Prior EEG research has primarily focused on N-back cognitive task data, utilizing established techniques such as timefrequency spectrum and wavelet-based methods for feature extraction. However, Principal Component Analysis (PCA), despite its utility in boosting classifier performance, falls short in capturing nonlinear feature relationships. This study proposes a novel approach that integrates Multivariate Empirical Mode Decomposition (MEMD) with Independent Component Analysis (ICA) to enhance signal processing and feature extraction. Multivariate empirical mode decomposition generates analytic functions from EEG data by decomposing the multichannel EEG into Intrinsic Mode Functions (IMFs), from which diverse features are extracted. ICA then further reduces dimensionality, leveraging higher-order statistics to pinpoint critical features. The resulting significant, independent features are used to train and test various machine learning models, with the k-nearest neighbors algorithm emerging as the most successful, achieving a remarkable 95.27% classification accuracy. This approach enhances feature extraction and classification of cognitive task-related EEG data.

Superconducting Quantum Interference Devices (SQUID) are known for their unmatched sensitivity in measuring extremely weak magnetic fields. The technique of measuring the weak magnetic fields (50-100 pico Tesla) generated by the electrical activity of the heart is magnetocardiography (MCG). This completely noninvasive and non-contact technique is known to offer diagnostic information that complements the conventional ECG. This paper highlights the capabilities of the first Magnetocardiography (MCG) facility established in the country at the Materials Science Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam. The utility of MCG in clinical cardiology is demonstrated through case studies conducted in collaboration with hospitals, focusing on subjects with cardiac anomalies. Furthermore, the study investigates the operational feasibility of the MCG setup in unshielded environments, specifically targeting hospital-based applications. Additionally, the potential for deploying cost-effective, home-built SQUID electronics is assessed by utilizing readily available electronic components, emphasizing their viability for broader clinical adoption.

Accurate liquid nitrogen level measurement is crucial in cryogenics for safety and optimal storage. Industrial-grade solutions, such as optical fibers and capacitive sensors, are effective but costly and complex, while conventional dipstick methods suffer from heat transfer issues and measurement inaccuracies. This study explores the feasibility of using an ultrasonic sensor for non-contact level detection in an open-mouth laboratory storage setup. By measuring the time of flight of ultrasonic pulses and applying a machine learning model to compensate for environmental fluctuations, the proposed approach offers a non-contact, accurate, and reliable alternative for laboratory applications.

Magnetocardiography (MCG) offers a non-contact, non-invasive way of capturing the heart's weak magnetic field signals. MCG measurements often suffer from physiological artifacts, particularly during exercise. Blind source separation algorithms (BSS) are commonly utilized to eliminate noise in multichannel systems. This study assesses the performance of BSS algorithms in noise removal from multichannel MCG recordings under varying physical conditions. We used three BSS algorithms such as fast independent component analysis (Fast ICA), temporally decorrelated source separation (TDSEP), and second order blind identification (SOBI) for noise reduction in different physical conditions of the subject rest, stress, and recovery. It has been observed that Fast ICA demonstrated superior performance in rest conditions, while SOBI exhibited better performance during stress conditions, as evidenced by higher signal-to-noise ratio (SNR) across all cardiac beats. This study helps to explore the possibility of improving the quality of MCG signals, facilitating the cardiac health assessment under different physical conditions, especially for subjects with diverse cardiac abnormalities.

We use Fourier transform infrared spectroscopy (FTIR) and photoluminescence spectroscopy to characterize boron and nitrogen concentrations needed for the stabilization of neutral silicon vacancy centers (SiV0) in Si-implanted diamonds co-doped with boron and nitrogen.

Background. Magnetocardiography (MCG) is a non-invasive and non-contact technique that measures weak magnetic fields generated by the heart. It is highly effective in the diagnosis of heart abnormalities. Multichannel MCG provides detailed spatio-temporal information of the measured magnetic fields. While multichannel MCG systems are costly, usage of the optimal number of measurement channels to characterize cardiac magnetic fields without any appreciable loss of signal information would be economically beneficial and promote the widespread use of MCG technology. Methods. An optimization method based on the sequential selection approach is used to choose channels containing the maximum signal information while avoiding redundancy. The study comprised 40 healthy individuals, along with two subjects having ischemic heart disease and one subject with premature ventricular contraction. MCG measured using a 37 channel MCG system. After revisiting the existing methods of optimization, the mean error and correlation of the optimal set of measurement channels with those of all 37 channels are evaluated for different sets, and it has been found that 18 channels are adequate. Results. The chosen 18 optimal channels exhibited a strong correlation (0.99 +/- 0.006) between the original and reconstructed magnetic field maps for a cardiac cycle in healthy subjects. The root mean square error is 0.295 pT, indicating minimal deviation. Conclusion. This selection method provides an efficient approach for choosing MCG, which could be used for minimizing the number of channels as well as in practical unforeseen measurement conditions where few channels are noisy during the measurement.

This study aims to develop an automated method for de-noising cardiac signals using independent component analysis (ICA) on a 37-channel magnetocardiography (MCG) system. The traditional approach of applying ICA involves manual visual inspection to determine the retention or removal of independent component (IC) related to signal or noise, which is time-consuming and lacks assurance in preserving essential attributes of signal components during the de-noising process. To address these challenges, we propose a novel approach. A feature set comprising spectral, statistical, and nonlinear time series properties is computed from the ICs of thirty subjects. These features are then evaluated by a few machine learning (ML) models to optimally select ICs for de-noising cardiac time series. It is found that ICs evaluated by a gradient boosting decision tree (GBDT) classifier could accomplish the task of efficiently selecting components to de-noise MCG with an accuracy of 93

NV-based magnetometry in single-crystal diamond grown by chemical vapour deposition (CVD), is now a wellestablished technology with demonstrated applications in DC and AC bulk magnetometry. The approx. 500 µm thick plates normally used offer limited contrast when attempting to measure the magnetic properties of small samples (for example biological samples or minerals) placed in proximity of the diamond magnetic sensor. Such applications would benefit from a few µm high-[NV] layer on a low luminescence substrate in order to collect the signal only from NV centres spatially close to the area of interest, allowing the formation of a magnetic image with increased resolution. It is important to ensure that the strain in the high-[NV] layer is spatially uniform and low in magnitude, to preserve the magnetic resolution and to avoid unusable regions on the magnetic sensor. Established techniques to manage strain during CVD diamond growth are not applicable for the deposition of a few µm of material; normally, the substrate would undergo extensive plasma etching to remove contaminants and polishing damage from the surface of the substrate. This is not possible for thin layers, since the etching would increase the roughness and produce NV layers with non-uniform thickness. Here, we present recent developments to obtain thin, high-[NV] layers on high purity substrates with large areas of low strain, by discussing the substrate preparation and strain characterisation before and after growth.

Ensembles of nitrogen vacancy centres (NVCs) in diamond can be employed for sensitive magnetometry. In this work we present a fiber-coupled NVC magnetometer with an unshielded sensitivity of (30 $\pm$ 10) pT/$\sqrt{\textrm{Hz}}$ in a (10 - 500)-Hz frequency range. This sensitivity is enabled by a relatively high green-to-red photon conversion efficiency, the use of a [100] bias field alignment, microwave and lock-in amplifier (LIA) parameter optimisation, as well as a balanced hyperfine excitation scheme. Furthermore, a silicon carbide (SiC) heat spreader is used for microwave delivery, alongside low-strain $^{12}\textrm{C}$ diamonds, one of which is placed in a second magnetically insensitive fluorescence collecting sensor head for common-mode noise cancellation. The magnetometer is capable of detecting signals from sources such as a vacuum pump up to 2 m away, with some orientation dependence but no complete dead zones, demonstrating its potential for use in remote sensing applications.

The magnetocardiogram (MCG) is an innovative method for non-contact assessment of subtle magnetic fields linked to the heart’s electric behavior. However, signals from chest movement during respiration disrupt MCG data, challenging accurate interpretation. Traditional techniques like high-pass filters and wavelets mitigate these effects but require manual effort for optimal output. We propose an automated approach using empirical mode decomposition and principal component analysis to remove breathing artifacts from single-channel MCG data. Our method reduces distortion and outperforms conventional techniques, enhancing MCG data interpretation efficiency.

In this paper, we report a novel way of choosing the initial estimates for solving magnetocardiographic inverse problems using the pattern search method. As opposed to the conventional choice of pseudo random numbers, the co-ordinates of the maximum spatial gradient of magnetic fields across sensor locations were used as initial estimates for pattern search method with the help of current density maps. The proposed method has been extensively validated by computer simulation and test coil localization before applying to real magnetocardiogram of a few healthy subjects and those with a known conduction anomaly and ischemic heart disease.

Many studies have been carried out related to the analysis of cognitive workload assessment using the N-back task. However, fixed analytic functions like time-frequency spectrum and wavelet-based approaches have been primarily used to analyze non-stationary EEG signals. Moreover, these approaches removed redundant information present in the input features by implementing the feature reduction approaches like correlation analysis and Principal Component Analysis, which are primarily based on the assumption of linearity in the input features. In the present work, we have analyzed multichannel EEG data for the N-back (0, 1, 2-back) task using a data-driven technique called multivariate empirical mode decomposition (MEMD). MEMD breaks down multichannel data into a fixed number of intrinsic mode functions (IMFs). Various features have been extracted from each IMF based on statistical parameters (variance, skewness, and kurtosis), spectral power (related to brain waves: delta, theta, alpha, beta, and gamma), and parameters based on time-series data (relative MEMD energy and zero-crossing rate). The effective feature reduction is obtained by kernel principal component analysis (kPCA). These new reduced transformed features are taken as input for training and testing different machine learning (ML) models viz K-nearest neighbor (KNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and random forest. The best average classification accuracy of 97.34% could be achieved using KNN with kPCA transformed features (based on third-order polynomial kernel function). The proposed approach performs better in classifying the N-back EEG data than earlier techniques.

The large signal due to cardiac activity can easily distort the signals originating from the relatively weak electrical activity of the brain, commonly measured as an Electroencephalogram (EEG). The artifact due to cardiac activity in EEG is called cardiac artifact, which contaminates the EEG data and makes interpretation of the EEG difficult for clinicians. Hence it is crucial to remove the cardiac artifact from EEG data. To suppress the cardiac artifact, we propose a novel approach to effectively extract cardiac artifacts from single-channel contaminated EEG data without using reference Electrocardiogram (EKG) data. The proposed methodology uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose EEG data contaminated by cardiac activity into the Intrinsic Mode Functions (IMFs). Principal Component Analysis (PCA) is performed on these IMFs to obtain the principal components arranged in the order of decreasing variance. Effective cardiac artifact extraction is achieved by optimizing the signal reconstruction process so that only those principal components that capture the cardiac activity are retained with the constraint that distortion introduced in EEG data should be minimum. The comparison clearly shows that the proposed method outperforms conventionally employed methods like wavelet-based approach.

Magnetocardiography (MCG) measures weak magnetic fields originating due to the electrical activity of the heart. MCG offers distinct diagnostic information on the cardiac electrophysiology in a variety of dysfunctions. This list includes myocardial ischemia, which is associated with reduced blood supply to the heart, electrically manifested as changes in the ST segment of the cardiac cycle. As opposed to the conventional measurement of electrocardiogram (ECG) on subjects undergoing physical stress test to investigate these ST changes, rest MCG itself has been demonstrated to be more sensitive. Considerable interest exists among researchers to investigate MCG signals measured during physical exertion as well to explore the possibilities of improvements in its sensitivity. This paper portrays the MCG measurements of a few subjects under rest and during moderate cycling in supine posture using a non-magnetic bicycle ergometer. The work details the signal processing steps followed in processing MCG to refine the signal quality in computing the parameter, ST fluctuation score in an automated manner. Significant changes are seen on the ST fluctuation scores measured on a few healthy subjects across rest and stress conditions. These results persuade its possible use on MCG measured on subjects with ischemic heart diseases by treating this analysis as baseline measurements.

