Representation Of EEG Signals Research Articles

Short-time Fourier transform and embedding method for recurrence quantification analysis of EEG time series

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. Creating features useful for the analysis of the EEG recording can be challenging. Here we introduce a new method of pre-processing the time-series for the analysis of the resting state and binary task classification using recurrence quantification analysis (RQA) and compare it with the existing state-of-the-art approach based on signal embedding. To reveal patterns that unfold brain dynamics, we present a new pipeline that does not rely on selection of embedding parameters for RQA. Instead of using EEG time-series signals directly, Short-term Fourier transform (STFT) is used to generate new time-series, based on the power spectra from sliding, overlapping windows. Recurrence plots are created in a standard way from embedded EEG signals, and the STFT vectors. The efficiency of RQA features extracted from such plots is compared in classification of EEG segments that correspond to open and closed eye conditions. In contrast to the common approaches to such analysis, no filtering into separate frequency bands was needed. Differences between the two representations of EEG signals are illustrated using histograms of RQA features and UMAP plots. Classification results at the 95.9% level were obtained using selected features for less than 10 electrodes.

Durağan Durum Görsel Uyarılmış Potansiyellerin Uyarı Frekansını Kestirmek için Wigner-Ville Dağılım Özniteliklerinin Değerlendirilmesi

Brain Computer Interface (BCI) is a system that enables people to communicate with the outside world and control various electronic devices by interpreting only brain activity (motor movement imagination, emotional state, any focused visual or auditory stimulus, etc.). The visual stimulation based recording is one of the most popular methods among various electroencephalography (EEG) recording methods. Steady-state visual-evoked potentials (SSVEPs) where visual objects are blinking at a fixed frequency play an important role due to their high signal-to-noise ratio and higher information transfer rate in BCI applications. However, the design of multiple (more than 3) commands systems in SSVEPs based BCI systems is limited. The different approaches are recommended to overcome these problems. In this study, an approach based on machine learning is proposed to determine stimulating frequency in SSVEP signals. The data set (AVI SSVEP Dataset) is obtained through open access from the internet for simulations. The dataset includes EEG signals that was recorded when subjects looked at a flickering frequency at seven different frequencies (6-6.5-7-7.5-8.2-9.3-10Hz). In the machine learning-based approach Wigner-Ville Distribution (WVD) is used and features are extracted using Time-Frequency (TF) representations of EEG signals. These features are classified by Decision Tree, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes, Ensemble Learning classifiers. Simulation results demonstrate that the proposed approach achieved promising accuracy rates for 7 command SSVEP systems. As a consequence, the maximum accuracy is achieved in the Ensemble Learning classifier with 47.60%.

SleepPrintNet: A Multivariate Multimodal Neural Network Based on Physiological Time-Series for Automatic Sleep Staging

Sleep is one of the most fundamental physiological activities of human beings. Sleep assessment based on physiological time-series can efficiently assist human experts to diagnose the sleep health of people. However, most of the existing methods only considered one or two kinds of time-domain, frequency-domain, and spatial-domain information from electroencephalogram (EEG). Besides, existing deep learning methods share the feature extraction module of EEG with other modalities, which ignore the discriminative features of electrooculogram (EOG) and electromyography (EMG). Therefore, how to make full use of the complementarity of different features of EEG and capture the discriminative features from other modalities is challenging. To tackle this challenge, we design SleepPrintNet to capture the SleepPrint in physiological time-series, which represents the complementarity among different features of EEG and discriminative features from other modalities in different sleep stages. SleepPrintNet consists of an EEG temporal feature extraction module, an EEG spectral-spatial feature extraction module for the temporal-spectral-spatial representation of EEG signals, and two multimodal feature extraction modules including EOG and EMG feature extraction module. To the best of our knowledge, it is the first attempt to integrate EEG temporal-spectral-spatial as well as the multimodal features simultaneously in a unified model for sleep staging. Experiments on the benchmark dataset MASS-SS3 demonstrate that SleepPrintNet outperforms all baseline models. The implementation code of SleepPrintNet is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xiyangcai/SleepPrintNet</uri> . <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement–</i> Sleep staging helps sleep experts assess sleep quality and diagnose sleep health. The polysomnography, which contains the physiological signal recordings during sleep, is a kind of multimodal multivariant physiological time-series for sleep staging. However, existing methods treat physiological time-series from different parts of the body equally, which ignore the abundant information in multimodal signals. The SleepPrintNet proposed in this article has improved the accuracy of sleep staging with the help of the discriminative characteristics from different physiological time-series, which is made up of several independent modules for the extraction of different modalities signals. SleepPrintNet is also a universal framework for the classification of multivariate and multimodal signals. It can be applied in the diagnosis and treatment of other diseases based on physiological signals. The high-accuracy classification of physiological signals has great significance in the field of intelligent medical diagnostics.

Robust Approach Based on Convolutional Neural Networks for Identification of Focal EEG Signals

Electroencephalogram (EEG) signals provide important information for the identification of the epileptogenic area. Identification of focal EEG signals locates the epileptogenic area, which is an important task for successful surgery. In this article, a convolutional neural networks (CNNs) based framework is proposed for automatic identification of focal EEG signals. The proposed intelligent system initially uses a window for randomly segment the input EEG signals. The short-time Fourier transform is applied on to the segmented EEG signals for conversion of input EEG signals into time-frequency (T-F) representation. The T-F representation of EEG signals is used as T-F images. Instead of training an end-to-end CNNs which necessitates more input images and time, we opt to use a pretrained CNNs model for transfer learning. Specifically, deep feature extraction (DFE) is employed for acquiring the more convenient features from the input EEG images. The deep features extracted from AlexNet, VGG16, VGG19, and Resnet50 models are used as input to different variants of the k-nearest neighbor (k-NN) classifier. The conducted experimental works show that AlexNet, VGG16, and Resnet50 achieve promising results. Specifically, the fc6 layers of AlexNet, VGG16, and fc1000 layer of Resnet50 produce a 99.8 $\%$ accuracy score with the weighted-k-NN approach. The comparative study shows that the proposed method provides better performance in comparison to state-of-the-art methods.

Analysis of translation invariant wavelet transform for epileptic seizure detection using support vector machine

Electroencephalogram (EEG) signal of human provide important information about the changes in the brain. Based on the information, epileptic seizure can be easily identified. In this paper, Translation Invariant Wavelet Transform (TIWT) based EEG signal classification is presented. At first, signals are represented by TIWT to extract information. TIWT representation of EEG signal produces Low Frequency Band (LFB) and High Frequency Band (HFB). From LFB and HFB features are extracted and Support Vector Machine (SVM) classifier evaluates them into either Normal Signal (NS) or Epileptic Signal (ES). Experimental results prove that TIWT representation of EEG signals provides average classification accuracy of 97.5% at 3rd level representation.

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

Reconstruction of EEG Signal Based on Compressed Sensing and Wavelet Transform

This paper, both theoretically and numerically, investigates an effective reconstruction of EEG signal. An optimization model is presented, which unifies different sparse signals. The model is solved by employing the proximal algorithm. Based on the theoretical analysis, the simulation of EEG signal is performed. Sparse representation of EEG signal is got by the technique of wavelet transform and the signal denoising is also obtained. Then, by using compressed sensing, the EEG signal is reconstructed. Our results show that the reconstructed signal is in good agreement with the original signal and retains the leading characteristic.