Electroencephalography signals obtained from the brain‘s electrical activity are commonly used for the diagnosis of neurological diseases. These signals indicate the electrical activity in the brain and contain information about the brain. Epilepsy, one of the most important diseases in the brain, manifests itself as a result of abnormal pathological oscillating activity of a group of neurons in the brain. Automated systems that employed the electroencephalography signals are being developed for the assessment and diagnosis of epileptic seizures. The aim of this study is to focus on the effectiveness of stacking ensemble approach based model for predicting whether there is epileptic seizure or not. So, this study enables the readers and researchers to examine the proposed stacking ensemble model. The benchmark clinical dataset provided by Bonn University was used to assess the proposed model. Comparative experiments were conducted by utilizing the proposed model and the base deep neural networks model to show the effectiveness of the proposed model for seizure detection. Experiments show that the proposed model is proven to be competitive to base DNN model. The results indicate that the performance of the epileptic seizure detection by the stacking ensemble based deep neural networks model is high; especially the average accuracy value of 97.17%. Also, its average sensitivity with 93.11% is superior to the base DNN model. Thus, it can be said that the proposed model can be included in an expert system or decision support system. In this context, this system would be precious for the clinical diagnosis and treatment of epilepsy.

Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection

A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

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A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction

Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features

The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

https://www.mdpi.com/1424-8220/20/17/4952/pdf

Xiao Zheng

Papers

Ensemble deep learning for automated visual classification using EEG signals

An Attention-based Bi-LSTM Method for Visual Object Classification via EEG

Decoding human brain activity with deep learning

Automatic focal and non-focal EEG detection using entropy-based features from flexible analytic wavelet transform

Developing multi-component dictionary-based sparse representation for automatic detection of epileptic EEG spikes