Abstract In electroencephalography ( EEG ) a valid conductor model of the head ( forward model ) is necessary for predicting measurable scalp voltages from intra-cranial current distributions . All inverse models , capable of inferring the spatial distribution of the neural sources generating measurable electrical and magnetic signals outside the brain are normally formulated in terms of a pre-estimated forward model , which implies considering one ( or more ) current dipole(s ) inside the head and computing the electrical potentials generated at the electrode sites on the scalp surface . Those issues include : EEG preprocessing , checking if the necessary conditions of VAR model applicability are met , optimal order selection , and assessment of the validity of fitted VAR model . Both non-directed ( ordinary coherence and imaginary part of the complex-valued coherency ) and directed ( directed coherence , directed transfer function and partial directed coherence ) measures of the strength of inter-channel coupling are discussed . Then , we introduce the montage to describe and apply the location of scalp electrodes in the context of EEG studies . Further , we describe several preprocessing steps that are commonly used in the EEG preprocessing , including filtering , re-referencing , segmenting , removal of bad channels and trials , as well as decomposition of EEG using independent component analysis . More specifically , appropriate band-pass filtering can effectively reduce superimposed artifacts from various sources which are embedded in the EEG recordings . However , video recordings themselves are also subject to the risk of contamination by artifacts . We report a unique case in which a digital video artifact was identified , occurring during synchronous video-EEG recording , albeit independently of the EEG tracing itself . A synchronous digital video-EEG recording was performed on a 67-year-old male who presented in focal motor status epilepticus . In this paper , we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD . Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs , which characterize the presence of PSWCs in an EEG tracing . The new coefficient , called connection coefficient , and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs . As the electroencephalography ( EEG ) biomedical signals are affected under the presence of the muscular motion artifacts . Presence of these artifacts leads to error in visual analysis of EEG signal , thus results in wrong diagnosis of human diseases . The variants of blind source separation ( BSS ) methods are available . The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform . The analysis focuses on the EEG energy distribution among the well-established delta , theta and alpha rhythms ( 2 - 13 Hz ) , as energy variations in these frequency bands are widely associated with seizure activity . Relying on seizure rhythmicity , the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others . We present a novel and simple technique for the detection , and subsequent de-noising , of these ocular artifacts using Haar wavelets of high orders . A comprehensive error analysis has been carried out , in both the time domain based artifact detection and the frequency domain based SWT de-noising of EEG . This procedure also has the advantage of being highly artifact selective , and so we have applied it to detect and de-noise epileptic EEG signals . Wavelet Based Classification Approach for EEG Diagnoses ، للحصول على النص الكامل يرجى زيارة مكتبة الحسين بن طلال في جامعة اليرموك او زيارة موقعها الالكتروني In this study , electroencephalogram ( EEG ) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer 's disease ( AD ) and 14 age-matched Controls ( CN ) - were analyzed using Discrete Wavelet Transform ( DWT ) . The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks , since AD is characterized by progressive impairments in cognition and memory . At each recording block , DWT extracts EEG features corresponding to major brain frequency bands . A hybrid system for automated EEG sleep staging is presented in this article . By combining a self-organizing feature map ( SOFM ) with a fuzzy reasoning-based classifier ( FRBC ) and utilizing both temporal and spectrum features of the EEG signal , the system provides a reliable tool for automatic EEG sleep staging . Conceptually , the system is divided into four passes : artifact detection , rough staging , stage refinement and post processing . Besides , even if features are chaotic , ML techniques might classify them in such a manner that an element in a particular class becomes easy to generate . In this paper , we perform entropy analysis on common features used in EEG-based security systems to estimate their information content , which is used to propose a novel technique for EEG signal generation in feature domain instead of time domain . These generated signals can potentially be used for spoofing attacks . D is related to Hurst parameter ( H ) , which is a measure of self-similarity . Hence , it is important to estimate H for accurate noise prediction using fractal theory . H parameter estimators assume stationarity in time series data . The FW – Hpds method rapidly showed promise in the studywork of a few researchers [ 7–10 ] when it was used for predicting the noise generated by helicopter rotors in hover and forward flight . More recently , Farassat and Casper [ 11 ] emphasized the role of analytical methods in computational aeroacoustics and recommended FW – Hpds as a very promising method for noise prediction of a complicated flowfield . To predict nonlinear noise generated by transonic rotors in hover , three-dimensional Euler equations were commonly used to consider the nonlinear effect related to shockwaves . Linear relationship between the sea noise spectrum levels and the wind speed were found for the entire frequency range , but the slope were frequency dependent . The results of empirical fitting based on the analysis were used for noise level prediction and the model predictions were compared with the measured noise level . Such study post extensive validation has the potential to develop predictive models for ambient noise estimation based on recorded data . An efficient procedure for accurate noise parameter prediction of microwave MESFETs / HEMTs