We demonstrate a simple, robust and contactless method for non-destructive testing of magnetic materials such as steel. This uses a fiber-coupled magnetic sensor based on nitrogen vacancy centers (NVC) in diamond without magnetic shielding. Previous NVC magnetometry has sought a homogeneous bias magnetic field, but in our design we deliberately applied an inhomogeneous magnetic field. As a consequence of our experimental set-up we achieve a high spatial resolution: 1~mm in the plane parallel and 0.1~mm in the plane perpendicular to the surface of the steel. Structural damage in the steel distorts this inhomogeneous magnetic field and by detecting this distortion we reconstruct the damage profile through quantifying the shifts in the NVC Zeeman splitting. This works even when the steel is covered by a non-magnetic material. The lift-off distance of our sensor head from the surface of 316 stainless steel is up to 3~mm.

Electroencephalogram (EEG) is a non-invasive measurement of electrical signal on the scalp originated due to neuronal activity. EEG signals associated with cortical activity are orders of magnitude lower in amplitude compared to other parasitic signals such as eye-blink artifacts, which contaminate the recorded EEG data making it imperative for the investigators to adopt an effective artifact suppression strategy. In all the previous studies, suppression of the pattern related to the artifacts was performed based on the assumption of a linear interaction between the source of artifact (eye-blink) and the brain signal (EEG data). This paper presents a novel methodology by considering the non-linear interaction between the artifact signal associated with the eye-blink and the contaminated EEG data using kernel functions. In the present work, adaptive data-driven approach called Ensemble Empirical Mode Decomposition (EEMD) is hybridized with kernel Principal Component Analysis (kPCA) to decode the non-linear interaction. The contaminated segment of EEG data is decomposed by the EEMD technique into a series of basic building blocks called intrinsic mode functions (IMFs). The features of the eye-blink signals are captured by some of these IMFs; subsequently, effective extraction of the ocular artifact from the IMFs is performed by kPCA using non-linear kernel functions (radial basis function, second and third-order polynomial function). In the present study, technique used for artifact suppression relies on the extraction of ocular artifact signal from IMFs based on optimizing the different parameters of the kPCA. Compared with other techniques used in previous studies, the proposed method (based on third-order polynomial kernel function) is capable of effectively extracting the pattern related to the ocular artifacts from the single channel contaminated EEG data with low distortion of the EEG signal. (C) 2018 Elsevier B.V. All rights reserved.

Sensing small magnetic fields is relevant for many applications ranging from geology to medical diagnosis. We present a fiber-coupled diamond magnetometer with a sensitivity of (310 $\pm$ 20) pT$/\sqrt{\text{Hz}}$ in the frequency range of 10-150 Hz. This is based on optically detected magnetic resonance of an ensemble of nitrogen vacancy centers in diamond at room temperature. Fiber coupling means the sensor can be conveniently brought within 2 mm of the object under study.

We present characterization of a lock-in amplifier based on a field programmable gate array capable of demodulation at up to 50 MHz. The system exhibits 90 nV/√Hz of input noise at an optimum demodulation frequency of 500 kHz. The passband has a full-width half-maximum of 2.6 kHz for modulation frequencies above 100 kHz. Our code is open source and operates on a commercially available platform.

BACKGROUND:Identification of coronary ischemia in patients presenting with chronic chest pain is difficult as resting ECG can be normal. Diagnosis of coronary ischemia requires evaluation during exercise or pharmacological stress. A noninvasive test to identify coronary ischemia at rest without the need for exercise is desirable. We studied the diagnostic accuracy of magnetocardiography (MCG) at rest to detect coronary ischemia in these patients.METHODS:Patients with chronic chest pain and suspected coronary ischemia with a normal ECG were included. Patients underwent treadmill test (TMT) and were divided into TMT positive and TMT negative groups. MCG was recorded in a magnetically shielded room. Iso-field contour maps generated at the T-wave peak were compared between the groups. From the magnetic field map (MFM), the magnetic field angle at T-wave peak was calculated and was also compared across the two groups.RESULTS:There were a total of 29 patients, 12 with positive TMT and 17 with negative TMT. An abnormal magnetic field angle was more common in the TMT positive group (72% vs. 6%). Abnormal contour maps in the form of nondipole patterns or abnormal orientation were seen in 81.8% (9/11) patients in TMT positive group and 6.8% (1/17) patients in the TMT negative group (p < .001).CONCLUSION:Abnormal magnetic field angle and abnormal magnetic field maps in MCG recorded at rest are able to identify the presence of coronary ischemia in patients with chronic chest pain and a normal resting ECG.