Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

ICA-derived cortical responses indexing rapid multi-feature auditory processing in six-month-old infants

A fully automatic ocular artifact suppression from EEG data using higher order statistics: Improved performance by wavelet analysis

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process

Objective: The activity of the brain can be recorded by means of an electroencephalogram (EEG). An EEG is a multichannel signal related to brain activity. However, EEG presents a wide variety of undesired artefacts. Removal of these artefacts is often done using blind source separation methods (BSS) and mainly those based on Independent Component Analysis (ICA). ICA-based methods are well-accepted in the literature for filtering artefacts and have proved to be satisfactory in most scenarios of interest. Our goal is to develop a generic and unsupervised ICA-based algorithm for EEG artefacts removal. Approach: The proposed algorithm makes use of a new unsupervised artefact detection, ICA and a statistical criterion to automatically select the artefact related independent components (ICs) requiring no human intervention. The algorithm is evaluated using both simulated and real EEG data with artefacts (SEEG and AEEG). A comparison between the proposed unsupervised selection of ICs related to the artefact and other supervised selection is also presented. Main results: A new unsupervised ICA-based algorithm to filter artefacts, where ICs related to each artefact are automatically selected. It can be used in online applications, it preserves most of the original information among the artefacts and removes different types of artefacts. Significance: ICA-based methods for filtering artefacts prevail in the literature. The work in this article is important insofar as it addresses the problem of automatic selection of ICs in ICA-based methods. The selection is unsupervised, avoiding the manual ICs selection or a learning process involved in other methods. Our method is a generic algorithm that allows removing EEG artefacts of various types and, unlike some ICA-based algorithms, it retains most of the original information among the artefacts. Within the algorithm, the artefact detection method implemented does not require human intervention either.

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

Electroencephalography (EEG) has been used extensively for classifying cognitive tasks. Many investigators have demonstrated classification accuracies well over 90% for some combinations of cognitive tasks, signal transformations, and classification methods. Unfortunately, EEG data is prone to significant interference from a wide variety of artifacts, particularly eye blinks. Most methods for classifying cognitive tasks with EEG data simply discard time windows containing eye blink artifacts. However, future applications of EEG-based cognitive task classification should not be hindered by eye blinks. The value of an EEG-controlled human-computer interface, for instance, would be severely diluted if it did not work in the presence of eye blinks. Fortunately, recent advances in blind signal separation algorithms and their applications to EEG data mitigate the artifact contamination issue. In this paper, we show how independent components analysis (ICA) and its extension for sub-Gaussian sources, extended ICA (eICA), can be applied to accurately classify cognitive tasks with eye blink contaminated EEG recordings.

Simultaneous head tissue conductivity and EEG source location estimation

<p>The goal of this work is to improve the efficiency and the reliability of the automatic artifact rejection, in particular from the Electroencephalographic (EEG) recordings. Artifact rejection is a key topic in signal processing. The artifacts are unwelcome signals that may occur during the signal acquisition and that may alter the analysis of the signals themselves. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we enhance this technique introducing the Renyi-s entropy. The performance of our method was tested exploiting the Independent Component scalp maps and it was compared to the performance of the method in literature and it showed to outperform it.</p>

The electroencephalogram (EEG) is a practical and reasonably applied tool for researching brain disorders and behavior changes. EEG offers a minimally restricted and non-invasive method, where the significant difficulties in utilizing EEG in studies on cognitive development are in the temporal resolution, the outbound signal sources, and the EEG Artifacts. Making an informed judgment about the application of EEG technology requires careful consideration of these factors. Independent component analysis (ICA) has been demonstrated to isolate the various source generator processes underlying simultaneously recorded signals from multiple, near-adjacent EEG scalp electrode channels. ICs generated by ICA decomposition must be manually inspected, chosen, and interpreted, but this process takes time and experience. Automated IC category classification of ICs can be achieved sufficiently accurately, which expedites the analysis of large-scale EEG research and permits the use of ICA decomposition in near-real-time applications. At the same time, for many such classifiers, the next step of Brain activity rejection is necessary for medical specialists. Thus, this work presents an automated convolution neural network-based brain activity labeling for ICA rejection using the data from the well-used and widely utilized by neurologists and Scientists such as ICLabel MATLAB, EEGLab tools, etc. Replacing the manual task via an atoms system, which makes the proposed system reduces the processing time by 7200x and accuracy of 89.45%. The proposed system was trained, verified, and tested using CCHMC clinical data, using a 128-channel HydroCel electrode net (Magstim EGI, Eugene, OR) and an EGI NetAmp 400 at a 1000Hz sampling rate.

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

Simple color, intensity representations of polarimetric synthetic aperture radar (PolSAR) images fail to show the physical characteristics of the recorded ground objects, so several coherent and incoherent decomposition theorems have been proposed in the state-of-the-art literature. All these decompositions assume the fact that any scattering mechanism can be represented as the sum of some simpler, canonical scattering mechanisms. Following the same assumption, in this paper we employ the independent component analysis (ICA) for PolSAR images representation. Since ICA is a method used for blind sources separation, we expect that the derived ICA channels represent as well as possible certain types of scattering mechanisms present in the image. ICA decomposition is validated against the coherent Pauli and the incoherent H/a/α decompositions. The normalized compression distance (NCD) is used as a measure of quality of decompositions. Experiments are made on a SLC L-band F-SAR image over Kaufbeuren airfield, Germany.

Alcoholism is a clinical symptom characterized by a tendency to drink more alcohol than planned or commonly called alcoholics. Alcoholics will suffer the damage in some parts of the body, including the brain. One way to detect alcoholics from the brain is to record the electrical activity of the brain through the scalp or called electroencephalography (EEG). EEG records are often disturbed by noise such as muscle movements, eye blinking and heartbeat. Therefore, this research suggests Independent Component Analysis (ICA), as noise removal, Stationary Wavelet Transform (SWT) as a feature extraction method and are classified into two classes, namely alcoholism and normal using Probabilistic Neural Network (PNN). In this research, the result obtained from the ICA noise removal, signal decomposition using Daubechies SWT at level 6 and Probabilistic Neural Network (PNN) is considered effective to extract features and classify the 64 channels alcoholism data. The data come from Neurodynamics Laboratory, State University of New York Health Center. The result of this research generate an accuracy of 85.00% from 100 random data trial using ICA, SWT decomposition level 6, Wavelet Daubechies type 4 and PNN deviation value of 0.6.

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.

The independent component analysis and wavelet transform technology are used to separate and identify the internal combustion engine noise signal. According to the basic principle of independent component analysis, FastICA based on negative entropy great with the good stability and convergence speed algorithm is applied to separate the noise signals of six cylinder diesel engine. And the noise signals are decomposed into a series of independent components. The fast Fourier transform and wavelet transform technology are applied on each independent component analysis. Combining with the time-frequency analysis results and the internal combustion engine noise signal spectrum and the structure of the separation of the independent component and getting the corresponding relationship of different internal combustion engine noise sources. The results show that: the independent components correspond to the diesel engine combustion noise sources, the piston knock noise, and fuel injection pump noise and so on.