for various bias conditions is proposed in this paper . It is based on an improved Pospieszalski ’s noise model . In this paper , we present a new performance bound for uplink channel estimation ( CE ) accuracy in the Massive Multiple Input Multiple Output ( MIMO ) system . The proposed approach is based on noise power prediction after the CE unit . Our method outperforms the accuracy of a well-known Cramer-Rao lower bound ( CRLB ) due to considering more statistics since performance strongly depends on a number of channel taps and power ratio between them . The question is what is a standard deviation between forecast and noise measurement . Normally for noise prediction analysis the worst case scenario is taken into account , so one can expect that real on-board noise levels will be lower then forecasted . However , when weight limits are taken into account in standard work case scenario the real on-board noise levels are more or less the same as forecasted . In this work , we investigate methods for the prediction and suppression of the ego-motion noise . In the first part , we analyze the performance of different noise subtraction strategies , assuming that the noise prediction problem has been solved . In the second part , we present some results for a noise prediction scheme based on the current robot joint status . We propose a novel bi-directional pattern-dependent noise prediction ( BiPDNP ) detector to improve the performance of the HAMR channel under high jitter noise conditions . The BiPDNP detector utilizes backward linear prediction in the noise prediction process , as well as the conventional forward linear prediction . At BER of 10−3 , simulation results show that BiPDNP outperforms conventional PDNP by 0.5 - 1.5 dB in HAMR channels . The model introduced is reflecting the noise characteristics of the instrument 's analog image data channel statistically so that the analog channel itself is not modeled explicitly . Nevertheless a precise noise strength prediction can be achieved . The prediction results depend both on the specific shutter open/close sequence used and a system specific spatial-temporal autocovariance function which can be easily estimated from simple image data cubes . Electrocardiograph ( ECG ) denoising is the most important step in diagnosis of heart-related diseases , as the diagnosis gets influenced with noises . In this paper , a new method for ECG denoising is proposed , which incorporates empirical mode decomposition algorithm with Riegmann Liouvelle ( RL ) fractional integral filtering and Savitzky – Golay ( SG ) filtering . In the proposed method , noisy ECG signal is decomposed into its intrinsic mode functions ( IMFs ) , from which noisy IMFs , corrupted with high-frequency ( HF ) and low-frequency ( LF ) noises , are identified by proposed noisy-IMFs identification methodologies . Recently , many researchers showed that the brain electrical activity or electroencephalogram ( EEG ) signals could provide robust and unique features that can be considered as a new biometric authentication technique , given that accurately methods to decompose the signals must also be considered . This paper proposes a novel method for EEG signal denoising based on the multi-objective Flower Pollination Algorithm and the Wavelet Transform ( MOFPA-WT ) to extract useful features from denoised signals . MOFPA-WT is tested using a standard EEG signal dataset , namely , EEG motor movement/imagery dataset , and its performance is evaluated using three criteria : ( i ) accuracy , ( ii ) true acceptance rate , and ( iii ) false acceptance rate . Conclusion . In conclusion , the results show that the proposed methods for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy . Therefore , using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method . The communication based on EEG between human brain and computer is a new modality of human — computer interaction . Through time-domain regression method for EEG denoising pretreatment , AR model coefficient is extracted as feature vector , classifies the mental tasks based on BP network and PNN network . A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions ( IMFs ) . Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising . Similar to other decomposition-based denoising approaches , EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components . Empirical mode decomposition(EMD ) has been proved that it is an extraordinary useful nonparametric signal denoising , which adaptively breaks down any signal into oscillatory components called intrinsic mode functions(IMFs ) . For EMD-based denoising , how to select relevant extracted modes and how to select threshold for denoising are two main questions to resolve urgently . This paper introduces a new EMD-based denoising , tremed EMD-corIT , which combines an estimation of correlation coefficient and the EMD interval thresholding denoising ( EMD-IT ) . In this paper , an effective optimization scheme for ECG signal denoising is presented based on low-rank matrix decomposition . First , the ECG denoising problem is formulated as low-rank matrix decomposition . So , an ECG beats matrix is assumed to be the combination of a sparse noise matrix and a low-rank matrix . The EEG data is inherently complex and difficult to analyze due to interference by eye and muscle movements as well as electrical grid noise . In this paper we analyze shrinkage functions for signal filtering and propose a class-adaptive method for EEG data denoising . The results are evaluated using a Support Vector Machine . The eigenvector for classification is obtained by combining the effective features from different channels . The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition , the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction . document : FFDNet : Toward a Fast and Flexible Solution for CNN based Image Denoising Due to the fast inference and good performance , discriminative learning methods have been widely studied in image denoising . However , these methods mostly learn a specific model for each noise level , and require multiple models for denoising images with different noise levels . Motivated by this target , in this paper , we propose a method for large-scale 3D reconstruction from consecutive monocular images . First , with the correlation of underlying information between depth and semantic prediction , a novel multi-task Convolutional Neural Network ( CNN ) is designed for joint prediction . Given a single image , the network learns low-level information with a shared encoder and separately predicts with decoders containing additional Atrous Spatial Pyramid Pooling ( ASPP ) layers and the residual connection which merits disparities and semantic mutually . We first learn knowledge sharing for exploring distinctive local semantic information based on latent semantic analysis and label ranking . Then , we employ the latent structural constraints of scene configuration for the semantic prediction . Finally , we infer the scene semantic using Bayes rules . Our three experiments are the following : 1 . , we incorporate DErivBase into distributional semantic models to overcome sparsity problems and to improve the prediction quality of the underlying model . We test this method , which we call derivational smoothing , for semantic similarity prediction , and for synonym choice . 2 . , we employ DErivBase to model a psycholinguistic experiment that examines priming effects of transparent and opaque derivations to draw conclusions about the mental lexical representation in German . These models typically treat each word in a sentence equally , which ignores the fact that keywords always play a leading role in expressing the sentence semantic . Especially , on the tasks of sentence classification and semantic relatedness prediction , we can judge through the information of several keywords , no need for the whole sentence . To this end , we propose a window-based intra-weighing approach to weigh words in the sentence . We propose Untied Long Short Term Memory ( ULSTM ) as a solution to the above problem . We test our model on various tasks , including semantic relatedness prediction , language modeling and sentiment classification . Experimental results indicate that our proposed model is capable to at least partially solve the problem and outperform LSTM for all these tasks . In the framework , we effectively model the intrinsic correlations among different types of knowledge relations within a max-margin multi-relational ranking scheme , which jointly optimizes the tasks of entity embedding and cross-entity relation prediction in terms of multi-relational structures of the knowledge graph . Specifically , we devise a bilinear scoring function that aims to evaluate the confidence degree of semantic relation prediction for entity pairs through a multi-relational learning-to-rank pipeline . In essence , the pipeline formulates the problem of relation prediction for entity pairs as that of learning relation-specific ranking functions by max-margin optimization . Automatic identification of synonym/hyponym relations is useful for many NLP applications , e.g. knowledge base completion and ontology construction . 2 Challenges The relation prediction task of ScienceIE is challenging and quite different from other semantic relation prediction task like SemEval-2010 Task 8 ( Hendrickx et al. , 2009 ) . In SemEval-2010 Task 8 , there are two marked nominals in a sentence and the task is to predict if any of nine semantic relations hold between the nominal pair . These knowledge sources are integrated with sophisticated signal processing and acoustic-phonetic analysis of the input signal , to produce a total system for understanding continuous speech . The system contains components for signal analysis , acoustic parameter extraction , acoustic-phonetic analysis of the signal , phonological expansion of the lexicon , lexical matching and retrieval , syntactic analysis and prediction , semantic analysis and prediction , pragmatic evaluation and prediction , and inferential fact retrieval and question answering , as well as synthesized text or spoken output . In order to improve multi-label prediction performance , in this paper , we firstly propose a multinomial label distribution to estimate the importance degree of each associated label for an instance by using conditional probability , and then establish a deep neural network ( DNN ) based model which contains both multinomial label distribution and label co-occurrence information to realize the multi-label classification of semantic information in fMRI brain signals . Experiments on three fMRI recording datasets demonstrate that our approach performs better than the state-of-the-art methods on semantic information prediction . Then , we learn deep depth features from the estimated depth and combine with RGB features for object detection and semantic segmentation . In addition , we propose an RGB-D semantic segmentation method , which applies a multi-task training scheme : semantic label prediction and depth value regression . We test our methods on several data sets and demonstrate that incorporating information from estimated depth improves the performance of object detection and semantic segmentation remarkably . We use this method , " Predict To Learn " ( P2L ) , to predict the most likely " source " dataset to produce effective transfer for training on a " target " dataset . We validate our approach extensively across 21 tasks , including image classification tasks and semantic relationship prediction tasks in the linguistic domain . The P2L approach selects the best transfer learning model on 62 % of the tasks , compared with a baseline of 48 % of cases when using a heuristic of selecting the largest source dataset and 52 % of cases when using a distance measure between source and target datasets . Qualitatively , the primary function of these short - range CRFs has been to clean up the spurious predictions of weak classifiers built on top of local hand - engineered features . Compared to these weaker classifiers , modern DCNN architectures such as the one we use in this work produce score maps and semantic label predictions which are qualitatively different . As illustrated in Figure [ reference ] , the score maps are typically quite smooth and produce homogeneous classification results . section : Discussion Our work combines ideas from deep convolutional neural networks and fully - connected conditional random fields , yielding a novel method able to produce semantically accurate predictions and detailed segmentation maps , while being computationally efficient . Our experimental results show that the proposed method significantly advances the state - of - art in the challenging PASCAL VOC 2012 semantic image segmentation task . We now present each component in details . subsection : Semantic Saliency Prediction Large modality and interaction variations are the main challenge to multi - human parsing and also the key obstacle to learning a well - performing human - centric analysis model . The attention prediction task finds direct application in the listening , monitoring and engaging activities of the businesses that have such brand-pages . In this paper , we formulate the problem of attention prediction on social media brand pages . We further propose Attention Prediction ( AP ) framework which integrates the various features that influence the attention received by a post using classification and regression based approaches . Videos are segmented into shorter clips attending to the audience ’s increasing or decreasing attention , and we process videos ’ audio waveform to extract meaningful aural embeddings from a VGGish model pretrained on the Audioset database . While previous similar work on attention level prediction using only audio accomplished 69.83 % accuracy , we propose a Mixture of Experts approach to train a binary classifier that outperforms the main existing state-of-the-art approaches predicting increasing and decreasing attention levels with 81.76 % accuracy . These results confirm the usefulness of providing acoustic features with a semantic significance , and the convenience of considering experts over partitions of the dataset in order to predict group-level attention from audio . While it is easy to mount the sensors in the forehead area , which is not covered with hair , the recorded signals become greatly contaminated with eyeblink and movement artifacts , which makes it a challenge to acquire the data of suitable for analysis quality , particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms . In this article , we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis , it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor . Fifteen healthy 22 - 28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment . Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Unsurprisingly , multi - scale features have been widely used in both conventional feature design and deep learning . Without residual connections , the model fails to utilize the increase in modeling power from additional layers , and performance drops significantly for deeper encoders ( Figure 2b ) . Our convolutional architecture relies on two sets of networks , CNN - a for attention score computation a i and CNN - c for the conditional input This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children . The methods of analysis utilized were signal-processing ( EEG artifact removal , Laplacian filtering , and magnitude square coherence measurement ) and the characterization ( Graph metrics ) and classification ( Decision Tree ) of EEG signals recorded during performance of a numerical comparison task . Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement . ICA is a statistical method by which corrupted EEG signal is decomposed and artifacts in it can be detected . This paper illustrates the ICA and fast ICA technique for EEG artifact detection and removal . Before applying ICA , preprocessing of the EEG data is done using Centering and Sphering . Disclosed herein is a method ( 1000 ) and a system ( 25 ) for EEG artifact removal of a user interface . The present invention allows the operator to use the user interface to select a plurality of artifacts from EEG recorded automatically removed . The use of the widely linear adaptive predictor is justified by the relationship between the mean square prediction error and the crosscorrelation and cross-pseudocorrelations of the source signals . Simulations on benchmark improper quaternion sources together with a real-world example of EEG artifact removal illustrate the usefulness of the proposed methodology .
Abstract In electroencephalography ( EEG ) a valid conductor model of the head ( forward model ) is necessary for predicting measurable scalp voltages from intra-cranial current distributions . All inverse models , capable of inferring the spatial distribution of the neural sources generating measurable electrical and magnetic signals outside the brain are normally formulated in terms of a pre-estimated forward model , which implies considering one ( or more ) current dipole(s ) inside the head and computing the electrical potentials generated at the electrode sites on the scalp surface .
Those issues include : EEG preprocessing , checking if the necessary conditions of VAR model applicability are met , optimal order selection , and assessment of the validity of fitted VAR model . Both non-directed ( ordinary coherence and imaginary part of the complex-valued coherency ) and directed ( directed coherence , directed transfer function and partial directed coherence ) measures of the strength of inter-channel coupling are discussed .
Then , we introduce the montage to describe and apply the location of scalp electrodes in the context of EEG studies . Further , we describe several preprocessing steps that are commonly used in the EEG preprocessing , including filtering , re-referencing , segmenting , removal of bad channels and trials , as well as decomposition of EEG using independent component analysis . More specifically , appropriate band-pass filtering can effectively reduce superimposed artifacts from various sources which are embedded in the EEG recordings .
However , video recordings themselves are also subject to the risk of contamination by artifacts . We report a unique case in which a digital video artifact was identified , occurring during synchronous video-EEG recording , albeit independently of the EEG tracing itself . A synchronous digital video-EEG recording was performed on a 67-year-old male who presented in focal motor status epilepticus .
In this paper , we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD . Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs , which characterize the presence of PSWCs in an EEG tracing . The new coefficient , called connection coefficient , and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs .
As the electroencephalography ( EEG ) biomedical signals are affected under the presence of the muscular motion artifacts . Presence of these artifacts leads to error in visual analysis of EEG signal , thus results in wrong diagnosis of human diseases . The variants of blind source separation ( BSS ) methods are available .
The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform . The analysis focuses on the EEG energy distribution among the well-established delta , theta and alpha rhythms ( 2 - 13 Hz ) , as energy variations in these frequency bands are widely associated with seizure activity . Relying on seizure rhythmicity , the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others .
We present a novel and simple technique for the detection , and subsequent de-noising , of these ocular artifacts using Haar wavelets of high orders . A comprehensive error analysis has been carried out , in both the time domain based artifact detection and the frequency domain based SWT de-noising of EEG . This procedure also has the advantage of being highly artifact selective , and so we have applied it to detect and de-noise epileptic EEG signals .
Wavelet Based Classification Approach for EEG Diagnoses ، للحصول على النص الكامل يرجى زيارة مكتبة الحسين بن طلال في جامعة اليرموك او زيارة موقعها الالكتروني
In this study , electroencephalogram ( EEG ) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer 's disease ( AD ) and 14 age-matched Controls ( CN ) - were analyzed using Discrete Wavelet Transform ( DWT ) . The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks , since AD is characterized by progressive impairments in cognition and memory . At each recording block , DWT extracts EEG features corresponding to major brain frequency bands .
A hybrid system for automated EEG sleep staging is presented in this article . By combining a self-organizing feature map ( SOFM ) with a fuzzy reasoning-based classifier ( FRBC ) and utilizing both temporal and spectrum features of the EEG signal , the system provides a reliable tool for automatic EEG sleep staging . Conceptually , the system is divided into four passes : artifact detection , rough staging , stage refinement and post processing .
Besides , even if features are chaotic , ML techniques might classify them in such a manner that an element in a particular class becomes easy to generate . In this paper , we perform entropy analysis on common features used in EEG-based security systems to estimate their information content , which is used to propose a novel technique for EEG signal generation in feature domain instead of time domain . These generated signals can potentially be used for spoofing attacks .
D is related to Hurst parameter ( H ) , which is a measure of self-similarity . Hence , it is important to estimate H for accurate noise prediction using fractal theory . H parameter estimators assume stationarity in time series data .
The FW – Hpds method rapidly showed promise in the studywork of a few researchers [ 7–10 ] when it was used for predicting the noise generated by helicopter rotors in hover and forward flight . More recently , Farassat and Casper [ 11 ] emphasized the role of analytical methods in computational aeroacoustics and recommended FW – Hpds as a very promising method for noise prediction of a complicated flowfield . To predict nonlinear noise generated by transonic rotors in hover , three-dimensional Euler equations were commonly used to consider the nonlinear effect related to shockwaves .
Linear relationship between the sea noise spectrum levels and the wind speed were found for the entire frequency range , but the slope were frequency dependent . The results of empirical fitting based on the analysis were used for noise level prediction and the model predictions were compared with the measured noise level . Such study post extensive validation has the potential to develop predictive models for ambient noise estimation based on recorded data .
An efficient procedure for accurate noise parameter prediction of microwave MESFETs / HEMTs for various bias conditions is proposed in this paper . It is based on an improved Pospieszalski ’s noise model .
In this paper , we present a new performance bound for uplink channel estimation ( CE ) accuracy in the Massive Multiple Input Multiple Output ( MIMO ) system . The proposed approach is based on noise power prediction after the CE unit . Our method outperforms the accuracy of a well-known Cramer-Rao lower bound ( CRLB ) due to considering more statistics since performance strongly depends on a number of channel taps and power ratio between them .
The question is what is a standard deviation between forecast and noise measurement . Normally for noise prediction analysis the worst case scenario is taken into account , so one can expect that real on-board noise levels will be lower then forecasted . However , when weight limits are taken into account in standard work case scenario the real on-board noise levels are more or less the same as forecasted .
In this work , we investigate methods for the prediction and suppression of the ego-motion noise . In the first part , we analyze the performance of different noise subtraction strategies , assuming that the noise prediction problem has been solved . In the second part , we present some results for a noise prediction scheme based on the current robot joint status .
We propose a novel bi-directional pattern-dependent noise prediction ( BiPDNP ) detector to improve the performance of the HAMR channel under high jitter noise conditions . The BiPDNP detector utilizes backward linear prediction in the noise prediction process , as well as the conventional forward linear prediction . At BER of 10−3 , simulation results show that BiPDNP outperforms conventional PDNP by 0.5 - 1.5 dB in HAMR channels .
The model introduced is reflecting the noise characteristics of the instrument 's analog image data channel statistically so that the analog channel itself is not modeled explicitly . Nevertheless a precise noise strength prediction can be achieved . The prediction results depend both on the specific shutter open/close sequence used and a system specific spatial-temporal autocovariance function which can be easily estimated from simple image data cubes .
Electrocardiograph ( ECG ) denoising is the most important step in diagnosis of heart-related diseases , as the diagnosis gets influenced with noises . In this paper , a new method for ECG denoising is proposed , which incorporates empirical mode decomposition algorithm with Riegmann Liouvelle ( RL ) fractional integral filtering and Savitzky – Golay ( SG ) filtering . In the proposed method , noisy ECG signal is decomposed into its intrinsic mode functions ( IMFs ) , from which noisy IMFs , corrupted with high-frequency ( HF ) and low-frequency ( LF ) noises , are identified by proposed noisy-IMFs identification methodologies .
Recently , many researchers showed that the brain electrical activity or electroencephalogram ( EEG ) signals could provide robust and unique features that can be considered as a new biometric authentication technique , given that accurately methods to decompose the signals must also be considered . This paper proposes a novel method for EEG signal denoising based on the multi-objective Flower Pollination Algorithm and the Wavelet Transform ( MOFPA-WT ) to extract useful features from denoised signals . MOFPA-WT is tested using a standard EEG signal dataset , namely , EEG motor movement/imagery dataset , and its performance is evaluated using three criteria : ( i ) accuracy , ( ii ) true acceptance rate , and ( iii ) false acceptance rate .
Conclusion . In conclusion , the results show that the proposed methods for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy . Therefore , using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method .
The communication based on EEG between human brain and computer is a new modality of human — computer interaction . Through time-domain regression method for EEG denoising pretreatment , AR model coefficient is extracted as feature vector , classifies the mental tasks based on BP network and PNN network .
A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions ( IMFs ) . Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising . Similar to other decomposition-based denoising approaches , EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components .
Empirical mode decomposition(EMD ) has been proved that it is an extraordinary useful nonparametric signal denoising , which adaptively breaks down any signal into oscillatory components called intrinsic mode functions(IMFs ) . For EMD-based denoising , how to select relevant extracted modes and how to select threshold for denoising are two main questions to resolve urgently . This paper introduces a new EMD-based denoising , tremed EMD-corIT , which combines an estimation of correlation coefficient and the EMD interval thresholding denoising ( EMD-IT ) .
In this paper , an effective optimization scheme for ECG signal denoising is presented based on low-rank matrix decomposition . First , the ECG denoising problem is formulated as low-rank matrix decomposition . So , an ECG beats matrix is assumed to be the combination of a sparse noise matrix and a low-rank matrix .
The EEG data is inherently complex and difficult to analyze due to interference by eye and muscle movements as well as electrical grid noise . In this paper we analyze shrinkage functions for signal filtering and propose a class-adaptive method for EEG data denoising . The results are evaluated using a Support Vector Machine .
The eigenvector for classification is obtained by combining the effective features from different channels . The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition , the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction .
document : FFDNet : Toward a Fast and Flexible Solution for CNN based Image Denoising Due to the fast inference and good performance , discriminative learning methods have been widely studied in image denoising . However , these methods mostly learn a specific model for each noise level , and require multiple models for denoising images with different noise levels .
Motivated by this target , in this paper , we propose a method for large-scale 3D reconstruction from consecutive monocular images . First , with the correlation of underlying information between depth and semantic prediction , a novel multi-task Convolutional Neural Network ( CNN ) is designed for joint prediction . Given a single image , the network learns low-level information with a shared encoder and separately predicts with decoders containing additional Atrous Spatial Pyramid Pooling ( ASPP ) layers and the residual connection which merits disparities and semantic mutually .
We first learn knowledge sharing for exploring distinctive local semantic information based on latent semantic analysis and label ranking . Then , we employ the latent structural constraints of scene configuration for the semantic prediction . Finally , we infer the scene semantic using Bayes rules .
Our three experiments are the following : 1 . , we incorporate DErivBase into distributional semantic models to overcome sparsity problems and to improve the prediction quality of the underlying model . We test this method , which we call derivational smoothing , for semantic similarity prediction , and for synonym choice . 2 . , we employ DErivBase to model a psycholinguistic experiment that examines priming effects of transparent and opaque derivations to draw conclusions about the mental lexical representation in German .
These models typically treat each word in a sentence equally , which ignores the fact that keywords always play a leading role in expressing the sentence semantic . Especially , on the tasks of sentence classification and semantic relatedness prediction , we can judge through the information of several keywords , no need for the whole sentence . To this end , we propose a window-based intra-weighing approach to weigh words in the sentence .
We propose Untied Long Short Term Memory ( ULSTM ) as a solution to the above problem . We test our model on various tasks , including semantic relatedness prediction , language modeling and sentiment classification . Experimental results indicate that our proposed model is capable to at least partially solve the problem and outperform LSTM for all these tasks .
In the framework , we effectively model the intrinsic correlations among different types of knowledge relations within a max-margin multi-relational ranking scheme , which jointly optimizes the tasks of entity embedding and cross-entity relation prediction in terms of multi-relational structures of the knowledge graph . Specifically , we devise a bilinear scoring function that aims to evaluate the confidence degree of semantic relation prediction for entity pairs through a multi-relational learning-to-rank pipeline . In essence , the pipeline formulates the problem of relation prediction for entity pairs as that of learning relation-specific ranking functions by max-margin optimization .
Automatic identification of synonym/hyponym relations is useful for many NLP applications , e.g. knowledge base completion and ontology construction . 2 Challenges The relation prediction task of ScienceIE is challenging and quite different from other semantic relation prediction task like SemEval-2010 Task 8 ( Hendrickx et al. , 2009 ) . In SemEval-2010 Task 8 , there are two marked nominals in a sentence and the task is to predict if any of nine semantic relations hold between the nominal pair .
These knowledge sources are integrated with sophisticated signal processing and acoustic-phonetic analysis of the input signal , to produce a total system for understanding continuous speech . The system contains components for signal analysis , acoustic parameter extraction , acoustic-phonetic analysis of the signal , phonological expansion of the lexicon , lexical matching and retrieval , syntactic analysis and prediction , semantic analysis and prediction , pragmatic evaluation and prediction , and inferential fact retrieval and question answering , as well as synthesized text or spoken output .
In order to improve multi-label prediction performance , in this paper , we firstly propose a multinomial label distribution to estimate the importance degree of each associated label for an instance by using conditional probability , and then establish a deep neural network ( DNN ) based model which contains both multinomial label distribution and label co-occurrence information to realize the multi-label classification of semantic information in fMRI brain signals . Experiments on three fMRI recording datasets demonstrate that our approach performs better than the state-of-the-art methods on semantic information prediction .
Then , we learn deep depth features from the estimated depth and combine with RGB features for object detection and semantic segmentation . In addition , we propose an RGB-D semantic segmentation method , which applies a multi-task training scheme : semantic label prediction and depth value regression . We test our methods on several data sets and demonstrate that incorporating information from estimated depth improves the performance of object detection and semantic segmentation remarkably .
We use this method , " Predict To Learn " ( P2L ) , to predict the most likely " source " dataset to produce effective transfer for training on a " target " dataset . We validate our approach extensively across 21 tasks , including image classification tasks and semantic relationship prediction tasks in the linguistic domain . The P2L approach selects the best transfer learning model on 62 % of the tasks , compared with a baseline of 48 % of cases when using a heuristic of selecting the largest source dataset and 52 % of cases when using a distance measure between source and target datasets .
Qualitatively , the primary function of these short - range CRFs has been to clean up the spurious predictions of weak classifiers built on top of local hand - engineered features . Compared to these weaker classifiers , modern DCNN architectures such as the one we use in this work produce score maps and semantic label predictions which are qualitatively different . As illustrated in Figure [ reference ] , the score maps are typically quite smooth and produce homogeneous classification results .
section : Discussion Our work combines ideas from deep convolutional neural networks and fully - connected conditional random fields , yielding a novel method able to produce semantically accurate predictions and detailed segmentation maps , while being computationally efficient . Our experimental results show that the proposed method significantly advances the state - of - art in the challenging PASCAL VOC 2012 semantic image segmentation task .
We now present each component in details . subsection : Semantic Saliency Prediction Large modality and interaction variations are the main challenge to multi - human parsing and also the key obstacle to learning a well - performing human - centric analysis model .
The attention prediction task finds direct application in the listening , monitoring and engaging activities of the businesses that have such brand-pages . In this paper , we formulate the problem of attention prediction on social media brand pages . We further propose Attention Prediction ( AP ) framework which integrates the various features that influence the attention received by a post using classification and regression based approaches .
Videos are segmented into shorter clips attending to the audience ’s increasing or decreasing attention , and we process videos ’ audio waveform to extract meaningful aural embeddings from a VGGish model pretrained on the Audioset database . While previous similar work on attention level prediction using only audio accomplished 69.83 % accuracy , we propose a Mixture of Experts approach to train a binary classifier that outperforms the main existing state-of-the-art approaches predicting increasing and decreasing attention levels with 81.76 % accuracy . These results confirm the usefulness of providing acoustic features with a semantic significance , and the convenience of considering experts over partitions of the dataset in order to predict group-level attention from audio .
