Noise Reduction in EEG Signals using Convolutional Autoencoding Techniques

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with 95% explained variance, by comparing the Harrell-Davis decile differences between the SNR distributions of both methods and the raw signal SNR distribution for each task. It was found that the CAE outperformed PCA for the full dataset across all three tasks, however the CAE did not outperform PCA for the person specific datasets in any of the three tasks. The results indicate that CAEs can perform better than PCA for noise reduction in EEG signals, but performance of the model may be training size dependent

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

Multimodal autoencoder predicts fNIRS resting state from EEG signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model’s fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project

Stacked Convolutional Recurrent Auto-encoder for Noise Reduction in EEG

Electroencephalogram (EEG) can be used to record electrical potentials in the brain by attaching electrodes to the scalp. However, these low amplitude recordings are susceptible to noise which originates from several sources including ocular, pulse and muscle artefacts. Their presence has a severe impact on analysis and diagnoses of brain abnormalities. This research assessed the effectiveness of a stacked convolutional-recurrent auto-encoder (CR-AE) for noise reduction of EEG signal. Performance was evaluated using the signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) in comparison to principal component analysis (PCA), independent component analysis (ICA) and a simple auto-encoder (AE). The Harrell-Davis quantile estimator was used to compare SNR and PSNR distributions of reconstructed and raw signals. It was found that the proposed CR-AE achieved a mean SNR of 5:53 db and signicantly increased the SNR across all quantiles for each channel compared to the state-of-the-art methods. However, though SNR increased PSNR did not and the proposed CR-AE was outperformed by each baseline across the majority of quantiles for all channels. In addition, though reconstruction error was very low none of the proposed CR-AE architectures could generalize to the second dataset

Leveraging Artificial Intelligence to Improve EEG-fNIRS Data Analysis

La spectroscopie proche infrarouge fonctionnelle (fNIRS) est apparue comme une technique de neuroimagerie qui permet une surveillance non invasive et à long terme de l'hémodynamique corticale. Les technologies de neuroimagerie multimodale en milieu clinique permettent d'étudier les maladies neurologiques aiguës et chroniques. Dans ce travail, nous nous concentrons sur l'épilepsie - un trouble chronique du système nerveux central affectant près de 50 millions de personnes dans le monde entier prédisposant les individus affectés à des crises récurrentes. Les crises sont des aberrations transitoires de l'activité électrique du cerveau qui conduisent à des symptômes physiques perturbateurs tels que des changements aigus ou chroniques des compétences cognitives, des hallucinations sensorielles ou des convulsions de tout le corps. Environ un tiers des patients épileptiques sont récalcitrants au traitement pharmacologique et ces crises intraitables présentent un risque grave de blessure et diminuent la qualité de vie globale. Dans ce travail, nous étudions 1. l'utilité des informations hémodynamiques dérivées des signaux fNIRS dans une tâche de détection des crises et les avantages qu'elles procurent dans un environnement multimodal par rapport aux signaux électroencéphalographiques (EEG) seuls, et 2. la capacité des signaux neuronaux, dérivé de l'EEG, pour prédire l'hémodynamique dans le cerveau afin de mieux comprendre le cerveau épileptique. Sur la base de données rétrospectives EEG-fNIRS recueillies auprès de 40 patients épileptiques et utilisant de nouveaux modèles d'apprentissage en profondeur, la première étude de cette thèse suggère que les signaux fNIRS offrent une sensibilité et une spécificité accrues pour la détection des crises par rapport à l'EEG seul. La validation du modèle a été effectuée à l'aide de l'ensemble de données CHBMIT open source documenté et bien référencé avant d'utiliser notre ensemble de données EEG-fNIRS multimodal interne. Les résultats de cette étude ont démontré que fNIRS améliore la détection des crises par rapport à l'EEG seul et ont motivé les expériences ultérieures qui ont déterminé la capacité prédictive d'un modèle d'apprentissage approfondi développé en interne pour décoder les signaux d'état de repos hémodynamique à partir du spectre complet et d'une bande de fréquences neuronale codée spécifique signaux d'état de repos (signaux sans crise). Ces résultats suggèrent qu'un autoencodeur multimodal peut apprendre des relations multimodales pour prédire les signaux d'état de repos. Les résultats suggèrent en outre que des gammes de fréquences EEG plus élevées prédisent l'hémodynamique avec une erreur de reconstruction plus faible par rapport aux gammes de fréquences EEG plus basses. De plus, les connexions fonctionnelles montrent des modèles spatiaux similaires entre l'état de repos expérimental et les prédictions fNIRS du modèle. Cela démontre pour la première fois que l'auto-encodage intermodal à partir de signaux neuronaux peut prédire l'hémodynamique cérébrale dans une certaine mesure. Les résultats de cette thèse avancent le potentiel de l'utilisation d'EEG-fNIRS pour des tâches cliniques pratiques (détection des crises, prédiction hémodynamique) ainsi que l'examen des relations fondamentales présentes dans le cerveau à l'aide de modèles d'apprentissage profond. S'il y a une augmentation du nombre d'ensembles de données disponibles à l'avenir, ces modèles pourraient être en mesure de généraliser les prédictions qui pourraient éventuellement conduire à la technologie EEG-fNIRS à être utilisée régulièrement comme un outil clinique viable dans une grande variété de troubles neuropathologiques.----------ABSTRACT Functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that allows for non-invasive and long-term monitoring of cortical hemodynamics. Multimodal neuroimaging technologies in clinical settings allow for the investigation of acute and chronic neurological diseases. In this work, we focus on epilepsy—a chronic disorder of the central nervous system affecting almost 50 million people world-wide predisposing affected individuals to recurrent seizures. Seizures are transient aberrations in the brain's electrical activity that lead to disruptive physical symptoms such as acute or chronic changes in cognitive skills, sensory hallucinations, or whole-body convulsions. Approximately a third of epileptic patients are recalcitrant to pharmacological treatment and these intractable seizures pose a serious risk for injury and decrease overall quality of life. In this work, we study 1) the utility of hemodynamic information derived from fNIRS signals in a seizure detection task and the benefit they provide in a multimodal setting as compared to electroencephalographic (EEG) signals alone, and 2) the ability of neural signals, derived from EEG, to predict hemodynamics in the brain in an effort to better understand the epileptic brain. Based on retrospective EEG-fNIRS data collected from 40 epileptic patients and utilizing novel deep learning models, the first study in this thesis suggests that fNIRS signals offer increased sensitivity and specificity metrics for seizure detection when compared to EEG alone. Model validation was performed using the documented open source and well referenced CHBMIT dataset before using our in-house multimodal EEG-fNIRS dataset. The results from this study demonstrated that fNIRS improves seizure detection as compared to EEG alone and motivated the subsequent experiments which determined the predictive capacity of an in-house developed deep learning model to decode hemodynamic resting state signals from full spectrum and specific frequency band encoded neural resting state signals (seizure free signals). These results suggest that a multimodal autoencoder can learn multimodal relations to predict resting state signals. Findings further suggested that higher EEG frequency ranges predict hemodynamics with lower reconstruction error in comparison to lower EEG frequency ranges. Furthermore, functional connections show similar spatial patterns between experimental resting state and model fNIRS predictions. This demonstrates for the first time that intermodal autoencoding from neural signals can predict cerebral hemodynamics to a certain extent. The results of this thesis advance the potential of using EEG-fNIRS for practical clinical tasks (seizure detection, hemodynamic prediction) as well as examining fundamental relationships present in the brain using deep learning models. If there is an increase in the number of datasets available in the future, these models may be able to generalize predictions which would possibly lead to EEG-fNIRS technology to be routinely used as a viable clinical tool in a wide variety of neuropathological disorders

Deep learning classification model of mental workload levels using EEG signals

Understanding and improving humance performance, especially in situations that require safety, productivity, and well-being, relies on categorising mental workload (MWL). Traditional methods for measuring MWL, such as in driving and piloting, have given us some understanding, but these methods must accurately distinguish between low and high workload levels. Excessive work can tyre participants, while insufficient work can make them bored and inefficient. Traditional MWL assessment tools, such as questionnaires, sometimes make it harder for people to manage their MWL, especially when they struggle to express or understand their thoughts and feelings. The recent work shift to neurophysiological signals, specifically electroencephalogram (EEG), provides a promising way to measure brain activity related to MWL non-invasively. Advanced techniques such as deep learning have made it easier to study EEG signals in more detail. Our goal was to develop a clear and consistent approach for using EEG signals to classify MWL effectively. Our approach focused on each process stage, from preparing the data to evaluating the model and addressing common mistakes and misunderstandings in current techniques. The first study addresses the challenges of using EEG data contaminated by artefacts for assessing MWL. EEG signal artefacts, such as eye movement or muscle activity, can skew MWL assessment. Recently, there has been significant progress in using deep learning models to interpret EEG signals, but the challenge remains. The preprocessing pipeline for EEG artefact removal is broad and inconsistently adopted; some pipelines are time-consuming and contain human intervention steps, so they are unsuitable for automation systems. Therefore, this study focused on automatic EEG artefact removal for deep learning analysis. Furthermore, we examined the impact of various preprocessing techniques on the effectiveness of deep learning models in classifying MWL levels. We used state-of-the-art models such as Stacked LSTM, BLSTM, and BLSTM-LSTM, and found that certain techniques—specifically, the ADJUST algorithm—significantly enhanced model performance. However, the sophisticated models could extract relevant information from raw data, indicating a reduced need for preprocessing. The second study shifted the focus to channel selection to refine the automation of MWL classification and reduce unnecessary computational expenses by using unnecessary electrodes, aligning more closely to real-world applications. We prioritised the best electrode setup focusing on brain activity related to MWL. We removed unnecessary data using Riemannian geometry, an effective method for EEG channel selection. We aimed to balance information sufficiency with computational efficiency and to reduce the number of electrodes. The study also evaluated covariance estimators for Riemannian geometry for their effectiveness in channel selection and impact on deep learning models for MWL classification, as the traditional Empirical Covariance (EC) has limitations for the EEG signal. Finally, the third study tackled a critical but frequently overlooked aspect of MWL level classification using machine learning or deep learning techniques: the temporal nature of EEG signals. We underscored that the traditional cross-validation technique violates the sequential nature of time series data, leading to data leakage, model overfitting, and inaccurate MWL assessment. Specifically, to predict the subject’s MWL level, we could not randomly split data and use future data to train the model and predict the previous MWL level. To address this problem, this study focused on the model training phase, specifically on the importance of time series cross-validation methods. We adopted the expanding window and rolling window strategies, finding that using the expanding window strategy outperformed those using the rolling window strategy. This research carefully developed a comprehensive and consistent method for classifying MWL using EEG signals. We aimed to correct misunderstandings and set a standard in brain-computer interface (BCI) systems. This will help guide future research and development efforts.Understanding and improving humance performance, especially in situations that require safety, productivity, and well-being, relies on categorising mental workload (MWL). Traditional methods for measuring MWL, such as in driving and piloting, have given us some understanding, but these methods must accurately distinguish between low and high workload levels. Excessive work can tyre participants, while insufficient work can make them bored and inefficient. Traditional MWL assessment tools, such as questionnaires, sometimes make it harder for people to manage their MWL, especially when they struggle to express or understand their thoughts and feelings. The recent work shift to neurophysiological signals, specifically electroencephalogram (EEG), provides a promising way to measure brain activity related to MWL non-invasively. Advanced techniques such as deep learning have made it easier to study EEG signals in more detail. Our goal was to develop a clear and consistent approach for using EEG signals to classify MWL effectively. Our approach focused on each process stage, from preparing the data to evaluating the model and addressing common mistakes and misunderstandings in current techniques. The first study addresses the challenges of using EEG data contaminated by artefacts for assessing MWL. EEG signal artefacts, such as eye movement or muscle activity, can skew MWL assessment. Recently, there has been significant progress in using deep learning models to interpret EEG signals, but the challenge remains. The preprocessing pipeline for EEG artefact removal is broad and inconsistently adopted; some pipelines are time-consuming and contain human intervention steps, so they are unsuitable for automation systems. Therefore, this study focused on automatic EEG artefact removal for deep learning analysis. Furthermore, we examined the impact of various preprocessing techniques on the effectiveness of deep learning models in classifying MWL levels. We used state-of-the-art models such as Stacked LSTM, BLSTM, and BLSTM-LSTM, and found that certain techniques—specifically, the ADJUST algorithm—significantly enhanced model performance. However, the sophisticated models could extract relevant information from raw data, indicating a reduced need for preprocessing. The second study shifted the focus to channel selection to refine the automation of MWL classification and reduce unnecessary computational expenses by using unnecessary electrodes, aligning more closely to real-world applications. We prioritised the best electrode setup focusing on brain activity related to MWL. We removed unnecessary data using Riemannian geometry, an effective method for EEG channel selection. We aimed to balance information sufficiency with computational efficiency and to reduce the number of electrodes. The study also evaluated covariance estimators for Riemannian geometry for their effectiveness in channel selection and impact on deep learning models for MWL classification, as the traditional Empirical Covariance (EC) has limitations for the EEG signal. Finally, the third study tackled a critical but frequently overlooked aspect of MWL level classification using machine learning or deep learning techniques: the temporal nature of EEG signals. We underscored that the traditional cross-validation technique violates the sequential nature of time series data, leading to data leakage, model overfitting, and inaccurate MWL assessment. Specifically, to predict the subject’s MWL level, we could not randomly split data and use future data to train the model and predict the previous MWL level. To address this problem, this study focused on the model training phase, specifically on the importance of time series cross-validation methods. We adopted the expanding window and rolling window strategies, finding that using the expanding window strategy outperformed those using the rolling window strategy. This research carefully developed a comprehensive and consistent method for classifying MWL using EEG signals. We aimed to correct misunderstandings and set a standard in brain-computer interface (BCI) systems. This will help guide future research and development efforts

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section