Diagnosis Of Brain Disorders Research Articles

SPECTnet: a deep learning neural network for SPECT image reconstruction

BackgroundSingle photon emission computed tomography (SPECT) is an important functional tool for clinical diagnosis and scientific research of brain disorders, but suffers from limited spatial resolution and high noise due to hardware design and imaging physics. The present study is to develop a deep learning technique for SPECT image reconstruction that directly converts raw projection data to image with high resolution and low noise, while an efficient training method specifically applicable to medical image reconstruction is presented.MethodsA custom software was developed to generate 20,000 2-D brain phantoms, of which 16,000 were used to train the neural network, 2,000 for validation, and the final 2,000 for testing. To reduce development difficulty, a two-step training strategy for network design was adopted. We first compressed full-size activity image (128×128 pixels) to a one-D vector consisting of 256×1 pixels, accomplished by an autoencoder (AE) consisting of an encoder and a decoder. The vector is a good representation of the full-size image in a lower-dimensional space and was used as a compact label to develop the second network that maps between the projection-data domain and the vector domain. Since the label had 256 pixels only, the second network was compact and easy to converge. The second network, when successfully developed, was connected to the decoder (a portion of AE) to decompress the vector to a regular 128×128 image. Therefore, a complex network was essentially divided into two compact neural networks trained separately in sequence but eventually connectable.ResultsA total of 2,000 test examples, a synthetic brain phantom, and de-identified patient data were used to validate SPECTnet. Results obtained from SPECTnet were compared with those obtained from our clinic OS-EM method. Images with lower noise and more accurate information in the uptake areas were obtained by SPECTnet.ConclusionsThe challenge of developing a complex deep neural network is reduced by training two separate compact connectable networks. The combination of the two networks forms the full version of SPECTnet. Results show that the developed neural network can produce more accurate SPECT images.

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution.

The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.

Integration of Multimodal Data for Deciphering Brain Disorders.

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

The Application of Nanotechnology for the Diagnosis and Treatment of Brain Diseases and Disorders.

Brain is by far the most complex organ in the body. It is involved in the regulation of cognitive, behavioral, and emotional activities. The organ is also a target for many diseases and disorders ranging from injuries to cancers and neurodegenerative diseases. Brain diseases are the main causes of disability and one of the leading causes of deaths. Several drugs that have shown potential in improving brain structure and functioning in animal models face many challenges including the delivery, specificity, and toxicity. For many years, researchers have been facing challenge of developing drugs that can cross the physical (blood–brain barrier), electrical, and chemical barriers of the brain and target the desired region with few adverse events. In recent years, nanotechnology emerged as an important technique for modifying and manipulating different objects at the molecular level to obtain desired features. The technique has proven to be useful in diagnosis as well as treatments of brain diseases and disorders by facilitating the delivery of drugs and improving their efficacy. As the subject is still hot, and new research findings are emerging, it is clear that nanotechnology could upgrade health care systems by providing easy and highly efficient diagnostic and treatment methods. In this review, we will focus on the application of nanotechnology in the diagnosis and treatment of brain diseases and disorders by illuminating the potential of nanoparticles.

A multi-modal fusion of features method based on deep belief networks to diagnosis schizophrenia disease

Computer-based diagnostics of schizophrenia (SZ) have been recently the subject of intense interest. Some recent studies have shown promising results in the diagnosis of this disorder by the process and analysis of neuroimaging data, such as functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI). Each of these neuroimaging data provides different brain structural and functional features that integrating them can help accurately diagnose brain disorders. In this study, we propose a deep learning architecture to combine functional brain data with two different types of structural brain data to accurately diagnose SZ. Our two-step model consists of deep belief networks (DBN) and a fusing rule to extract and learn hidden features via the combining resting-state fMRI with both sMRI and DTI data. We examine this architecture on all combinations of features extracted from fMRI, MRI and DTI. The experimental results indicate that multi-modal fusion improved classification accuracy between the SZ and the healthy cases. By finding a suitable configuration for algorithm parameters, the proposed method showed an accuracy level of 99.36% for the COBRE dataset and outperformed similar state-of-the-art works.

A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity.

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.

Deep Learning Algorithms in EEG Signal Decoding Application: A Review

In recent years, deep learning algorithms have been developed rapidly, and they are becoming a powerful tool in biomedical engineering. Especially, there has been an increasing focus on the use of deep learning algorithms for decoding physiological or pathological status of the brain from electroencephalographic (EEG). This paper overviews current application of deep learning algorithms in various EEG decoding tasks, and introduces commonly used algorithms, typical application scenarios, important progresses and existing problems. Firstly, the basic principles of deep learning algorithms used in EEG decoding is briefly described, including convolutional neural network, deep belief network, auto-encoder and recurrent neural network. In this paper, existing applications of deep learning on EEG is discussed, including brain-computer interfaces, cognitive neuroscience and diagnosis of brain disorders. Finally, this paper outlines some key problems that will be addressed in future applications of deep learning for EEG decoding, such as parameter selection, computational complexity, and the capability of generalization.

Cerebral palsy rehabilitation - effectiveness of visual stimulation method by analysing the quantitative assessment of oculomotor abnormalities

Cerebral palsy (CP) is a developmental brain abnormality which occur prior to birth or during birth and after delivery. The major health issue for CP children is vision problem. The proposed approach deals with vision dysfunction and oculomotor assessment for diagnosis and treatment of brain disorder. In these approaches, we assessed the oculomotor deficits of CP children by recording the eye movement of 26 CP children (age ranges from 4-14) and the performance was compared with age matched control and we also analysed the CP children eye fixation centroid, smooth pursuit and eyelid blinking activities. From these activities, the movement of the eye provides the window of neuroplasticity for CP children. The oculomotor abnormalities indicating the lesion brain of CP children retains the ability to reorganise. Continuous work process of these vision stimulation task techniques for eye gaze direction approach will improve the cognitive rehabilitation of CP children.

Automated Cognitive Workload Assessment Using Logical Teaching Learning-Based Optimization and PROMETHEE Multi-Criteria Decision Making Approach

Cognitive workload assessment plays an important role to examine the mental status of brain for emergency situations faced by air traffic controllers, military personals and many more. It is also significant for brain disorder diagnosis and psychological health monitoring. Mental workload induced by various factors needs to be critically examined for better decisions. This paper presents an automated model for cognitive workload assessment to provide accurate categorization on intensity of workload induced by multitasking situations. Statistical wavelets corresponding to brain frequencies act as features for categorization task. The brain analysis needs technical expertise to identify the relevant information but, due to lack of sufficient number of experts, feature selection model helps to identify the relevant features responsible to categorize cognitive workload. Teaching Learning based Optimization (TLBO) method has been modified using ‘Logical Operators’ for binary feature selection problem in the current study and evaluated based on different parameters. Results on Logical TLBO reached upto 100% for binary and multiclass classification of cognitive workload. The results also show the proposed method is converging in minimum iteration for multiclass cognitive workload. PROMETHEE based multi-criteria decision-making approach has adopted for ranking of algorithm to select the best algorithm when multiple alternatives are available. Based on study of current literature, this approach appears to be first time implemented to provide ranking of optimization algorithms. The PROMETHEE method also suggests that the Logical TLBO is best among all the other compared approaches.

Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction

PurposeRecently, brain connectivity networks have been used for the classification of neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease (AD). Network analysis provides a new way for exploring the association between brain functional deficits and the underlying structural disruption related to brain disorders. Network embedding learning that aims to automatically learn low-dimensional representations for brain networks has drawn increasing attention in recent years. MethodIn this work we build upon graph neural network in order to learn useful representations for graph classification in an end-to-end fashion. Specifically, we propose a hierarchical GCN framework (called hi-GCN) to learn the graph feature embedding while considering the network topology information and subject's association at the same time. ResultsTo demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset. Extensive experiments on ABIDE and ADNI datasets have demonstrated competitive performance of the hi-GCN model. Specifically, we obtain an average accuracy of 73.1%/78.5% as well as AUC of 82.3%/86.5% on ABIDE/ADNI. The comprehensive experiments demonstrate that our hi-GCN is effective for graph classification with brain disorders diagnosis. ConclusionThe proposed hi-GCN method performs the graph embedding learning from a hierarchical perspective while considering the structure in individual brain network and the subject's correlation in the global population network, which can capture the most essential embedding features to improve the classification performance of disease diagnosis. Moreover, the proposed jointly optimizing strategy also achieves faster training and easier convergence than both the hi-GCN with pre-training and two-step supervision.

GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.

Deep learning for brain disorder diagnosis based on fMRI images

In modern neuroscience and clinical study, neuroscientists and clinicians often use non-invasive imaging techniques to validate theories and computational models, observe brain activities and diagnose brain disorders. The functional Magnetic Resonance Imaging (fMRI) is one of the commonly-used imaging modalities that can be used to understand human brain mechanisms as well as the diagnosis and treatment of brain disorders. The advances in artificial intelligence and the emergence of deep learning techniques have shown promising results to better interpret fMRI data. Deep learning techniques have rapidly become the state of the art for analyzing fMRI data sets and resulted in performance improvements in diverse fMRI applications. Deep learning is normally presented as an end-to-end learning process and can alleviate feature engineering requirements and hence reduce domain knowledge requirements to some extent. Under the framework of deep learning, fMRI data can be considered as images, time series or images series. Hence, different deep learning models such as convolutional neural networks, recurrent neural network, or a combination of both, can be developed to process fMRI data for different tasks. In this review, we discussed the basics of deep learning methods and focused on its successful implementations for brain disorder diagnosis based on fMRI images. The goal is to provide a high-level overview of brain disorder diagnosis with fMRI images from the perspective of deep learning applications.

Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

Message from the Chief Editor Vol.1(1)

Brain and Spine Foundation Nepal is a not-for profit organization registered (Registration No. 226156/076/077) on 13th Ashwin 2076. The Foundation aims to work with all those involved in prevention, diagnosis and treatment of brain and spine disorders and diseases. One of the guiding principle of the foundation is dissemination of medical information related to these diseases.
 In Nepal, there are two neurosurgical journals being regularly published Nepal Journal of Neuroscience (NJNS, ISSN 1813-1948), official journal of Nepalese Society of Neurosurgeons (NESON) and Eastern Green Neurosurgery (EGN, ISSN 2705-4470), published by Department of Neurosurgery, B and C Medical College and Teaching Hospital, Birtamode. NJNS is being published since January 2004 and EGN is being published since April 2019. However these journals accept articles mainly from neurologists and neurosurgeons.
 The objective of any journal is to provide information about various research projects, interaction with own fraternity and to publish scientific articles of research and clinical practice. We at the Foundation believe that the neurological problems should be tackled with multidimensional probe. We focus on awareness programs, information dissemination and involve other healthcare workers like physiotherapists, physiatrists, neuro nurses, emergency physicians, neuro-anaesthesiologists, pain physicians etc. We aim to publish this journal twice a year, on March and on September.
 We request all healthcare workers who take care of brain and spine patients to send articles related to their work. You can send case reports, review articles and original articles for publication in this journal.

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.

Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.

Scalable and energy efficient seizure detection based on direct use of compressively-sensed EEG data on an ultra low power multi-core architecture

