The present results suggest that age may represent a deranging factor for dominant rsEEG alpha rhythms in Nold seniors, while rsEEG alpha rhythms in ADMCI patients may be more affected by the disease variants related to earlier versus later onset of the AD.

Automatic classification and reduction of wind noise in spectral data.

Outdoor acoustic data often include non-acoustic pressures caused by atmospheric turbulence, particularly below a few hundred Hz in frequency, even when using microphone windscreens. This paper describes a method for automatic wind-noise classification and reduction in spectral data without requiring measured wind speeds. The method finds individual frequency bands matching the characteristic decreasing spectral slope of wind noise. Uncontaminated data from several short-timescale spectra can be used to obtain a decontaminated long-timescale spectrum. This method is validated with field-test data and can be applied to large datasets to efficiently find and reduce the negative impact of wind noise contamination.

Time-Varying Spectral Kurtosis: Generalization of Spectral Kurtosis for Local Damage Detection in Rotating Machines under Time-Varying Operating Conditions.

Vibration-based local damage detection in rotating machines (i.e., rolling element bearings) is typically a problem of detecting low-energy cyclic impulsive modulations in the measured signal. This can be challenging as both the amplitude of a single damage-related impulse and the distance between impulses might be changing in time. From the signal processing point of view, this means time varying regarding the the signal-to-noise ratio, location of information in the frequency domain, and loss of periodicity (this remains cyclic but not periodic). One of the many attempted approaches to this problem is filtration using custom filters derived in a data-driven fashion. One of the methods to obtain such filters is a selector approach, where the value of a certain statistic is calculated for individual frequency bands of a signal that results in the magnitude response of a filter. In this approach, each chosen statistic will yield different results, and the obtained filter will be focused on different frequency bands focusing on different behaviors. One of the most popular and powerful selectors is spectral kurtosis as popularized by Antoni, which uses kurtosis as an operational statistic. Unfortunately, after closer inspection, it is easy to notice that, although selectors can significantly enhance the signal, they accumulate a great deal of noise and other background content of signals, which occupies the space (or rather time) in between the impulses. Another disadvantage is that such filters are time-invariant, because, in the principle of their construction, they are not adaptive, and even slight changes in the signal yield suboptimal results either by missing relevant data when the conditions in the signal change (i.e., informative impulses widen in bandwidth), or by accumulating unnecessary noise when the relevant information is not present (in between impulses or in frequency bands that impulses no longer occupy). To address this issue, I propose generalization of the selector approach using the example of spectral kurtosis. This assumes creating a time-varying selector that can be seen as a spatial filter in the time-frequency domain. The time-varying SK (TVSK) is estimated for segments of the signal, and, instead of a vector of SK-based filter coefficients, one obtains a TVSK-based matrix of coefficients that takes into account the time-varying properties of the signal. The obtained structure is then binarized and used as a filter. The presented method is tested using a simulated signal as well as two real-life signals measured on heavy-duty bearings in two different types of machine.

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

Objective. The objective of the study is to identify phase coupling patterns that are shared across subjects via a machine learning approach that utilises source space magnetoencephalography (MEG) phase coupling data from a working memory (WM) task. Indeed, phase coupling of neural oscillations is putatively a key factor for communication between distant brain areas and is therefore crucial in performing cognitive tasks, including WM. Previous studies investigating phase coupling during cognitive tasks have often focused on a few a priori selected brain areas or a specific frequency band, and the need for data-driven approaches has been recognised. Machine learning techniques have emerged as valuable tools for the analysis of neuroimaging data since they catch fine-grained differences in the multivariate signal distribution. Here, we expect that these techniques applied to MEG phase couplings can reveal WM-related processes that are shared across individuals. Approach. We analysed WM data collected as part of the Human Connectome Project. The MEG data were collected while subjects (n = 83) performed N-back WM tasks in two different conditions, namely 2-back (WM condition) and 0-back (control condition). We estimated phase coupling patterns (multivariate phase slope index) for both conditions and for theta, alpha, beta, and gamma bands. The obtained phase coupling data were then used to train a linear support vector machine in order to classify which task condition the subject was performing with an across-subject cross-validation approach. The classification was performed separately based on the data from individual frequency bands and with all bands combined (multiband). Finally, we evaluated the relative importance of the different features (phase couplings) for classification by the means of feature selection probability. Main results. The WM condition and control condition were successfully classified based on the phase coupling patterns in the theta (62% accuracy) and alpha bands (60% accuracy) separately. Importantly, the multiband classification showed that phase coupling patterns not only in the theta and alpha but also in the gamma bands are related to WM processing, as testified by improvement in classification performance (71%). Significance. Our study successfully decoded WM tasks using MEG source space functional connectivity. Our approach, combining across-subject classification and a multidimensional metric recently developed by our group, is able to detect patterns of connectivity that are shared across individuals. In other words, the results are generalisable to new individuals and allow meaningful interpretation of task-relevant phase coupling patterns.

Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information.

Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli. We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction. We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP. The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.

The just noticeable difference of early decay time (EDT)

The purpose of this study was to determine the just noticeable difference (JND) of early decay time (EDT) for broadband conditions and individual frequency bands. JNDs of room acoustic parameters are important for assessing if changes in geometry or materials in a room will have a discernible impact in the listener’s perception. The EDT JND has been reported to be 5% in the ISO 3382-1:2009 standard, which is the same value usually quoted for the JND of classical reverberation time (T30). No formal studies have been undertaken so far to validate if this JND value is appropriate for EDT, or to determine its dependency on frequency. Measured spatial room impulse responses (SRIRs) from three concert halls were used as base cases, which were then modified with a computer algorithm to attain specified values of EDT. All stimuli were reproduced in an anechoic chamber with an array of 30 loudspeakers using third-order ambisonics. The method of constant stimuli, in which a psychometric function is fitted to the measured data, was used to determine the JND. Thirty subjects with musical backgrounds participated in the study. The obtained broadband JND was 26%, while individual frequency band JNDs were higher.

Perception of atomic speech

Natural speech signals are continuous and have great redundancy in time, which is learned by human beings and guarantees speech comprehension even in challenging circumstances. This work develops a sound synthesis method to study some time-related characteristics of the human auditory system. The temporal information within the individual frequency band of a speech is down-sampled using Gaussian-shaped pulses and then recombined into a new sound which may have some remaining intelligibility or feasible phonetic features. The “atomic” sound is coined to the sound with an extremely spectral-temporally sparse spectrogram generated using the method. A battery of speech perception tests was administered in normal-hearing listeners. Results show that (1) atomic sounds from clear speech can be understood as speech, although the listeners often reported a feeling of water sound textures; (2) the temporal and spectral resolution could be traded off in the atomic speech comprehension; and (3) only one maximum envelope value preserved among a 32-channel filter-bank at a rate of 400 Hz was surprisingly adequate for speech understanding, which indicates that the brain can organize the only spectral peak within each short duration no longer than 2.5-ms into sentence understanding without either explicit or implicit encoding of the first three formants.

Perceptual Weighting of Binaural Lateralization Cues across Frequency Bands

The auditory system uses interaural time and level differences (ITD and ILD) as cues to localize and lateralize sounds. The availability of ITDs and ILDs in the auditory system is limited by neural phase-locking and by the head size, respectively. Although the frequency-specific limitations are well known, the relative contribution of ITDs and ILDs in individual frequency bands in broadband stimuli is unknown. To determine these relative contributions, or spectral weights, listeners were asked to lateralize stimuli consisting of eleven simultaneously presented 1-ERB-wide noise bands centered between 442 and 5544 Hz and separated by 1-ERB-wide gaps. Either ITDs or ILDs were varied independently across each noise band, while fixing the other interaural disparity to either 0 dB or 0 μs. The weights were obtained using a multiple linear regression analysis. In a second experiment, the effect of auditory enhancement on the spectral weights was investigated. The enhancement of single noise bands was realized by presenting ten of the noise bands as preceding and following sounds (pre- and post-cursors, respectively). Listeners were asked to lateralize the stimuli as in the first experiment. Results show that in the absence of pre- and post-cursors, only the lowest or highest frequency band received highest weight for ITD and ILD, respectively. Auditory enhancement led to significantly enhanced weights given to the band without the pre- and post-cursor. The weight enhancement could only be observed at low frequencies, when determined with ITD cues and for low and high frequencies for ILDs. Hence, the auditory system seems to be able to change the spectral weighting of binaural information depending on the information content.

A concurrent dual-band radar sensor for vital sign tracking and short-range positioning

Abstract This paper presents a concurrent dual-band radar system for noncontact tracking of vital signs (e.g., respiration and heartbeat), and indoor short-range localization. The proposed sensor, which has been achieved with our own customized concurrent dual-band subsystems, operates at 1.67 and 2.06 GHz synchronously. Based on the Doppler principle, tiny vital signs are obtained by analysis of spectrum of the signals received at each individual frequency band. Moreover, the location of a target is estimated based on the phase difference between these two closely spaced frequencies. The azimuth information is obtained by beam scanning. Combining the results of range and azimuth information allows the radar system to plot two-dimensional maps. As a result, the proposed radar is capable of monitoring human’s life activities and tracking the location of individuals continuously. System-level experiments were carried out to reveal the versatile capability of the life activity monitoring system.

On the time-course of functional connectivity: theory of a dynamic progression of concussion effects.

The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.

Design and exploration of functioning of a D-Z shaped SNG multiband metamaterial for L-, S-, and X-bands applications

A design and performance analysis of a new triple-band metamaterial (MTM) is imparted in this article. The proposed MTM is able to produce three operating frequency bands and can be applicable for L-, S-, and X-bands applications. The L-band and S-band frequency responses are caused by electric resonance, and the X-band is generated by magnetic resonance. The triple-band response is produced by combining two open D-shaped loops coupled with a Z-shaped metal strip. To conclude the electromagnetic behaviour of the proposed MTM, the Nicolson Rose Weir method is utilized, and effective parameter extraction from reflection and transmission coefficient is done. Then the parametric analysis is done by changing the arm length of the loop, varying the capacitive gaps of the structure to analyse the performance of the proposed structure. Moreover, the equivalent circuit analysis of three individual frequency bands is performed for a clear understanding of the resonance phenomena of the proposed MTM. Besides, the analysis of 1×2, 2×1, 2×2, and 4×4 array structures of the unit cells is done to validate the activities of the MTM because array structure is often needed to improve the performance of an antenna. All the structures reveal their triple-band operation and promising for L-, S-, and X-bands applications. The proposed MTM unit cell exhibits a larger effective medium ratio (17.7) for the lower frequency band. So, the projected compact MTM is a promising solution for different microwave applications.