Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study

Unsupervised EEG-Based Seizure Anomaly Detection with Denoising Diffusion Probabilistic Models

While many seizure detection methods have demonstrated great accuracy, their training necessitates a substantial volume of labeled data. To address this issue, we propose a novel method for unsupervised seizure anomaly detection called SAnoDDPM, which uses denoising diffusion probabilistic models (DDPM). We designed a novel pipeline that uses a variable lower bound on Markov chains to identify potential values that are unlikely to occur in anomalous data. The model is first trained on normal data, then anomalous data is input to the trained model. The model resamples the anomalous data and converts it to normal data. Finally, the presence of seizures can be determined by comparing the before and after data. Moreover, the input 2D spectrograms are encoded into vector-quantized representations, which enables powerful and efficient DDPM while maintaining its quality. Experimental comparisons on the publicly available datasets, CHB-MIT and TUH, show that our method delivers better results, significantly reduces inference time, and is suitable for deployment in a clinical environments. As far as we are aware, this is the first DDPM-based method for seizure anomaly detection. This novel approach significantly contributes to the progression of seizure detection algorithms, thereby augmenting their practicality in clinical settings.

OECT - Inspired electrical detection

Organic Electrochemical Transistors (OECTs) are integral in detecting human bioelectric signals, attributing their significance to distinct electrochemical properties, the utilization of soft materials, compact dimensions, and pronounced biocompatibility. This review traverses the technological evolution of OECT, highlighting its profound impact on non-invasive detection methodologies within the biomedicalfield. Four sensor types rooted in OECT technology were introduced: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Electrooculography (EOG), which hold promise for integration into wearable detection systems. The fundamental detection principles, material compositions, and functional attributes of these sensors are examined. Additionally, the performance metrics and delineates viable optimization strategies for assorted physiological electrical detection sensors are discussed. The overarching goal of this review is to foster deeper insights into the generation, propagation, and modulation of electrophysiological signals, thereby advancing the application and development of OECT in medical sciences.

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).

Measuring attention of ADHD patients by means of a computer game featuring biometrical data gathering

ADHD is a neurodevelopmental disorder diagnosed mainly in children, marked by inattention and hyperactivity-impulsivity. The symptoms are highly variable, such as different ages of onset and potential comorbidities, contributing to frequent misdiagnoses. Professionals note a gap in modern diagnostic tools, making accurate identification challenging. To address this, recent studies recommend gamification for better ADHD diagnosis and treatment, though further research is essential to confirm its efficacy. This work aims to create a serious game, namely "Attention Slackline", to assess attention levels. The game, designed with expert input, requires players to concentrate on a specific point to recognize specific patterns while managing distractions. A controlled experiment tested its precision, and results were compared with established attention tests by a correlation analysis. Statistical analysis confirmed the game's validity, especially in tracking attention through correct responses and errors. Preliminary evidence suggests that "Attention Slackline" may serve as a credible instrument for the assessment of attentional capacities in individuals with ADHD, given that its outcomes have been empirically shown to correlate with those derived from a well-established attention assessment methodology.

Flexible Dry Electrode Based on a Wrinkled Surface That Uses Carbon Nanotube/Polymer Composites for Recording Electroencephalograms

Electroencephalography (EEG) captures minute electrical signals emanating from the brain. These signals are vulnerable to interference from external noise and dynamic artifacts; hence, accurately recording such signals is challenging. Although dry electrodes are convenient, their signals are of limited quality; consequently, wet electrodes are predominantly used in EEG. Therefore, developing dry electrodes for accurately and stably recording EEG signals is crucial. In this study, we developed flexible dry electrodes using polydimethylsiloxane (PDMS)/carbon-nanotube (CNT) composites with isotropically wrinkled surfaces that effectively combine the advantages of wet and dry electrodes. Adjusting the PDMS crosslinker ratio led to good adhesion, resulting in a highly adhesive CNT/PDMS composite with a low Young's modulus that exhibited excellent electrical and mechanical properties owing to its ability to conformally contact skin. The isotropically wrinkled surface also effectively controls dynamic artifacts during EEG signal detection and ensures accurate signal analysis. The results of this study demonstrate that dry electrodes based on flexible CNT/PDMS composites and corrugated structures can outperform wet electrodes. The introduction of such electrodes is expected to enable the accurate analysis and monitoring of EEG signals in various scenarios, including clinical trials.

Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.

Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals

Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data. Specifically, this work aims to classify different mental disorders in the following ecological context accurately: (1) using raw EEG data, (2) collected during rest, (3) during both eye open, and eye closed conditions, (4) at short 2-min duration, (5) on participants with different psychiatric conditions, (6) with some overlapping symptoms, and (7) with strongly imbalanced classes. To tackle this challenge, we designed and optimized a transformer-based architecture, where class imbalance is addressed through focal loss and class weight balancing. Using the recently released TDBRAIN dataset (n= 1274 participants), our method classifies each participant as either a neurotypical or suffering from major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), subjective memory complaints (SMC), or obsessive-compulsive disorder (OCD). We evaluate the performance of the proposed architecture on both the window-level and the patient-level. The classification of the 2-min raw EEG data into five classes achieved a window-level accuracy of 63.2% and 65.8% for open and closed eye conditions, respectively. When the classification is limited to three main classes (MDD, ADHD, SMC), window level accuracy improved to 75.1% and 69.9% for eye open and eye closed conditions, respectively. Our work paves the way for developing novel AI-based methods for accurately diagnosing mental disorders using raw resting-state EEG data.

Preterm birth accelerates the maturation of spontaneous and resting activity in the visual cortex

Prematurity is among the leading risks for poor neurocognitive outcomes. The brains of preterm infants show alterations in structure and electrical activity, but the underlying circuit mechanisms are unclear. To address this, we performed a cross-species study of the electrophysiological activity in the visual cortices of prematurely born infants and mice. Using electroencephalography (EEG) in a sample of healthy preterm (N = 29) and term (N = 28) infants, we found that the maturation of the aperiodic EEG component was accelerated in the preterm cohort, with a significantly flatter 1/f slope when compared to the term infants. The flatter slope was a result of decreased spectral power in the theta and alpha bands and was correlated with the degree of prematurity. To determine the circuit and cellular changes that potentially mediate the changes in 1/f slope after preterm birth, we used in vivo electrophysiology in preterm mice and found that, similar to infants, preterm birth results in a flattened 1/f slope. We analyzed neuronal activity in the visual cortex of preterm (N = 6) and term (N = 9) mice and found suppressed spontaneous firing of neurons. Using immunohistochemistry, we further found an accelerated maturation of inhibitory circuits. In both preterm mice and infants, the functional maturation of the cortex was accelerated, underscoring birth as a critical checkpoint in cortical maturation. Our study points to a potential mechanism of preterm birth-related changes in resting neural activity, highlighting the utility of a cross-species approach in studying the neural circuit mechanisms of preterm birth-related neurodevelopmental conditions.

Preterm birth accelerates the maturation of spontaneous and resting activity in the visual cortex

Prematurity is among the leading risks for poor neurocognitive outcomes. The brains of preterm infants show alterations in structure and electrical activity, but the underlying circuit mechanisms are unclear. To address this, we performed a cross-species study of the electrophysiological activity in the visual cortices of prematurely born infants and mice. Using electroencephalography (EEG) in a sample of healthy preterm (N=29) and term (N=28) infants, we found that the maturation of the aperiodic EEG component was accelerated in the preterm cohort, with a significantly flatter 1/f slope when compared to the term infants. The flatter slope was a result of decreased spectral power in the theta and alpha bands and was correlated with the degree of prematurity. To determine the circuit and cellular changes that potentially mediate the changes in 1/f slope after preterm birth, we used in vivo electrophysiology in preterm mice and found that, similar to infants, preterm birth results in a flattened 1/f slope. We analyzed neuronal activity in the visual cortex of preterm mice (N=6 preterm and 9 term mice) and found suppressed spontaneous firing of neurons. Using immunohistochemistry, we further found an accelerated maturation of inhibitory circuits. In both preterm mice and infants, the functional maturation of the cortex was accelerated, underscoring birth as a critical checkpoint in cortical maturation. Our study points to a potential mechanism of preterm birth-related changes in resting neural activity, highlighting the utility of a cross-species approach in studying the neural circuit mechanisms of preterm birth-related neurodevelopmental conditions.

Recent Progress of Biomaterials-Based Epidermal Electronics for Healthcare Monitoring and Human-Machine Interaction

Epidermal electronics offer an important platform for various on-skin applications including electrophysiological signals monitoring and human-machine interactions (HMI), due to their unique advantages of intrinsic softness and conformal interfaces with skin. The widely used nondegradable synthetic materials may produce massive electronic waste to the ecosystem and bring safety issues to human skin. However, biomaterials extracted from nature are promising to act as a substitute material for the construction of epidermal electronics, owing to their diverse characteristics of biocompatibility, biodegradability, sustainability, low cost and natural abundance. Therefore, the development of natural biomaterials holds great prospects for advancement of high-performance sustainable epidermal electronics. Here, we review the recent development on different types of biomaterials including proteins and polysaccharides for multifunctional epidermal electronics. Subsequently, the applications of biomaterials-based epidermal electronics in electrophysiological monitoring and HMI are discussed, respectively. Finally, the development situation and future prospects of biomaterials-based epidermal electronics are summarized. We expect that this review can provide some inspirations for the development of future, sustainable, biomaterials-based epidermal electronics.

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.