Orthogonal Extended Infomax Algorithm

The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster. Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the unmixing matrix leading to an orthogonal extended infomax algorithm (OgExtInf). Computational performance of OgExtInf is compared with two fast ICA algorithms: the popular FastICA and Picard, a L-BFGS algorithm belonging to the family of quasi-Newton methods. Our results demonstrate superior performance of the proposed method on small-size EEG data sets as used for example in online EEG processing systems, such as brain-computer interfaces or clinical systems for spike and seizure detection.Comment: 17 pages, 6 figure

Review of Artifact Rejection Methods for Electroencephalographic Systems

Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms

EEG Artifact Removal Using a Wavelet Neural Network

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data

Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization

The significant advancements in electroencephalography (EEG)-driven technology have led to its widespread use in assessing stroke-related conditions. Over the years, various studies have explored the potential of EEG oscillatory patterns in neurological research, with several of them giving limited attention to the signal processing techniques employed, precluding a proper understanding of EEG oscillatory patterns under various conditions. To resolve this issue, we systematically investigated how artifacts impact EEG oscillatory rhythms associated with upper limb movement-related tasks. Thus, the EEG signals of motor tasks were acquired non-invasively from healthy subjects and processed using automated artifact-attenuation methods. Subsequently, the Mu and Beta bands in the brain's motor cortex region were extracted through time-frequency analysis and analyzed using relevant metrics. Experimental results revealed that artifacts in EEG would substantially influence the brain activation strength and response during motor tasks. Notably, signals preprocessed with Reduction of Electroencephalographic Artifacts based on Multi Wiener Filter and Enhanced Wavelet Independent Component Analysis (RELAX_MWF_wICA) showed better brain responses and high task classification performance compared to other methods and the raw signal across motor tasks. This study's findings revealed that the choice of signal processing technique is crucial, as it would influence its analysis and interpretation, thus highlighting the need for careful consideration and usage

Optimised use of independent component analysis for EEG signal processing

Electroencephalography (EEG) is the prevalent technique for monitoring brain function. It employs a set of electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of unwanted signals, which are known as artefacts, usually mix with the EEG at any point during the recording process. As the amplitudes of the EEG and ERPs are very small (in the order of microvolts), they can be buried in the artefacts which have very high amplitudes in the order of millivolts. Therefore, contamination of EEG activity by the artefacts can degrade the quality of the EEG recording and may cause error in EEG/ERP signal interpretation. Several EEG artefact removal methods already exist in the literature and these previous studies have concentrated on manual or automatic detection of either one or, of a few types of EEG artefacts. Among the proposed methods, Independent Component Analysis (ICA) based techniques are commonly applied to successfully detect the artefacts. Different types of ICA algorithms have been developed, which aim to estimate the individual sources of a linearly mixed signal. However, the estimation criterion differs across various ICA algorithms, which may deliver different results

Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm

An automatic artifact extraction system is proposed based on a hybridization of Stone’s BSS and genetic algorithm. This hybridization is called evolutionary Stone’s BSS algorithm (ESBSS). Original Stone’s BSS used short- and long-term half-life parameters as constant values, and the changes in these parameters will be affecting directly the separated signals; also there is no way to determine the best parameters. The genetic algorithm is a suitable technique to overcome this problem by finding randomly the optimum half-life parameters in Stone’s BSS. The proposed system is used to extract automatically the common artifacts such as ocular and heart beat artifacts from EEG mixtures without prejudice to the data; also there is no notch filter used in the proposed system in order not to lose any useful information

EEG based assessment of emotional wellbeing in smart environment

Abstract. Smart technologies are frequently united and automated in our everyday settings and commonplace task by linking computers and other devices. While there has been a necessity to build smart environments for an easy and comfortable life, research on measuring wellbeing in this environment becomes increasingly intensive. Emotion is one of the decisive aspects of wellbeing that encourages us to work effectively, manage, and cope with stress, and affect our physical health. This work evaluates the EEG signal to measure individuals the different emotional states in a smart space by creating a computer gaming scenario. EEG, a physiological signal which provides details on mental, physiological, and emotional states, EEG frequency bands are strongly correlated with positive and negative emotional responses. Since brain left frontal cortical area is responsible for positive emotion and the right frontal region associate, therefore, we choose two pairs of EEG electrodes F3-F4, and F7-F8 to assess the game player emotional states during the gaming situations. We measure the EEG frontal alpha asymmetry (FAA) by comparing variations in the alpha band power levels in the left and right frontal cortex, corresponding to positive and negative emotions. Our experiment outcome reveals considerable support with the emotional variance of the test participants. We note that multiple interruptions during the gaming situation create irritation to the test subjects. These findings also confirm that F3 and F4 EEG channels are the most sensitive to human emotional responses compared to F7 and F8 channels