EOG Artifacts Removal in EEG Measurements for Affective Interaction

A brain-computer interface (BCI) is a direct link between the brain and a computer. Multi-modal input with BCI forms a promising solution for creating rich gaming experience. Electroencephalography (EEG) measurement is the sole necessary component for a BCI system. EEG signals have the characteristics of large amount, multiple channels and sensitive to noise. The amount of valuable information derived from EEG signals is dependent on both the amount of noises embedded in the original measurement and the algorithms selected for postprocessing. Therefore, artifacts removal in the preprocess step is crucial. Electrooculography (EOG) signals are one of the major artifacts that often appear in EEG measurement. In this paper, we compared two different algorithms (Recursive Least Square (RLS) and Blind Source Separation (BSS)) to investigate their performances on removing EOG artifacts from EEG signals. Results indicate that the performance of RLS algorithm is better than BSS algorithm no matter whether there are any EOG reference signals. For BSS algorithm, the performance is better when EOG reference signals are available. These results show that for a BCI system, EEG reference is often necessary. Performance will be sacrificed if an EEG system cannot have any EOG reference signals

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Brain-computer interface for robot control with eye artifacts for assistive applications

Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot

Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder

The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the manual approach of ICA using the correlation coefficient, a comparative affect on the SNR using both approaches and a humanistic evaluation using visual inspections of the components of the stacked dense autoencoder reconstruction to that of the post ICA reconstruction where an inverse RMSE allowed for a further statistical evaluation of this comparison. It was found that the stacked dense autoencoder was unable to reconstruct random signal segments in any meaningful capacity when compared to that of ICA

Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

A systematic review on artifact removal and classification techniques for enhanced MEG-based BCI systems

Neurological disease victims may be completely paralyzed and unable to move, but they may still be able to think. Their brain activity is the only means by which they can interact with their environment. Brain-Computer Interface (BCI) research attempts to create tools that support subjects with disabilities. Furthermore, BCI research has expanded rapidly over the past few decades as a result of the interest in creating a new kind of human-to-machine communication. As magnetoencephalography (MEG) has superior spatial and temporal resolution than other approaches, it is being utilized to measure brain activity non-invasively. The recorded signal includes signals related to brain activity as well as noise and artifacts from numerous sources. MEG can have a low signal-to-noise ratio because the magnetic fields generated by cortical activity are small compared to other artifacts and noise. By using the right techniques for noise and artifact detection and removal, the signal-to-noise ratio can be increased. This article analyses various methods for removing artifacts as well as classification strategies. Additionally, this offers a study of the influence of Deep Learning models on the BCI system. Furthermore, the various challenges in collecting and analyzing MEG signals as well as possible study fields in MEG-based BCI are examined

Использование метода независимых компонент для автоматического удаления артефактов ЭЭГ, связанных с движениями глаз

В роботі аналізується можливість використання методу незалежних компонент для часових рядів, а саме алгоритму TDSEP, для видалення складових сигналу ЕЕГ, джерелом яких є активність м’язів під час руху очей. Був запропонований алгоритм автоматичного видалення артефактів ЕЕГ та проведена оцінка його ефективності на реальних записах ЕЕГ.Background. Eye movement artifacts contained in EEG recordings hamper a lot the automatic processing and analysis of EEG signal. Therefore, the removal of such artifacts is important stage for any further signal processing. There are artifacts removal methods based on using wavelet transformation, regression analysis in the time and frequency domain, Principal component analysis and Independent component analysis. Methods. The novel method of automatic EEG eye movement artifacts removal based on Independent Component Analysis was proposed. The method utilizes the TDSEP algorithm for blind source separation. Own criteria for artifact components detection were used. The method was implemented with the Python programming language and tested on EEG signals recorded from two healthy volunteers. Results. Comparison of the effectiveness of the method was conducted with the participation of two experts. They were asked to review the EEG fragments before and after artifacts removal and evaluate the quality of artifacts removal. The average value of assessing the quality of artifacts removal was 4.83 for TDSEP based algorithm and 4.58 for FastICA based algorithm. Conclusion. The proposed method is more effective then method based on FastICA algorithm and using it for automatic EEG eye movement artifacts removal is expedient.В работе анализируется возможность использования метода независимых компонент для временных рядов, а именно алгоритма TDSEP, для удаления составляющих сигнала ЭЭГ, источником которых является активность мышц во время движения глаз. Был предложен алгоритм автоматического удаления артефактов ЭЭГ и проведена оценка его эффективности на реальных записях ЭЭГ

TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction

OBJECTIVE: During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. METHODS: We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. RESULTS: Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. CONCLUSIONS: ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. SIGNIFICANCE: We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts