Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance (original) (raw)

Robin’s Viewer: Using Deep-Learning Predictions to Assist EEG Annotation

Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models’ output and re-evaluate potential false predictions. As a first step towards addressing these challenges, we developed Robin’s Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file forma...

Unsupervised EEG Artifact Detection and Correction

Frontiers in Digital Health, 2021

Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by physiological-, movement-, and equipment-related artifacts. Existing solutions to artifactdetectionare deficient because they require experts to manually explore and annotate data for artifact segments. Existing solutions to artifactcorrectionor removal are deficient because they assume that the incidence and specific characteristics of artifacts are similar across both subjects and tasks (i.e., “one-size-fits-all”). In this paper, we describe a novel EEG noise-reduction method that uses representation learning to perform patient- and task-specific artifact detection and correction. More specifically, our method extracts 58 clinically relevant features and applies an ensemble ...

The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

Frontiers in Neuroscience, 2022

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, co...

A streamable large-scale clinical EEG dataset for Deep Learning

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on their data without extensive feature engineering. The availability of large-scale datasets is a crucial aspect of allowing the experimentation of Deep Learning models. We are publishing the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network. We demonstrate a use case integrating this framework, and discuss why providing such neuroinformatics infrastructure to the community is critical for future scientific discoveries.

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN

Contrast Media & Molecular Imaging

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain’s function. EEG is an effective instrument for deciphering the brain’s complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader’s level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, ...

A Review on Machine Learning Algorithms in Handling EEG Artifacts

Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

Assessing learned features of Deep Learning applied to EEG

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs' outstanding capability to learn discriminative features with deep layers of neuron structures and iterative training process. This has inspired the EEG research community to adopt CNN in performing EEG classification tasks. However, CNNs learned features are not immediately interpretable, causing a lack of understanding of the CNNs' internal working mechanism. To improve CNN interpretability, CNN visualization methods are applied to translate the internal features into visually perceptible patterns for qualitative analysis of CNN layers. Many CNN visualization methods have been proposed in the Computer Vision literature to interpret the CNN network structure, operation, and semantic concept, yet applications to EEG data analysis have been limited. In this work we use 3 different methods to extract EEG-relevant features from a CNN trained on raw EEG data: optimal samples for each classification category, activation maximization, and reverse convolution. We applied these methods to a high-performing Deep Learning model with state-of-the-art performance for an EEG sex classification task, and show that the model features a difference in the theta frequency band. We show that visualization of a CNN model can reveal interesting EEG results. Using these tools, EEG researchers using Deep Learning can better identify the learned EEG features, possibly identifying new class relevant biomarkers.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

Frontiers in Neuroinformatics, 2021

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visu...

Robust Approach Based on Convolutional Neural Networks for Identification of Focal EEG Signals

IEEE sensors letters, 2019

Electroencephalogram (EEG) signals provide important information for identification of epileptogenic area. Identification of focal EEG signals locates the epileptogenic area which is an important task for successful surgery. In this paper, convolutional neural networks (CNNs) based framework is proposed for automatic identification of focal EEG signals. The proposed intelligent system initially uses a window for randomly segment the input EEG signals. The short time Fourier transform (STFT) is applied on to the segmented EEG signals for conversion of input EEG signals into Time-Frequency (T-F) representation. The T-F representation of EEG signals are used as T-F images. Instead of training an end-to-end CNNs which necessitates more input images and time, we opt to use a pre-trained CNNs model for transfer learning. Specifically, the deep feature extraction is employed for acquiring the more convenient features from the input EEG images. The deep features extracted from AlexNet, VGG16, VGG19, and Resnet50 models used as input to different variant of k-nearest neighbor (k-NN) classifier. The conducted experimental works show that AlexNet, VGG16, and Resnet50 achieve promising results. Specifically, the fc6 layers of AlexNet, VGG16 and fc1000 layer of Resnet50 produce 99.8% accuracy score with weighted-k-NN approache. The comparative study shows that proposed method provides better performance in comparison to state-of-the-art methods. Index Terms-Electroencephalogram (EEG) signal; convolutional neural networks (CNNs); transfer learning; k-nearest neighbor .

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Sensors 2024, 24, 877, 2024

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.