Data recordings are often corrupted by noise, and it can be difficult to isolate clean data of interest. For example, mobile electroencephalography is commonly corrupted by motion artifact, which limits its use in real-world settings. Here, we describe a novel noise-canceling algorithm that uses canonical correlation analysis to find and remove subspaces of corrupted data recordings that are most strongly correlated with subspaces of reference noise recordings. The algorithm, termed iCanClean, is computationally efficient, which may be useful for real-time applications, such as brain computer interfaces. In future work, we will quantify the algorithm's performance and compare it with alternative cleaning methods.
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis
Sharif University of Technology（谢里夫理工大学）；University of Toronto（多伦多大学）；University Health Network（大学医疗网络）；Amirkabir University of Technology（阿米尔卡比尔理工大学）
Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added …
Physiological monitoring in intensive care units (ICU) generates data that can be used in clinical research. However, the recording conditions in clinical settings limit the automated extraction of relevant information from physiological signals due to noise and artifacts. Therefore, removing artifacts before clinical research is essential. Manual annotation by experienced researchers, which is the gold standard for removing artifacts, is time-consuming and costly due to the volume of the data generated in the ICU. In this study, we propose a hybrid artifact detection system that combines a Va…
How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings
brainboost GmbH（brainboost有限公司）；Institute for Applied Computer Science, Bundeswehr University Munich（慕尼黑联邦国防军大学应用计算机科学研究所）；Center for Machine Learning, Heilbronn University（海尔布隆大学机器学习中心）
Biological data like electroencephalography (EEG) are typically contaminated by unwanted signals, called artifacts. Therefore, many applications dealing with biological data with low signal-to-noise ratio require robust artifact correction. For some applications like brain-computer-interfaces (BCI), the artifact correction needs to be real-time capable. Artifact subspace reconstruction (ASR) is a statistical method for artifact reduction in EEG. However, in its current implementation, ASR cannot be used in mobile data recordings using limited hardware easily. In this report, we add to the grow…
The task of heart rate estimation using photoplethysmographic (PPG) signal is challenging due to the presence of various motion artifacts in the recorded signals. In this paper, a fast algorithm for heart rate estimation based on modified SPEctral subtraction scheme utilizing Composite Motion Artifacts Reference generation (SPECMAR) is proposed using two-channel PPG and three-axis accelerometer signals. First, the preliminary noise reduction is obtained by filtering unwanted frequency components from the recorded signals. Next, a composite motion artifacts reference generation method is develo…
The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An autom…
Objective: Often, people with Subjective Cognitive Impairment (SCI), Mild Cognitive Impairment (MCI) and dementia are underwent to Electroencephalography (EEG) in order to evaluate through biological indexes the functional connectivity between brain regions and activation areas during cognitive performance. EEG recordings are frequently contaminated by muscle artifacts, which obscure and complicate their interpretation. These muscle artifacts are particularly difficult to be removed from the EEG in order the latter to be used for further clinical evaluation. In this paper, we proposed a new ap…
With the development of widely available commercial eye trackers, the use of eye tracking and pupillometry have become prevalent tools in cognitive research, both in industry and academia. However, dealing with pupil recordings often proves challenging, as the raw data produced by the eye tracker is subject to noise and artifacts. With the PUPILS toolbox the authors wish to simplify the processing of pupil data for researchers. The toolbox receives the raw data extracted from the eye tracker output files and provides an analysis of the relevant pupillary events (e.g., blinks and saccades), int…
Taipei Veteran General Hospital（台北荣民总医院）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；University of Washington（华盛顿大学）；Duke University（杜克大学）；National Center for Theoretical Sciences（理论科学研究中心）
Object: It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a…
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attem…
SSN College of Engineering（SSN工程学院）；CSIR - Central Electronics Engineering Research Institute（CSIR中央电子工程研究所）
Electroencephalogram (EEG) is the recording which is the result due to the activity of bio-electrical signals that is acquired from electrodes placed on the scalp. In Electroencephalogram signal(EEG) recordings, the signals obtained are contaminated predominantly by the Electrooculogram(EOG) signal. Since this artifact has higher magnitude compared to EEG signals, these noise signals have to be removed in order to have a better understanding regarding the functioning of a human brain for applications such as medical diagnosis. This paper proposes an idea of using Independent Component Analysis…
Objective. Recent advancements in electrode designs and micro-fabrication technology has allowed existence of microelectrode arrays with hundreds of channels for single-cell recordings. In such electrophysiological recordings, each implanted micro-electrode can record the activities of more than one neuron in its vicinity. Recording the activities of multiple neurons may also be referred to as multiple unit activity. However, for any further analysis, the main goal is to isolate the activity of each recorded neuron and thus called single-unit activity. This process may also be referred to as s…
Epilepsy is the second most common brain disorder after migraine. Automatic detection of epileptic seizures can considerably improve the patients' quality of life. Current Electroencephalogram (EEG)-based seizure detection systems encounter many challenges in real-life situations. The EEGs are non-stationary signals and seizure patterns vary across patients and recording sessions. Moreover, EEG data are prone to numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges, we introduce the use of a deep learning-based approach that autom…