Extracting information from dense multi-channel neural sensors for accurate diagnosis of brain disorders necessitates computationally expensive and advanced signal processing approaches to analyze the massive volume of recorded data. Compressive Sensing (CS) is an efficient method for reducing the computational complexity and power consumption in the resource-constrained multi-site neural systems. However, reconstructing the signal from compressed measurements is computationally intensive, making it unsuitable for real-time applications such as seizure detection. In this paper, a seizure detection algorithm is proposed to overcome these limitations by circumventing the reconstruction phase and directly processing the compressively sampled EEG signals. The Lomb-Scargle Periodogram (LSP) is used to extract the spectral energy features of the compressed data. Performance of the seizure detector using non-linear support vector machine (SVM) classifier, tested on 24 patients of the CHB-MIT data-set for compression ratios (CR) of 1–64x, is 96–93%, 92-87%, 0.95–0.91, and <1 s for sensitivity, accuracy, the area under the curve, and latency, respectively. A power-efficient classification method based on the utilization of dual linear SVM classifiers is proposed. The proposed classification method based on the dual linear SVM classification achieved better classification performance compared to commonly used classifiers, such as K-nearest neighbor, random forest, artificial neural network, and linear SVM, while consuming low power in comparison to non-linear SVM kernels. The hardware-optimized implementation of this algorithm is proposed on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. Optimized implementation of this algorithm on Mr. Wolf platform leads to detecting a seizure with an energy budget of 18.4 μJ and 3.9 μJ for a compression ratio of 24x using non-linear SVM classifier and the dual linear SVM based classification method, respectively.

Convolutional Sparse Coded Dynamic Brain Functional Connectivity

Functional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications.

Glutamate Chemical Exchange Saturation Transfer (GluCEST) Magnetic Resonance Imaging in Pre-clinical and Clinical Applications for Encephalitis.

BackgroundEncephalitis is a common central nervous system inflammatory disease that seriously endangers human health owing to the lack of effective diagnostic methods, which leads to a high rate of misdiagnosis and mortality. Glutamate is implicated closely in microglial activation, and activated microglia are key players in encephalitis. Hence, using glutamate chemical exchange saturation transfer (GluCEST) imaging for the early diagnosis of encephalitis holds promise.MethodsThe sensitivity of GluCEST imaging with different concentrations of glutamate and other major metabolites in the brain was validated in phantoms. Twenty-seven Sprague–Dawley (SD) rats with encephalitis induced by Staphylococcus aureus infection were used for preclinical research of GluCEST imaging in a 7.0-Tesla scanner. For the clinical study, six patients with encephalitis, six patients with lacunar infarction, and six healthy volunteers underwent GluCEST imaging in a 3.0-Tesla scanner.ResultsThe number of amine protons on glutamate that had a chemical shift of 3.0 ppm away from bulk water and the signal intensity of GluCEST were concentration-dependent. Under physiological conditions, glutamate is the main contributor to the GluCEST signal. Compared with normal tissue, in both rats and patients with encephalitis, the encephalitis areas demonstrated a hyper-intense GluCEST signal, while the lacunar infarction had a decreased GluCEST signal intensity. After intravenous immunoglobulin therapy, patients with encephalitis lesions showed a decrease in GluCEST signal, and the results were significantly different from the pre-treatment signal (1.34 ± 0.31 vs 5.0 ± 0.27%, respectively; p = 0.000).ConclusionGlutamate plays a role in encephalitis, and the GluCEST imaging signal has potential as an in vivo imaging biomarker for the early diagnosis of encephalitis. GluCEST will provide new insight into encephalitis and help improve the differential diagnosis of brain disorders.

Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges

Machine learning is an artificial intelligence technique that allows computers to perform a task without being explicitly programmed. Machine learning can be used to assist diagnosis and prognosis of brain disorders. Although the earliest articles date from more than ten years ago, research increases at a very fast pace. Recent works using machine learning for diagnosis have moved from classification of a given disease versus controls to differential diagnosis. Intense research has been devoted to the prediction of the future patient state. Although a lot of earlier works focused on neuroimaging as data source, the current trend is on the integration of multimodal data. In terms of targeted diseases, dementia remains dominant but approaches have been developed for a wide variety of neurological and psychiatric diseases. Machine learning is extremely promising for assisting diagnosis and prognosis in brain disorders. Nevertheless, we argue that key challenges remain to be addressed by the community for bringing these tools in clinical routine: good practices regarding validation and reproducible research need to be more widely adopted; extensive generalization studies are required; interpretable models are needed to overcome the limitations of black-box approaches.

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification. The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand. The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches. The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics.