Query: EEG signals wheelchair

title: EEG Wheelchair for People of Determination
abstract: the aim of this paper is to design and construct an electroencephalograph (EEG) based brain controlled wheelchair to provide a communication bridge from the nervous system to the external technical device for people of determination or individuals suffering from partial or complete paralysis. EEG is a technique that reads the activity of the brain by capturing brain signals non invasively using a special EEG headset. The signals acquired go through pre processing, feature extraction and classification. This technique allows human thoughts alone to be converted to control the wheelchair. The commands used are moving to the right, left, forward, and backward and stop. The brain signals are acquired using the Emotiv Epoc headset. Discrete Wavelet Transform is used for feature extraction and Support Vector Machine (SVM) is used for classification.

title: Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals
abstract: Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed. In this study, an effective autism diagnostic system using electroencephalogram (EEG) signals, which are generated from electrical activity in the brain, was developed and characterized. The pre processed signals were converted to two dimensional images using the higher order spectra (HOS) bispectrum. Nonlinear features were extracted thereafter, and then reduced using locality sensitivity discriminant analysis (LSDA) Significant features were selected from the condensed feature set using Student's t test, and were then input to different classifiers. The probabilistic neural network (PNN) classifier achieved the highest accuracy of 98.70% with just five features. Ten fold cross validation was employed to evaluate the performance of the classifier. It was shown that the developed system can be useful as a decision support tool to assist healthcare professionals in diagnosing autism.

title: A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI
abstract: In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4 core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.

title: Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers
abstract: Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54 DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM) K Nearest Neighbors (KNN) Artificial Neural Network (ANN) and Naive Bayes (NB) The performance of 14 different combinations of two class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54 DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers.

title: Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
abstract: With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state of the art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.

title: Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis
abstract: This paper evaluates the approach of imaging timeseries data such as EEG in the diagnosis of epilepsy through Deep Neural Network (DNN) EEG signal is transformed into an RGB image using Gramian Angular Summation Field (GASF) Many such EEG epochs are transformed into GASF images for the normal and focal EEG signals. Then, some of the widely used Deep Neural Networks for image classification problems are used here to detect the focal GASF images. Three pre trained DNN such as the AlexNet, VGG16, and VGG19 are validated for epilepsy detection based on the transfer learning approach. Furthermore, the textural features are extracted from GASF images, and prominent features are selected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, a Custom Convolutional Neural Network (CNN) with three CNN layers, Batch Normalization, Max pooling layer, and Dense layers, is proposed for epilepsy diagnosis from GASF images. The results of this paper show that the Custom CNN model was able to discriminate against the focal and normal GASF images with an average peak Precision of 0.885, Recall of 0.92, and F1 score of 0.90. Moreover, the Area Under the Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve is 0.92 for the Custom CNN model. This paper suggests that Deep Learning methods widely used in image classification problems can be an alternative approach for epilepsy detection from EEG signals through GASF images.

title: Deep C LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High Dimension EEG Signals
abstract: Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML) based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real time epileptic seizure detection. In this paper, a novel deep convolutional long short term memory (C LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close) It achieves to predict a result in every 0.006 seconds with a short detection duration (one second) By comparing with other two types deep learning approaches (DCNN and LSTM) the presented deep C LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%

title: Decoding Movement Imagination and Execution From Eeg Signals Using Bci Transfer Learning Method Based on Relation Network
abstract: A brain computer interface (BCI) is used to control external devices for healthy people as well as to rehabilitate motor functions for motor disabled patients. Decoding movement intention is one of the most significant aspects for performing arm movement tasks using brain signals. Decoding movement execution (ME) from electroencephalogram (EEG) signals have shown high performance in previous works, however movement imagination (MI) paradigm based intention decoding has so far failed to achieve sufficient accuracy. In this study, we focused on a robust MI decoding method with transfer learning for the ME and MI paradigm. We acquired EEG data related to arm reaching for 3D directions. We proposed a BCI transfer learning method based on a Relation network (BTRN) architecture. Decoding performances showed the highest performance compared to conventional works. We confirmed the possibility of the BTRN architecture to contribute to continuous decoding of MI using ME datasets.

title: Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting
abstract: Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost) named CEEMD XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (IMFs) and residues. Secondly, the multi domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB MIT (Children's Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy.

title: Prediction of gait intention from pre movement EEG signals: a feasibility study
abstract: Prediction of Gait intention from pre movement Electroencephalography (EEG) signals is a vital step in developing a real time Brain computer Interface (BCI) for a proper neuro rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence.  An eight channel, custom made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform based method was employed to capture event related information in alpha and beta bands in the time frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a ten fold cross validation scheme.  Using a fully predictive intention detection system, 76.41+ 4.47 accuracy, 72.85+ 7.48 sensitivity, and 79.93+ 5.50 specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12+ 4.12 70.24+ 6.45 and 77.78+ 7.01 respectively. Overall average True Positive Rate achieved by this methodology was 72.06+ 8.27 with 1.45 False Positives/min.  Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre movement EEG features or trans movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre movement EEG signal to be applied in real life prosthetic and neuro rehabilitation systems.