Blind Source Separation Research Articles

On the Initial Phase of the Ongoing Unrest at Campi Flegrei and Its Relation with Subsidence at Vesuvio (Italy)

The densely inhabited area of Naples (Italy), between the Campi Flegrei and Vesuvio volcanoes, is one of the most hazardous regions in the world. After two decades of sustained subsidence, Campi Flegrei has been experiencing an accelerating uplift since 2005. The uplift is currently associated with unusual seismicity and increased degassing. To try to identify the cause of the shift from subsidence to uplift and explore any connection between Campi Flegrei and Vesuvio, we analysed the ground displacement time series of the two volcanoes from 1993 to 2010, obtained from ERS/ENVISAT Synthetic Aperture Radar imagery. To distinguish between the various sources of deformation, we used simple scatter plots and a blind source separation technique called variational Bayesian independent component analysis (vbICA). We obtained consistent results using both approaches. Specifically, with vbICA, we identified two significant independent components (ICs). IC1 describes the subsidence that occurred at Campi Flegrei prior to 2000, including the mini-uplifts of 2000 and 2005, and part of the post-2005 uplift. The expansion and contraction of two volumes beneath Campi Flegrei satisfy IC1: a sill-shaped volume at a depth of approximately 3 km and a small volume at a depth of 1–2 km, respectively. The two sources of deformation reproduce the large-scale deformation in the Campi Flegrei area and the local deformation in the Solfatara area, respectively. In the Campi Flegrei area, IC2 exhibits primarily uplift, which is concentrated in the eastern part of the caldera. The deformation pattern is complex and difficult to interpret. If we model it using simple spheroidal deformation sources, the pattern suggests that two volumes at depths of approximately 9 and 8 km are experiencing opposite activity, namely contraction (beneath the southwestern part of the caldera) and expansion (beneath the central part of the caldera). In the Vesuvio area, IC2 is consistent with the deformation induced by the contraction of a volume at a depth of around 9 km. The contraction beneath Vesuvio is smaller in magnitude than the expansion/contraction beneath Campi Flegrei. The correlation observed after 2002 between uplift at Campi Flegrei and subsidence at Vesuvio suggests the transfer of magma and/or magmatic fluids between the two plumbing systems at 8–9 km depth. This implies that part of the ongoing unrest at Campi Flegrei may have been promoted by mass transfer from below Vesuvio.

End-to-end integration of speech separation and voice activity detection for low-latency diarization of telephone conversations

Recent works show that speech separation guided diarization (SSGD) is an increasingly promising direction, mainly thanks to the recent progress in speech separation. It performs diarization by first separating the speakers and then applying voice activity detection (VAD) on each separated stream. In this work we conduct an in-depth study of SSGD in the conversational telephone speech (CTS) domain, focusing mainly on low-latency streaming diarization applications. We consider three state-of-the-art speech separation (SSep) algorithms and study their performance both in online and offline scenarios, considering non-causal and causal implementations as well as continuous SSep (CSS) windowed inference. We compare different SSGD algorithms on two widely used CTS datasets: CALLHOME and Fisher Corpus (Part 1 and 2) and evaluate both separation and diarization performance. To improve performance, a novel, causal and computationally efficient leakage removal algorithm is proposed, which significantly decreases false alarms. We also explore, for the first time, fully end-to-end SSGD integration between SSep and VAD modules. Crucially, this enables fine-tuning on real-world data for which oracle speakers sources are not available. In particular, our best model achieves 8.8% DER on CALLHOME, which outperforms the current state-of-the-art end-to-end neural diarization model, despite being trained on an order of magnitude less data and having significantly lower latency, i.e., 0.1 vs. 1 s. Finally, we also show that the separated signals can be readily used also for automatic speech recognition, reaching performance close to using oracle sources in some configurations.

Post-processing of a distributed source method for the localization of somatosensory cortex in a cohort of epilepsy patients

Localizing eloquent cortices is crucial for many neurosurgical applications, such as epilepsy and tumor resections. Clinicians may use non-invasive methods such as magnetoencephalography (MEG) to localize these cortical regions using equivalent current dipoles (ECDs). While dipoles are clinically validated, they provide the estimated strength, location, and orientation of only one or a few sources that best describe the recorded neuromagnetic data, requiring clinicians to make subjective decisions on the spatial extent of the underlying cortical area. More accurate delineation of eloquent cortical areas using distributed source localization methods would provide additional pre-surgical information on these regions’ location and spatial distribution, which could lead to reduced post-surgical complications associated with damage to or removal of eloquent cortices. Our objective in this paper was to present a method to post-process the distributed source localization results to yield a directly interpretable, distributed region of activation. As a test case, we selected somatosensory stimulation in a retrospective cohort of focal and multi-focal epilepsy patients. Our algorithm performs source localization using a distributed method (sLORETA), followed by post-processing and blind source separation to identify the area and boundary of the cortical tissue that primarily activates in response to somatosensory stimulation. We calculated the statistical significance of localization by comparing the identified region to an anatomical atlas and random chance. While examining patients who received left (upper left, UL) and right (upper right, UR) sided median nerve stimulation, the cortical areas identified by the algorithm were in anatomically appropriate areas with a median overlap of 97.6% and 94.7%, respectively. We observe that our algorithm localized somatosensory responses better than random chance in 57/58 (98%) patients who performed the UL task (p < 10 × 10−10, binomial test) and 49/50 (98%) patients who performed the UR task (p < 10 × 10−10, binomial test). We compared the localization of our algorithm to current clinical methods and found that our algorithm is not inferior to dipole localization. The algorithm can successfully localize somatosensory responses on the cortical surface in anatomically appropriate regions while providing the spatial extent of cortical activation, reducing subjectivity associated with dipole localization.

Integrated photonic waveguides for on-chip SBS with OAM modes

Stimulated Brillouin Scattering (SBS) is a nonlinear optical process that can provide significant on-chip gain in various materials and platforms. The utilization of chalcogenide and silicon (Si) waveguides has facilitated the advancement of Brillouin lasers and diverse microwave photonic functionalities. The predominant approach has been centered on interactions between conventional guided modes, specifically TE/TM. This study introduces a novel approach that explores diverse guided-wave configurations capable of sustaining SBS with orbital angular momentum modes (OAM). These configurations include a cross-Si waveguide and a silica/chalcogenide waveguide. The simulations examine the forward and backward SBS utilizing OAM modes up to the 4th order in planar on-chip waveguides. A maximum on-chip SBS gain of up to ∼540W−1m−1 has been observed for pump and Stokes OAM mode numbers satisfying the topological charge conservation law. This OAM multiplexing approach can open new avenues for on-chip integrated nonlinear photonics for various applications, especially where pump and signal beam filtering or separation are critical.

Research on heart and lung sound separation method based on DAE–NMF–VMD

Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BackgroundMetabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. ResultsThis study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, ‘Low-Level data fusion’ (LLDF) and ‘Mid-Level data fusion’ (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing—encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. SignificanceBoth methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis.

Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.

Accurate Identification of Motoneuron Discharges From Ultrasound Images Across the Full Muscle Cross-Section.

Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. Here, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across Ten participants using concomitant sEMG decomposition as the ground truth. 140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 ± 10.3%. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin. The proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. The proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

Single-Channel Radar Signal Separation Based on Instance Segmentation With Mask Optimization

Single-Channel Radar Signal Separation Based on Instance Segmentation With Mask Optimization

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

Utilizing data-driven algorithms for blind modal parameter identification of structures from output-only video measurements

In structural dynamics and vibration analysis, modal identification plays a pivotal role in understanding and characterizing the dynamic behavior of complex systems. Traditional modal analysis techniques heavily rely on accurate sensor placement and knowledge of the excitation forces, which may not always be feasible or practical in real-world scenarios. Recently, non-contact video-based modal analysis methods for structures with arbitrary complexity using computer vision techniques have gained much importance. But these methods need to know ahead of time where the structure's natural frequency ranges are, and they use steerable pyramids, which are complicated multi-scale image decomposition filters. To address these issues, a blind modal identification technique is suggested to extract the modal parameters (modal frequency and mode shapes) from the recorded structural vibration video signal. The proposed algorithm for unsupervised machine learning is the Non-Negative Matrix Factorization (NNMF) method, which is combined with the Generalized Complexity Pursuit (GCP) method for blind source separation. The NNMF algorithm is directly applied to the raw pixel-time series made from the video data to get the temporal components, which are then separated using GCP to find the individual modal frequencies and mode shapes. The above algorithm is first validated on a numerical model and then implemented on laboratory scale models (cantilever, single-degree, and multi-degree frame models). Finally, the proposed methodology is implemented on real-world recorded structural vibration videos to determine its noise sensitivity, such as the Tacoma Narrows bridge and the London Millennium footbridge. The modal parameters extracted are compared with those from the available literature for validation. The estimation errors obtained from all the validations are well below 1%, which makes the technique quite suitable and reliable for structural vibration monitoring by identifying and reproducing complex vibration modes.

On the factor ambiguity of MCR problems for blockwise incomplete data sets

Multivariate curve resolution (MCR) methods are sometimes faced with missing or erroneous data, e.g., due to sensor saturation. In some cases, an estimation of the missing data is possible, but often MCR works with the largest submatrix without missing entries. This ignores all rows and columns of the data matrix that contain missing values. A successful approach to deal with incomplete data multisets has been proposed by Alier and Tauler (2013), but it does not include a factor ambiguity analysis. Here, the missing data problem is addressed in combination with a factor ambiguity analysis. An approach is presented that minimizes the factor ambiguity by extracting a maximum of spectral information even from incomplete rows and columns of the spectral data matrix. The method requires a high signal-to-noise ratio. Applications are presented for UV/Vis and HSI data.

Analysis of the hydration water on the surface of human hair using a combination of infrared absorption vibrational spectroscopy and multivariate curve resolution

Modern society's keen regard for aesthetics made hair products an integral part of a multi‐billion‐dollar cosmetic industry. Hair care products (e.g., shampoos and conditioners) and chemical treatments (e.g., bleaching and permanent waving) result in various effects on the morphological attributes of hair. Generally, water adsorbed on the hair surface is known to significantly dictate the hair's mechanical characteristics (smoothness and friction), and hair's macroscopic wettability has been commonly used to indicate its surface properties. However, an approach to selectively characterize the hydration water in the hair surface is required to accurately understand the intermolecular events between the hair and its vicinal water. In this paper, we successfully obtained the infrared (IR) absorption spectra of the hydration water of human hair. We employed the multivariate curve resolution‐alternating least square (MCR‐ALS) method to separate the hydration and bulk water spectra from the whole spectra. Comparing the IR spectra of the hydration water of chemically untreated and bleached hair samples, we conclude that water molecules form strong hydrogen bonds with the bleached hair surface due to the destruction of the hair's hydrophobic outer layer and the consequent formation of hydrophilic residues.

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

Non-Invasive Fetal Well-Being Monitoring Approaches: A Mini Review on Fetal Signal Separation Techniques

Fetal signal separation is vital in producing an accurate interpretation of the health condition of a fetal. In the context of a non-invasive fetal monitoring approach, the signals are acquired from the abdomen of pregnant women. As a result, a mix of maternal and fetal signals is obtained. These maternal and fetal signals are vague, as both signals are interchanged during the signal acquisition stage. Since the signals are overlapped, a signal separation technique must be employed to process the fetal signal for further analysis. This paper presents published studies on applying signal processing techniques involving fetal signal separation. These papers are obtained through a strategy known as the PRISMA technique. The online databases include ACM, Emerald Publishing, IEEE Explore Digital Library, Science Direct, Scopus, and Springer, with published years spanning from 2018 until 2022. Numerous separation techniques were found, such as adaptive filtering, blind source separation (BSS), and alternative approaches. Issues on the existing methods for fetal signal separation are discussed. In addition, the limitations and drawbacks of the research work involving existing fetal signal separation are reviewed in the paper. The potential direction of future research in this field is addressed as well. Based on this mini-review, it can be concluded that noise and ambiguity can still occur in the extracted fetal signals, even when signal processing techniques are applied. In the future, deep learning would be accommodating in improving the efficiency of extracting fetal signals obtained from the non-invasive fetal well-being monitoring technique. Meanwhile, apart from fetal heart rate (fHR) detection, fetal hypoxia can also be another important focus of study for improving fetal well-being monitoring.

A novel framework for under-determined blind source separation based on adaptive source counting using mixed linear and circular data clustering algorithm for low latency applications

A novel framework for under-determined blind source separation based on adaptive source counting using mixed linear and circular data clustering algorithm for low latency applications

Speech intelligibility in reverberation based on audio-visual scenes recordings reproduced in a 3D virtual environment

Audio-visual scenes were collected in a medium-sized reverberant conference hall through in-field 3rd-order ambisonics impulse response recordings and 360-degree stereoscopic videos. The visual scenes included cues of the room and the location of the sound sources, without lip-sync-related cues. Speech intelligibility tests based on seven audio-visual scenes were administered inside an immersive virtual 3D environment reproduced through a spherical 16-speaker array synched with a head-mounted display. Forty normal-hearing subjects were engaged to test the effects on speech intelligibility of a talker in front of the listener and amplified by two lateral symmetrical loudspeakers, in the case of (i) different listener-to-talker distances, (ii) one-talker noise at various azimuth angles around the listener, (iii) high reverberation with –5 dB signal-to-noise ratio, (iv) self-motion, and (v) visual cues. We conducted tests in four configurations, that is, audio-visual and audio-only, both with self-motion and in the static condition. The static audio-only tests scored the highest speech intelligibility, followed by a tie between audio-visual with self-motion and in the static condition. Speech intelligibility decreased as the target-to-listener distance increased in all the noisy scenes. Additionally, speech intelligibility increased when the noise azimuth was at 120° compared to both 180° and 0° , with the talker at approximately 8 m from the listener. The advantage of the spatial separation of the noise signal in reverberation is evident in the case of the audio-visual with self-motion test. This suggests a spatial release from masking in the presence of reverberation, one-talker-interfering noise and within an more ecological scene.

VT‐NMR Analysis of Rotation‐Inversion of N‐(4‐hydroxybutyl)‐N‐(2,2,2‐trifluoroethyl) tert‐butyl Carbamate: Utilizing the −CH2CF3 Appendage as a Reporter on E/Z‐Isomerization

AbstractThe rotational barrier about the CN carbamate bond of N‐(4‐hydroxybutyl)‐N‐(2,2,2‐trifluoroethyl) tert‐butyl carbamate 1 was determined by variable temperature (VT) 13C and 19F NMR spectroscopy. The −CH2CF3 appendage reports on rotational isomerism and allows for the observation of separate signals for the E‐ and Z‐ensembles at low temperature. The activation barrier for E/Z‐isomerization was quantified using Eyring‐Polanyi theory which requires the measurements of the maximum difference in Larmor frequency Δνmax and the convergence temperature Tc. Both Δνmax and Tc were interpolated by analyzing sigmoidal functions fitted to data describing signal separation and the quality of the superposition of the E‐ and Z‐signals, respectively. Methods for generating the quality‐of‐fit parameters for Lorentzian line shape analysis are discussed. Our best experimental value for the rotational barrier ΔGc≠(1)=15.65±0.13 kcal/mol is compared to results of a higher level ab initio study of the model N‐ethyl‐N‐(2,2,2‐trifluoroethyl) methyl carbamate.

Enhancing MIMO Antenna Isolation with Novel Electromagnetic Band Gap (EBG) Structure and Genetic Algorithm Optimization

ABSTRACT A novel Electromagnetic Band Gap (EBG) structure is proposed to minimize common connector between closely spaced rectangular patch antennas. The 2D EBG architecture is placed on the horizontal plane of a 1/2 rectangle patch antenna array, reducing mutual coupling by over 20 dB without compromising far-field performance, gain, or bandwidth. By optimizing the EBG structure using a genetic algorithm, noise is reduced by −60 dB compared to −25 dB without it, improving the isolation of the antenna array. Inserting 3 EBG modules between the two patches decreases the 2.45 GHz mutual coupling to −60 dB, showcasing the significant enhancement in isolation. This MIMO antenna design with an EBG structure is suitable for wireless LAN communication systems, offering improved signal separation and performance. The proposed method integrates square patch antennas operating at 24 GHz with a metamaterial EBG structure on a Rogers R04350B substrate, using a genetic algorithm for optimization. Experimental results demonstrate over 22 dB improvement in isolation between antenna elements, maintaining consistent radiation patterns across frequencies.

Purification of Brain Signals Using Various Blind Source Separation Techniques

There arelot of challenges when analyzing the brain signals that did not yet have a basic solution, as there are electrical activities between neurons in the brain,related to all activities in the body. This activity can be seen using a non-surgical technique that is called EEG (i.e. Electroencephalography)such as the appearance of artifactsthrough the registration process, which increases the difficulty of analyzing the signals of the brain, so the technique of blind source separation(BSS) has been used to overcome the problem of artifacts and to separate the main sources (Mixed) without making noise aroundthe original sources.. Therefore, the system for rejecting allartifacts based on the algorithms for separating the blind source has been proposed, bymaking a comparison between four separation algorithms and choosing the best ones according to criteria. After passing a datasetsimulated through thosecriteria, the proposed system can remove the artifacts including Electrocardiogram (ECG), Electrooculogram (EOG)as well as apower line noise interference (LN)) and other EEG mixtures. The proposed method's influenceis checked by two performance indexes Interference to signal ratio (ISR) and (SNR) signal to noise ratio.The results indicated that the BEFICA algorithm is the best and most efficient, as it achieved the highest ratio of VSNR among four separation algorithms due to its developmental advantages.