While it is easy to mount the sensors in the forehead area , which is not covered with hair , the recorded signals become greatly contaminated with eyeblink and movement artifacts , which makes it a challenge to acquire the data of suitable for analysis quality , particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms . In this article , we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis , it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor . Fifteen healthy 22 - 28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment .
Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Unsurprisingly , multi - scale features have been widely used in both conventional feature design and deep learning .
Without residual connections , the model fails to utilize the increase in modeling power from additional layers , and performance drops significantly for deeper encoders ( Figure 2b ) . Our convolutional architecture relies on two sets of networks , CNN - a for attention score computation a i and CNN - c for the conditional input
This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children . The methods of analysis utilized were signal-processing ( EEG artifact removal , Laplacian filtering , and magnitude square coherence measurement ) and the characterization ( Graph metrics ) and classification ( Decision Tree ) of EEG signals recorded during performance of a numerical comparison task . Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement .
ICA is a statistical method by which corrupted EEG signal is decomposed and artifacts in it can be detected . This paper illustrates the ICA and fast ICA technique for EEG artifact detection and removal . Before applying ICA , preprocessing of the EEG data is done using Centering and Sphering .
Disclosed herein is a method ( 1000 ) and a system ( 25 ) for EEG artifact removal of a user interface . The present invention allows the operator to use the user interface to select a plurality of artifacts from EEG recorded automatically removed .
The use of the widely linear adaptive predictor is justified by the relationship between the mean square prediction error and the crosscorrelation and cross-pseudocorrelations of the source signals . Simulations on benchmark improper quaternion sources together with a real-world example of EEG artifact removal illustrate the usefulness of the proposed methodology .
Comparison between realistic and spherical approaches in EEG forward modelling [Study of cortico-cortical functional connectivity with vector autoregressive model of multichannel EEG]. EEG Preprocessing and Denoising Mirror Image Video Artifact: An Under-Reported Digital Video-EEG Artifact A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals A novel wavelet based technique for detection and de-noising of ocular artifact in normal and epileptic electroencephalogram Wavelet Based Classification Approach for EEG Diagnoses Discrete wavelet transform EEG features of Alzheimer'S disease in activated states Automated Sleep Staging by a Hybrid System Comprising Neural Network and Fuzzy Rule-based Reasoning A novel spoofing attack against electroencephalogram-based security systems Prediction of long-range dependence in cyclostationary noise in low-voltage PLC networks Prediction of Hovering Rotor Noise Based on Reynolds-Averaged Navier-Stokes Simulation Ambient Noise Analysis and Modeling in Shallow water of Arabian Sea Artificial neural networks in bias dependant noise modeling of MESFETs Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO STATISTICAL ANALYSIS OF A NOISE MEASUREMENTS FOR THE SERIES OF RORO SHIPS Ego noise suppression of a robot using template subtraction Signal Processing for Bit-Patterned Media and Heat-Assisted Magnetic Recording Systems with Media Noise MERTIS: configuration of measurement sequences for a maximized image SNR Riemann Liouvelle Fractional Integral Based Empirical Mode Decomposition for ECG Denoising EEG-based Person Authentication Using Multi-objective Flower Pollination Algorithm EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods Classification for Different Mental Tasks Based on EEG Signals Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition EMD interval thresholding denoising based on correlation coefficient to select relevant modes An Effective Optimization Scheme for ECG Signal Denoising via Low-Rank Matrix Decomposition Class-Adaptive Denoising for EEG Data Classification EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising Monocular Outdoor Semantic Mapping with a Multi-task Network Scene classification based on knowledge sharing and latent structural constraints Induction, Semantic Validation and Evaluation of a Derivational Morphology Lexicon for German A Window-Based Self-Attention approach for sentence encoding On Finer Control of Information Flow in LSTMs A Bilinear Ranking SVM for Knowledge Based Relation Prediction and Classification NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents Speech Understanding Systems. Volume I. Introduction and Overview. Multi-label Semantic Decoding from Human Brain Activity Exploiting Depth From Single Monocular Images for Object Detection and Semantic Segmentation P2L: Predicting Transfer Learning for Images and Semantic Relations Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing Attention prediction on social media brand pages Predicting Group-Level Skin Attention to Short Movies from Audio-Based LSTM-Mixture of Experts Models Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task. Res2Net: A New Multi-scale Backbone Architecture A Convolutional Encoder Model for Neural Machine Translation A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics Efficient Removal of Artifacts from EEG recordings by ICA and Fast ICA Techniques Method and system for displaying data, and user interface eeg Blind extraction of improper quaternion sources
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BIBE EMBC SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI IROS CEC ICEE ICAISC (2) IEEE Transactions on Image Processing ECML/PKDD (1) SemEval@ACL ICPR ICLR ArXiv CIKM INTERSPEECH IEEE transactions on pattern analysis and machine intelligence ACL ICASSP
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