Evaluation of the AFOP/DAFOP Method for Automatic Filtering of EEGs of Patients With Epilepsy

EPIC: Annotated epileptic EEG independent components for artifact reduction

Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain’s electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers.

Measurement(s)	Electroencephalogram Independent Components
Technology Type(s)	MATLAB and Python

Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components

OBJECTIVE

Independent component analysis (ICA) is commonly used to remove noisy artifacts from multi-channel scalp electroencephalogram (EEG) signals. ICA decomposes EEG into different independent components (ICs) and then, experts remove the noisy ones. This process is highly time-consuming and experts are not always available. To surpass this drawback, research is going on to develop models to automatically conduct IC classification. Current state-of-the-art models use power spectrum densities (PSDs) and topoplots to classify ICs. The performance of these methods may be limited by disregarding the IC time-series that would contribute to fully simulate the visual inspection performed by experts.

METHODS

We present a novel ensemble deep neural network that combines time-series, PSDs, and topoplots to classify ICs. Moreover, we study the ability to use our model in transfer learning approaches.

RESULTS

Experimental results showed that using time-series improves IC classification. Results also indicated that transfer learning obtained higher performance than simply training a new model from scratch.

CONCLUSION

Researchers should develop IC classifiers using the three sources of information. Moreover, transfer learning approaches should be considered when producing new deep learning models.

SIGNIFICANCE

This work improves IC classification, enhancing the automatic removal of EEG artifacts. Additionally, since labelled ICs are generally not publicly available, the possibility of using our model in transfer learning studies may motivate other researchers to develop their own classifiers.

Methods for artifact detection and removal from scalp EEG: A review

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Dual adaptive filtering by optimal projection applied to filter muscle artifacts on EEG and comparative study

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.

PureEEG: automatic EEG artifact removal for epilepsy monitoring

AIM OF THE STUDY

A novel method for removal of artifacts from long-term EEGs was developed and evaluated. The method targets most types of artifacts and works without user interaction.

MATERIALS AND METHODS

The method is based on a neurophysiological model and utilizes an iterative Bayesian estimation scheme. The performance was evaluated by two independent reviewers. From 48 consecutive epilepsy patients, 102 twenty-second seizure onset EEGs were used to evaluate artifacts before and after artifact removal and regarding the erroneous attenuation of true EEG patterns.

RESULTS

The two reviewers found "major improvements" in 59% and 49% of the EEG epochs respectively, and "minor improvements" in 38% and 47% of the epochs, respectively. The answer "similar or worse" was chosen only in 0% and 4%, respectively. Neither of the reviewers found "major attenuations", i.e., a significant attenuation of significant EEG patterns. Most EEG epochs were found to be either "mostly preserved" or "all preserved". A "minor attenuation" was found only in 0% and 17%, respectively.

CONCLUSIONS

The proposed artifact removal algorithm effectively removes artifacts from EEGs and improves the readability of EEGs impaired by artifacts. Only in rare cases did the algorithm slightly attenuate EEG patterns, but the clear visibility of significant patterns was preserved in all cases of this study. Current artifact removal methods work either semi-automatically or with insufficient reliability for clinical use, whereas the "PureEEG" method works fully automatically and leaves true EEG patterns unchanged with a high reliability.