Enhancement of Eeg Signal

This project is concerned with the rectification of EEG recording. EEG signal is often gets distorted due to the presence of various signals which are known as artifacts. Eye blinking is one of the major artifacts causing EEG to distort. Eye blinking distorts the EEG signal by varying the electric potential present over the scalp. To remove the artifacts, signal separation techniques are widely used in modern days. There are various methods used for removing different types of artifacts present in EEG recording and one of the techniques is Blind Source Separation which is used for separation of source signal from artifacts.This thesis also demonstrates the use of Second Order Blind Identification with Robust Orthogonalization (known as SOBI-RO) algorithm to remove the ocular artifacts and reconstruct the original EEG signal. Finally, the original signal and estimated signal is compared. To illustrate the algorithm a raw EEG data has been taken from the database. The data has been processed on MATLAB platform using the SOBI-RO algorithm. In the end it was found that the ocular artifacts are successfully removed from the raw EEG data. The performance is evaluated using signal to distortion ratio

Preliminary Results on a New Algorithm for Blink Correction Adaptive to Inter- and Intra-Subject Variability

This paper presents a new preprocessing method to correct blinking artifacts in Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). This Algorithm for Blink Correction (ABC) directly corrects the signal in the time domain without the need for additional Electrooculogram (EOG) electrodes. The main idea is to automatically adapt to the blink's inter- and intra-subject variability by considering the blink's amplitude as a parameter. A simple Minimum Distance to Riemannian Mean (MDRM) is applied as the classification algorithm. Preliminary results on three subjects show a mean classification accuracy increase of 13.7% using ABC

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

Brain-Computer Interface in Multimedia Communication

This article raises various issues in the design of an efficient BCI system in multimedia applications. The main focus is on one specific modality, namely an electroencephalography (EEG)-based BCI. In doing so, we provide an overview of the most recent progress achieved in this field, with an emphasis on signal processing aspects

Empirical Modeling of Asynchronous Scalp Recorded and Intracranial EEG Potentials

A Brain-Computer Interface (BCI) is a system that allows people with severe neuromuscular disorders to communicate and control devices using their brain signals. BCIs based on scalp-recorded electroencephalography (s-EEG) have recently been demonstrated to provide a practical, long-term communication channel to severely disabled users. These BCIs use time-domain s-EEG features based on the P300 event-related potential to convey the user\u27s intent. The performance of s-EEG-based BCIs has generally stagnated in recent years, and high day-to-day performance variability exists for some disabled users. Recently intracranial EEG (i-EEG), which is recorded from the cortical surface or the hippocampus, has been successfully used to control BCIs in experimental settings. Because these recordings are closer to the sources of the neural activity, i-EEG provides superior signal-to-noise ratio, spatial resolution, and broader bandwidth compared to s-EEG. However, because i-EEG requires surgery and the long-term efficacy for BCIs must still be explored, this approach is still not an option for patients. In order to improve s-EEG BCI performance, it is important understand the underlying brain phenomena and exploit the relationships between the s-EEG and generally superior i-BEG signals. Because the human head acts as a volume conductor consisting of the brain, cerebrospinal fluid, skull, and scalp tissue, linear mathematical models can be used to relate s-EEG and i-EEG. This dissertation presents unique s-EEG and i-EEG data that were recorded from the same subjects and used to develop novel empirical models to estimate s-EEG from i-EEG. These new empirical models can be used to better understand the sources and propagation of the relevant neural activity, as well as to validate existing theoretical volume conduction models. It is envisioned that this knowledge will help to advance algorithms for improving s-EEG BCI performance

A real-time noise cancelling EEG electrode employing Deep Learning

Two major problems of head worn electroencephalogram (EEG) are muscle and eye-blink artefacts, in particular in non-clinical environments while performing everyday tasks. Current artefact removal techniques such as principle component analysis (PCA) or independent component analysis (ICA) take signals from a high number of electrodes and separate the noise from the signal by processing them offline in a computationally expensive and slow way. In contrast, we present a smart compound electrode which is able to learn in real-time to remove artefacts. The smart 3D printed electrode consists of a central electrode and a ring electrode where poly-lactate acid (PLA) was used for the the base and Ag/AgCl for the conductive parts allowing standard manufacturing processes. A new deep learning algorithm then learns continuously to remove both eye-blink and muscle artefacts which combines the real-time capabilities of adaptive filters with the power of deep neural networks. The electrode setup together with the deep learning algorithm increases the signal to noise ratio of the EEG in average by 20 dB. Our approach offers a simple 3D printed design in combination with a real-time algorithm which can be integrated into the electrode itself. This electrode has the potential to provide high quality EEG in non-clinical and consumer applications, such as sleep monitoring and brain-computer interface (BCI).Comment: 12 pages, 4 figures, code available under http://doi.org/10.5281/zenodo.413110

Real-time noise cancellation with deep learning

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm’s performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones