Welch Power Research Articles

IDENTIFICATION OF SCHIZOPHRENIA USING ALPHA POWER AND THETA PEAK FREQUENCY DURING COGNITIVE ACTIVITY

Schizophrenia (SCH) is a psychiatric disorder that causes mental illness and is very difficult to discriminate from the normal subject. As the current diagnostic procedure DSM-V is qualitative, accurate clinical detection is essential. Cognitive dysfunction is widespread in [Formula: see text]CH and it relates to brain activity. The aim of this research is to analyze EEG as a screening tool to identify SCH as it records brain activity. To stimulate cognitive activity, two different types of visual oddball paradigms are designed and are named P1 and P2. Although many researchers have analyzed the EEG of SCH, it remains unclear which recording condition is suitable. Therefore, EEG is recorded from 52 SCH subjects and 29 normal subjects during conventional recording conditions, namely rest, HV, PHV, Photic and cognitive activities (P1 and P2). The Welch power spectrum method is used to find the absolute power of the four EEG bands called [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text]. In addition to these, each band’s peak power [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] and peak frequencies [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] are also calculated from the power spectrum of the EEG signal. Out of these 12 features, [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] are considered as the most significant features, because they are significant with [Formula: see text] < 0.001 in all recording conditions. The SVM classifier successfully predicts SCH with a maximum sensitivity of 98% and accuracy of 97% using [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text]. Therefore, it can be concluded that [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] can be used as features to use EEG as a screening tool for the identification of schizophrenia.

An Adaptive Chirp Mode Decomposition-Based Method for Modal Identification of Time-Varying Structures

Modal parameters are inherent characteristics of civil structures. Due to the effect of environmental factors and ambient loads, the physical and modal characteristics of a structure tend to change over time. Therefore, the effective identification of time-varying modal parameters has become an essential topic. In this study, an instantaneous modal identification method based on an adaptive chirp mode decomposition (ACMD) technique was proposed. The ACMD technique is highly adaptable and can accurately estimate the instantaneous frequencies of a structure. However, it is important to highlight that an initial frequency value must be selected beforehand in ACMD. If the initial frequency is set incorrectly, the resulting instantaneous frequencies may lack accuracy. To address the aforementioned problem, the Welch power spectrum was initially developed to extract a high-resolution time–frequency distribution from the measured signals. Subsequently, the time–frequency ridge was identified based on the maximum energy position in the time–frequency distribution plot, with the frequencies associated with the time–frequency ridge serving as the initial frequencies. Based on the initial frequencies, the measured signals with multiple degrees of freedom could be decomposed into individual time-varying components with a single degree of freedom. Following that, the instantaneous frequencies of each time-varying component could be calculated directly. Subsequently, a sliding window principal component analysis (PCA) method was introduced to derive instantaneous mode shapes. Finally, vibration data collected under various operational scenarios were used to validate the proposed method. The results demonstrated the effective identification of time-varying modal parameters in real-world civil structures, without missing modes.

Random vibration-based progressive fatigue damage monitoring in thermoplastic coupons: a preliminary investigation

In recent years there has been an increasing trend towards the utilization of composites, particularly thermoplastics, in various components of aerostructures. The prelusion of such materials has underscored the significance of investigating their fatigue behavior and developing reliable methods for detecting fatigue damage. In this context, vibration-based techniques hold significant potential as they leverage the inherent excitation provided by in-flight noise and turbulence. This study aims at assessing the progressively accumulated fatigue damage in thermoplastic coupons via random vibration signals while accounting for operational and inter-structural uncertainties. The experimental process consists of preliminary tension and fatigue tests, interrupted fatigue tests, C-Scan inspection tests, and non-contact random vibration tests. Consecutive fatigue states are obtained by performing fatigue tests at intervals of 10 000 cycles for a population of 7 coupons. At each interruption, ultrasonic C-Scan and vibration inspection tests are performed, allowing for the visualization of fatigue damage and random vibration signal analysis. Welch Power Spectral Density estimates are employed and are shown to have good potential for distinguishing among different fatigue states despite the inevitable population and experimental uncertainty. Furthermore, fatigue damage is found to progress symmetrically and laterally along the free edges of the test coupons, which is explained by the free edge effect.

A novel machine learning algorithm for finger movement classification from surface electromyogram signals using welch power estimation

Electromyogram (EMG) signal monitoring is an effective method for controlling the movements of a prosthetic limb. The classification of the EMG pattern of various finger motions in upper-arm amputees has drawn much attention in recent years to develop algorithms that provide adequate accuracy. However, due to the complexity of EMG data, movement detection is a challenging task. Therefore, an effective model is needed that can accurately process, analyze, and classify various hand and finger movements. This paper proposes a novel algorithm for processing and classifying 15 finger movements from surface EMG signals based on Welch power estimation from frequency analysis. Five time-domain features are extracted and trained with a machine learning classifier to classify 15 single fingers and combined finger gestures from eight healthy subjects. The experimental results show 92.30 % classification accuracy considering data from eight channels which was improved to 94.15 % after selecting two channels as dominating. For performance evaluation, 10-fold cross-validation is used during classification. We demonstrate an average accuracy of 92.35 % with 25 % test data.

A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography

Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine interaction. Recently, there has been a growing interest in ER based on electroencephalogram (EEG) signals. However, due to the noisy, nonlinear, and nonstationary properties of electroencephalography signals, developing an automatic and high-accuracy ER system is still a challenging task. In this study, a pretrained deep residual convolutional neural network model, including 17 convolutional layers and one fully connected layer with transfer learning technique in combination frequency-channel matrices (FCM) of two-dimensional data based on Welch power spectral density estimate from the one-dimensional EEG data has been proposed for improving the ER by automatically learning the underlying intrinsic features of multi-channel EEG data. The experiment result shows a mean accuracy of 93.61 ± 0.84%, a mean precision of 94.70 ± 0.60%, a mean sensitivity of 95.13 ± 1.02%, a mean specificity of 91.04 ± 1.02%, and a mean F1-score of 94.91 ± 0.68%, respectively using 5-fold cross-validation on the DEAP dataset. Meanwhile, to better explore and understand how the proposed model works, we noted that the ranking of clustering effect of FCM for the same category by employing the t-distributed stochastic neighbor embedding strategy is: softmax layer activation is the best, the middle convolutional layer activation is the second, and the early max pooling layer activation is the worst. These findings confirm the promising potential of combining deep learning approaches with transfer learning techniques and FCM for effective ER tasks.

Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG.

A Welch-EWT-SVD time–frequency feature extraction model for deformation monitoring data

It is a considerable challenge to accurately extract time–frequency features from non-linear and non-stationary deformation monitoring data. In this contribution, a time–frequency feature extraction model that integrates the Welch algorithm, empirical wavelet transform (EWT), and singular value decomposition (SVD) (Welch-EWT-SVD) is proposed. To avoid the impact of unreasonable spectral segmentation, the signal is decomposed at multiple levels based on the Welch power spectrum. The decomposed signal is then processed by EWT to obtain Intrinsic Mode Functions (IMFs). Finally, SVD is adopted to further denoise the IMFs to finely extract time–frequency features. Simulation tests and a field test at the Great Wall are conducted to validate the proposed method. The results demonstrate that Welch-EWT-SVD outperforms EWT, EMD, and Welch-EWT methods in terms of accuracy for time–frequency feature extraction. According to the simulation results, the relative error rate of its extracted frequency is kept within a maximum of 0.10%.

Spectral features of non-nutritive suck dynamics in extremely preterm infants.

Non-nutritive suck (NNS) is used to promote ororhythmic patterning and assess oral feeding readiness in preterm infants in the neonatal intensive care unit (NICU). While time domain measures of NNS are available in real time at cribside, our understanding of suck pattern generation in the frequency domain is limited. The aim of this study is to model the development of NNS in the frequency domain using Fourier and machine learning (ML) techniques in extremely preterm infants (EPIs). A total of 117 EPIs were randomized to a pulsed or sham orocutaneous intervention during tube feedings 3 times/day for 4 weeks, beginning at 30 weeks post-menstrual age (PMA). Infants were assessed 3 times/week for NNS dynamics until they attained 100% oral feeding or NICU discharge. Digitized NNS signals were processed in the frequency domain using two transforms, including the Welch power spectral density (PSD) method, and the Yule-Walker PSD method. Data analysis proceeded in two stages. Stage 1: ML longitudinal cluster analysis was conducted to identify groups (classes) of infants, each showing a unique pattern of change in Welch and Yule-Walker calculations during the interventions. Stage 2: linear mixed modeling (LMM) was performed for the Welch and Yule-Walker dependent variables to examine the effects of gestationally-aged (GA), PMA, sex (male, female), patient type [respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD)], treatment (NTrainer, Sham), intervention phase [1, 2, 3], cluster class, and phase-by-class interaction. ML of Welch PSD method and Yule-Walker PSD method measures revealed three membership classes of NNS growth patterns. The dependent measures peak_Hz, PSD amplitude, and area under the curve (AUC) are highly dependent on PMA, but show little relation to respiratory status (RDS, BPD) or somatosensory intervention. Thus, neural regulation of NNS in the frequency domain is significantly different for each identified cluster (classes A, B, C) during this developmental period. Efforts to increase our knowledge of the evolution of the suck central pattern generator (sCPG) in preterm infants, including NNS rhythmogenesis will help us better understand the observed phenotypes of NNS production in both the frequency and time domains. Knowledge of those features of the NNS which are relatively invariant vs. other features which are modifiable by experience will likewise inform more effective treatment strategies in this fragile population.

Interfacial debonding detection of steel beams reinforced by CFRP plates based on percussion method

Steel structure reinforced engineering makes extensive use of carbon fiber reinforced plastic (CFRP) plates. Although it happens frequently, the interfacial debonding of CFRP plate-reinforced steel beams has been the main cause of failure in flexural strengthened steel beams, which results in the brittle failure of steel structures. This paper proposes a novel percussion method to locate and quantify the debonding area based on the Welch power spectrum density (PSD) estimate, mel frequency cepstral coefficient (MFCC), and convolutional neural network (CNN). According to our knowledge, this is the first attempt to use the percussion method to identify and measure the debonding defects of steel beams strengthened by CFRP plates. Additionally, this experiment takes into account how percussion instruments affect localization and quantification. To begin with, the debonding location is qualitatively detected using the Welch PSD estimate method. The presented percussion method can successfully and accurately detect the interfacial debonding of CFRP plate-reinforced steel beams, according to repeated results. Subsequently, a novel CNN model is constructed to classify the various debonding areas based on the MFCC feature. In the end, the experimental findings show that the localization and quantification of the hammer types vary only slightly. Overall, the percussion-based approach can offer a fresh line of inquiry into the debonding problem on steel beams reinforced with CFRP plates.

Low-frequency acoustic rotorcraft classification

Rotorcraft emit unique acoustic signatures tied primarily to their blade pass frequencies. In this study, we examine the ability of artificial neural network machine learning methods to successfully classify several distinct types of rotorcraft based on their low-frequency acoustic signatures. Time series, power spectral density, and Welch power spectral density input types are examined, corresponding to convolutional neural networks for the first input type, and multi-layer perceptrons for the latter two input types. A K-fold cross-validation is used to initially assess the generalization performance of the various models, along with confusion matrices for a deeper dive into the model performance. Ablation studies are discussed for insights into model behavior. These insights are used to showcase further enhanced neural network models with increased robustness and accuracy.

EEG power spectrum analysis for Tunisian schizophrenic patients

IntroductionSchizophrenia (SCZ) is a common and disabling psychiatric condition. Its diagnosis is entirely clinical. Many researchers have looked into electroencephalogram (EEG) power spectrum analysis for SCZ to find specific abnormalities.Objectives The objective of this study was to analyze the EEG of patients with SCZ using power spectral density and to compare them with those of healthy controls, in order to look for specificities.Methods This was a cross-sectional, descriptive, and analytical case-control study conducted with patients followed for SCZ in the psychiatry "C" department at the Hedi Chaker hospital in Sfax. Healthy controls were included. Patients were assessed by the Positive and Negative schizophrenia scale (PANSS). All participants benefited from an EEG at rest condition at the service of the functional exploration at the Habib Bourguiba hospital in Sfax. We have measured the powers of each band using Welch power spectral density method called absolute power (AP). Statistical analyses were carried out.ResultsFifteen schizophrenics and 15 controls, all male, were included. The average age of schizophrenics and healthy controls was 40 years ±12.72 years and 47.93 ± 15.61 years respectively. There were no significant differences in age between patients and controls. Schizophrenics had a mean PANSS of 64.6±22.7.At the quantitative EEG, differences appeared to be insignificant. There was an overall decrease in AP for the alpha band particularly in the parietal and occipital lobes in schizophrenics (53,16 ± 48,83 μV2 and 75,17 ± 56,28 μV2 respectively) compared to controls (335,15 ± 994,73 μV2 and 400,24 ± 1109,95 μV2 respectively). There was also an overall decrease in AP for the different frequency bands in schizophrenics compared to controls. However, values persisted high in the temporal lobe for all frequency bands.ConclusionsIn conclusion, this decrease in AP for the alpha band in the parietal and occipital lobes in schizophrenics can be a sensitive biomarker for diagnosing SCZ.Disclosure of InterestNone Declared

Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data

Damage of shear connectors in steel-concrete composite (SCC) beams affects the composite action and appears as abnormalities in the shear slip between the composite components. The shear slip at the composite interface causes nonlinearity in the global composite beam response which is an issue beyond the inherent complexity of the composite system. This paper presents a novel approach for damage detection of SCCs by variational mode decomposition (VMD) of shear slip data. Numerical and experimental studies were conducted on steel-concrete composite beams to generate noise-contaminated shear slip data from undamaged and damaged states of the structure. The VMD algorithm is employed to decompose shear slip signals into intrinsic mode functions (IMFs) and then, the center frequency of each mode is captured. The higher center frequency realized in the second mode is taken as a damage sensitive feature. Because IMFs curves are extracted from the signal splines interpolated between the average of the peaks and troughs, the change of energy in the center frequencies of the undamaged and damaged states can be defined as a damage index. Welch power spectral densities of the IMFs are calculated to further investigate changes in the center frequencies of IMFs obtained using the VMD algorithm. The empirical mode decomposition (EMD) technique is also utilized to decompose shear slip signals into IMFs for comparison purposes. The results show that the EMD is not able to detect abnormalities in the shear slip signals affected by damage because of losing information through several mode decompositions and a phenomenon termed mode mixing. However, when the VMD was set to decompose the signal into two IMFs, it was found that it is more efficient in maintaining the frequency content of the shear slip signal. According to the results, the proposed method has been proved successful in detecting damage of composite beams and can be employed as a reliable and robust technique.

Transcranial Direct Current Stimulation Over the Left Dorsolateral Prefrontal Cortex Reduced Attention Bias Toward Negative Facial Expression: A Pilot Study in Healthy Subjects.

Research in the cognitive neuroscience field has shown that individuals with a stronger attention bias for negative information had higher depression risk, which may be the underlying pathogenesis of depression. This dysfunction of affect-biased attention also represents a decline in emotion regulation ability. Clinical studies have suggested that transcranial direct current stimulation (tDCS) treatment can improve the symptoms of depression, yet the neural mechanism behind this improvement is still veiled. This study aims to investigate the effects of tDCS on affect-biased attention. A sample of healthy participants received 20 min active (n = 22) or sham tDCS (n = 19) over the left dorsolateral prefrontal cortex (DLPFC) for 7 consecutive days. Electroencephalographic (EEG) signals were recorded while performing the rest task and emotional oddball task. The oddball task required response to pictures of the target (positive or negative) emotional facial stimuli and neglecting distracter (negative or positive) or standard (neutral) stimuli. Welch power spectrum estimation algorithm was applied to calculate frontal alpha asymmetry (FAA) in the rest task, and the overlapping averaging method was used to extract event-related potentials (ERP) components in the oddball task. Compared to sham tDCS, active tDCS caused an obvious increment in FAA in connection with emotion regulation (p < 0.05). Also, participants in the active tDCS group show greater P3 amplitudes following positive targets (p < 0.05) and greater N2 amplitudes following negative distracters (p < 0.05), reflecting emotion-related attention biases. These results offer valuable insights into the relationship between affect-biased attention and the effects of tDCS, which may be of assistance in exploring the neuropathological mechanism of depression and anxiety and new treatment strategies for tDCS.

Low Complexity Multiplierless Welch Estimator Based on Memory-Based FFT

This paper proposes a novel hardware efficient low power Welch power spectral density (PSD) estimator. The presented multiplier-less hardware uses a combined coefficient selection and shift-and-add implementation (CCSSI) unit in order to prevent multiplications in FFT computation. Two filtering operations, which are implemented in folded architecture, are utilized. The micro-rotation resources of the CCSSI unit can be shared with estimator’s modules simultaneously. The proposed architecture is a nonparametric estimator that operates based on a modified, memory-based, 128-point scalable radix-22 FFT processor. It uses bidirectional fractional delay filter to estimate half delay sample in merging two FFTs. Using modified safe-scaling, the final output would be valid, without any averaging operation. Another important feature of the proposed method is its capability of operating in short word lengths (WL). Artix-7, as an ideal option for DSP applications, is the utilized FPGA in this research. As results demonstrate, the hardware has a high capability in operating in short WLs which results in high performance in low-power applications.

Increased delta power as a scalp marker of epileptic activity: a simultaneous scalp and intracranial electroencephalography study.

Background and purposeThe purpose was to evaluate whether intracranial interictal epileptiform discharges (IEDs) that are not visible on the scalp are associated with changes in the frequency spectrum on scalp electroencephalograms (EEGs).MethodsSimultaneous scalp high‐density EEG and intracranial EEG recordings were recorded in nine patients undergoing pre‐surgical invasive recordings for pharmaco‐resistant temporal lobe epilepsy. Epochs with hippocampal IED visible on intracranial EEG (ic‐IED) but not on scalp EEG were selected, as well as control epochs without ic‐IED. Welch's power spectral density was computed for each scalp electrode and for each subject; the power spectral density was further averaged across the canonical frequency bands and compared between the two conditions with and without ic‐IED. For each patient the peak frequency in the delta band (the significantly strongest frequency band in all patients) was determined during periods of ic‐IED. The five electrodes showing strongest power at the peak frequency were also determined.ResultsIt was found that intracranial IEDs are associated with an increase in delta power on scalp EEGs, in particular at a frequency ≥1.4 Hz. Electrodes showing slow frequency power changes associated with IEDs were consistent with the hemispheric lateralization of IEDs. Electrodes with maximum power of slow activity were not limited to temporal regions but also involved frontal (bilateral or unilateral) regions.ConclusionsIn patients with a clinical picture suggestive of temporal lobe epilepsy, the presence of delta slowing ≥1.4 Hz in anterior temporal regions can represent a scalp marker of hippocampal IEDs. To our best knowledge this is the first study that demonstrates the co‐occurrence of ic‐IED and increased delta power.

Effect of Chaotic Nature on IPHICLUS and NEARCTIC Flapping MAV Wings in Lift Generation using Welch Power Spectral

The application of bio-mimetic in Micro Aerial Vehicles (MAV) is a developing field and has enormous applications in the aviation industry. In this work, experimental investigation of the insect like MAV having flapping wing is discussed. The iphiclus-egensis-aile flexible wing and the nearctic-ceratopogonidae flexible wing were chosen for this study. Values of the coefficient of lift for the unsteady aerodynamics were converted to normalized frequency domain using Welch power spectral density method at various flapping frequencies and Reynolds number. The wing tip vortices were captured using laser visualization technique and further the images were processed using MATLAB. Experimental investigations were carried out in wind tunnel for various velocities and Angle of Attack (AoA) and the results were obtained using digital six component balancing system. Detailed study of both the results shows that the nearctic-ceratopogonidae flexible wing has better lifting properties than the iphiclus-egensis-aile flexible wing at low flapping frequencies and low AoA. For higher flapping frequencies and high AoA, the latter shows better lifting properties than the former.

Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input.

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

Methodology proposal of ADHD classification of children based on cross recurrence plots

Dealing with electroencephalogram signals (EEG) is often not easy. The lack of predicability and complexity of such non-stationary, noisy and high-dimensional signals is challenging. Cross recurrence plots (CRP) have been used extensively to deal with the detection of subtle changes in signals, even when the noise is embedded in the signal. In this contribution, a total of 121 children performed visual attention experiments and a proposed methodology using CRP and a Welch Power Spectral Distribution have been used to classify then between those who have ADHD and the control group. Additional tools were presented to determine to which extent this methodology is able to classify accurately and avoid misclassifications, thus demonstrating that this methodology is feasible to classify EEG signals from subjects with ADHD. The experimental results indicate that the proposed methodology shows higher accuracy in comparison with methods proposed by other authors, providing that the correct recurrence tools are selected. Also, this methodology does not require extensive training such as the methods proposed using classical machine learning algorithms. Furthermore, this proposed methodology shows that it is not required to manually discriminate events among the EEG electrodes since CRP can detect even the smallest changes in the signal even when it has embedded noise. Lastly, the results were compared with baseline machine learning methods to prove experimentally that this methodology is consistent and the results repeatable. Given the right CRP metrics, an accuracy of up to 97.25% was achieved, indicating that this methodology outperformed many of the state-of-the-art techniques.

A Look Into the Power of fNIRS Signals by Using the Welch Power Spectral Estimate for Deception Detection.

Neuroimaging technologies have improved our understanding of deception and also exhibit their potential in revealing the origins of its neural mechanism. In this study, a quantitative power analysis method that uses the Welch power spectrum estimation of functional near-infrared spectroscopy (fNIRS) signals was proposed to examine the brain activation difference between the spontaneous deceptive behavior and controlled behavior. The power value produced by the model was applied to quantify the activity energy of brain regions, which can serve as a neuromarker for deception detection. Interestingly, the power analysis results generated from the Welch spectrum estimation method demonstrated that the spontaneous deceptive behavior elicited significantly higher power than that from the controlled behavior in the prefrontal cortex. Meanwhile, the power findings also showed significant difference between the spontaneous deceptive behavior and controlled behavior, indicating that the reward system was only involved in the deception. The proposed power analysis method for processing fNIRS data provides us an additional insight to understand the cognitive mechanism of deception.

Acoustic emissions in vertebral cortical shell failure

Understanding the initiation of bony failure is critical in assessing the progression of bone fracture and in developing injury criteria. Detection of acoustic emissions in bone can be used to identify fractures more sensitively and at an earlier inception time compared to traditional methods. However, high rate loading conditions, complex specimen-device interaction or geometry may cause other acoustic signals. Therefore, characterization of the isolated local acoustic emission response from cortical bone fracture is essential to distinguish its characteristics from other potential acoustic sources. This work develops a technique to use acoustic emission signals to determine when cortical bone failure occurs by characterization using both a Welch power spectral density estimate and a continuous wavelet transform. Isolated cortical shell specimens from thoracic vertebral bodies with attached acoustic sensors were subjected to quasistatic loading until failure. The resulting acoustic emissions had a wideband frequency response with peaks from 20 to 900 kHz, with the spectral peaks clustered in three bands of frequencies (166 ± 52.6 kHz, 379 ± 37.2 kHz, and 668 ± 63.4 kHz). Using these frequency bands, acoustic emissions can be used as a monitoring tool in biomechanical spine testing, distinguishing bone failure from structural response. This work presents a necessary set of techniques for effectively utilizing acoustic emissions to determine the onset of cortical bone fracture in biological material testing. Acoustic signatures can be developed for other cortical bone regions of interest using the presented methods.