Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks [Formula presented]

For epileptic seizure detection and prediction, to address the computational bottleneck of the von Neumann architecture, we develop an in-memory memristive crossbar-based accelerator simulator. The simulator software is composed of a Python-based neural network training component and a MATLAB-based memristive crossbar array component. The software provides a baseline network for developing deep learning-based signal processing tasks, as well as a platform to investigate the impact of weight mapping schemes and device and peripheral circuitry non-idealities

Universal EEG Encoder for Learning Diverse Intelligent Tasks

Brain Computer Interfaces (BCI) have become very popular with Electroencephalography (EEG) being one of the most commonly used signal acquisition techniques. A major challenge in BCI studies is the individualistic analysis required for each task. Thus, task-specific feature extraction and classification are performed, which fails to generalize to other tasks with similar time-series EEG input data. To this end, we design a GRU-based universal deep encoding architecture to extract meaningful features from publicly available datasets for five diverse EEG-based classification tasks. Our network can generate task and format-independent data representation and outperform the state of the art EEGNet architecture on most experiments. We also compare our results with CNN-based, and Autoencoder networks, in turn performing local, spatial, temporal and unsupervised analysis on the data

Brain Stroke Detection Using ANN Based On EEG Signals Using CNN ‎Path

تحدث السكتة الدماغية بسبب انسداد في الشريان الذي ينقل الدم المؤكسج إلى الدماغ. السكتة الدماغية الحادة هي السكتة الدماغية الأكثر شيوعًا. يمكن أن يكون الاكتشاف المبكر للسكتة الدماغية منقذاً لحياة المرضى. تخطيط كهربية الدماغ هو تقنية لتحليل الأنشطة الكهربائية الموجودة في الأجزاء المختلفة من الدماغ البشري، وباستخدام التتبع البصري، فإنه يسجل هذه الأنشطة. يوفر EEG قياسات فعالة من حيث التكلفة ومحمولة وعالية التردد ودقيقة مقارنة بأدوات مراقبة نشاط الموجات الدماغية الأخرى. يستخدم مخطط كهربية الدماغ لتشخيص متلازمة حساسية الاندروجين. في البحث المقترح، تم تطبيق الشبكة العصبية التلافيفية لتصنيف شدة السكتة الدماغية. في هذه الخوارزمية ، يتم حساب الكثافة الطيفية للطاقة (PSD) لإشارات مخطط كهربية الدماغ بناءً على الميزات المستخرجة من الشبكة العصبية الاصطناعية. ثم تم تدريب خريطة المعالم لتصنيف البيانات إلى أربع حالات بناءً على شدة السكتة الدماغية. بالنسبة لتحليل الأداء، تتم مقارنة الخوارزمية المقترحة مع الخوارزميات الموجودة، ويلاحظ أن دقة الخوارزمية المقترحة هي 98.3٪، وهي أفضل من الخوارزمية الموجودة للكشف عن السكتة الدماغية.Brain stroke occurs because of a blockage in the artery, which delivers oxygenated blood to the brain. Acute Ischemic Stroke (AIS) is mostly occurred brain stroke. Early detection of brain stroke can be life-saving for patients. Electroencephalography is a technique to analyze electrical activities present in the different parts of the human brain, and using visual trace, it records these activities. EEG provides cost-effective, portable, high-frequency and accurate measurement as compared to other brain wave activity monitoring tools. EEG is used to diagnose AIS. In the proposed research, the convolutional neural network is applied for the classification of stroke severity. In this algorithm, the power spectral density (PSD) of EEG signals is calculated based on the extracted features from the artificial neural network. The feature map was then trained to classify the data into four instances based on the severity of the brain stroke. The effectiveness of the suggested algorithm is examined by comparing it with several similar algorithms., and it is observed that the accuracy of the proposed algorithm is 98.3% and which is better than the existing algorithm for brain stroke detection

Classification of EEG by a multi-layer reservoir neural network based on asynchronous cellular automaton neurons

In this paper, a multi-layer reservoir neural network is designed using an asynchronous cellular automaton neuron model. Furthermore, a learning method of the network based on the simulated annealing is proposed. It is shown that the network with reservoir layers can classify a set of several EEG. In addition, the classification performance of networks with various configurations were compared, and it is shown the best performing network is a two-layer reservoir neural network

Application of machine-learning methods to recognize mitoBK channels from different cell types based on the experimental patch-clamp results

(1) Background: In this work, we focus on the activity of large-conductance voltage- and Ca2+-activated potassium channels (BK) from the inner mitochondrial membrane (mitoBK). The characteristic electrophysiological features of the mitoBK channels are relatively high single-channel conductance (ca. 300 pS) and types of activating and deactivating stimuli. Nevertheless, depending on the isoformal composition of mitoBK channels in a given membrane patch and the type of auxiliary regulatory subunits (which can be co-assembled to the mitoBK channel protein) the characteristics of conformational dynamics of the channel protein can be altered. Consequently, the individual features of experimental series describing single-channel activity obtained by patch-clamp method can also vary. (2) Methods: Artificial intelligence approaches (deep learning) were used to classify the patch-clamp outputs of mitoBK activity from different cell types. (3) Results: Application of the K-nearest neighbors algorithm (KNN) and the autoencoder neural network allowed to perform the classification of the electrophysiological signals with a very good accuracy, which indicates that the conformational dynamics of the analyzed mitoBK channels from different cell types significantly differs. (4) Conclusion: We displayed the utility of machine-learning methodology in the research of ion channel gating, even in cases when the behavior of very similar microbiosystems is analyzed. A short excerpt from the patch-clamp recording can serve as a “fingerprint” used to recognize the mitoBK gating dynamics in the patches of membrane from different cell types

Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals

Lightweight Machine Learning with Brain Signals

Electroencephalography(EEG) signals are gaining popularity in Brain-Computer Interface(BCI) systems and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks(CNNs) or graph neural networks(GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account; 3) navigating and training deep learning model with the most critical EEG channels; 4) detecting most similar EEG segments with target subject to reduce the cost of computation as well as inter-subject variability. Specifically, we construct a task-adaptive graph representation of brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. Lastly, contributory EEG segments are detected by several similarity estimation metrics, we then evaluate and train our proposed framework upon detected EEG segments to compare the performance of different metrics in EEG BCI tasks. We empirically show that our proposed approach, SIFT-EEG, outperforms state-of-the-art, with around 4% and 7% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data. Moreover, the best-performed metric can achieve a high level of accuracy with less than 9% training data, suggesting a possible shift in direction for future works other than simply scaling up the model