Review of Techniques for Predicting Epileptic Seizure using EEG Signals

Epilepsy is a disorder that is characterized by seizures. Seizures are caused due to unusual electrical activity in the brain. Electroencephalogram (EEG) is used to read brain signal in form of 5 sub-bands viz. Alpha, Beta, Gamma, Theta and Delta. The features within each of theses sub-bands can be analysed and processed upon to predict the onset of a seizure. By accurate prediction of seizures, we can take preventive measures such as providing medication to reduce the severity of suffering of the patient. This pape r reviews the different techniques by which we can predict the onset of epileptic seizure using EEG signals. Each method utilizes one or more sub-bands of the EEG signal and classifies the patient records based on the features extracted through that set of sub-bands. Every method uses a different way to extract the sub bands. Also different classification algorithms are used in every method. We compare t e performance of each technique and analyse their efficacy

Feature Extraction of Visual Evoked Potentials Using Wavelet Transform and Singular Value Decomposition

Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data of visual evoked potentials and extract time-locked signals with external visual stimulation. A bio-amplifier (iERG 100P) and data acquisition system (OMB-DAQ-3000) were utilized to record EEG raw data from the human scalp. MATLAB Data Acquisition Toolbox, Wavelet Toolbox, and Simulink model were employed to analyze EEG signals and extract VEP responses. Results: Results were verified in Simulink environment for the real recorded EEG data. The proposed model allowed precise decomposition and classification of VEP signals through the combined operation of DWT and SVD. DWT was successfully used for the decomposition of VEP signals to different frequencies followed by SVD for feature extraction and classification. Conclusion: The visual and quantitative results indicated that the impact of the proposed technique of combining DWT and SVD was promising. Combining the two techniques led to a two-fold increase in the impact of peak signal to noise ratio of all the tested signals compared to using each technique individually

The classification of wink-based eeg signals by means of transfer learning models

Stroke is one of the dominant causes of impairme nt. An estimation of half post-stroke survivors suffer from a severe motor or cognitive deterioration, that affects the functionality of the affected parts of the body, which in turn, prevents the patients from carrying out Activities of Daily Living (ADL). EEG signals which contains information on the activities carried out by a human that is widely used in many applications of BCI technologies which offers a means of controlling exoskeletons or automated orthosis to facilitate their ADL. Although motor imagery signals have been used in assisting the hand grasping motion amongst others motions, nonetheless, such signals are often difficult to be generated. It is non-trivial to note that EEG-based signals for instance, winking could mitigate the aforesaid issue. Nevertheless, extracting and attaining significant features from EEG signals are also somewhat challenging. The utilization of deep learning, particularly Transfer Learning (TL), have been demonstrated in the literature to b e able to provide seamless extraction of such signals in a myria d of various applications. Hitherto, limited studies have investigated the classification of wink-based EEG signals through TL accompanied by classical Machine Learning (ML) pipelines. This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . These pre-processing methods were utilized to convert the digital signals into respective images of all the right and left winking EEG signals along with no winking signals that were collected from ten (6 males and 4 females, aged between 22 and 29) subjects. The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. A new form of input which consists of scalogram and spectrogram images that represents both time and frequency domains , are then introduced in the classification of wink-based EEG signals. Different TL models were exploited to extract features from the transformed EEG signals. The features extracted were then classified through three classical ML models, namely Support Vector Machine, k -Nearest Neighbour (k-NN) and Random Forest to determine the best pipeline for wink -based EEG signals. The hyperparameters of the ML models were tuned through a 5-fold crossvalidation technique via an exhaustive grid search approach. The training, validation and testing of the models were split with a stratified ratio of 60:20:20, respectively. The results obtained from the TL-ML pipelines were evaluated in terms of classification accuracy, Precision, Recall, F1-Score and confusion matrix. It was demonstrated from the simulation investigation that the CWT model could yield a better signal transformation amongst the preprocessing algorithms. In addition, amongst the eighteen TL models evaluated based on the CWT transformation, fourteen was f ound to be able to extract the features reasonable, i.e., VGG16, VGG19, ResNet101, ResNet101 V2, ResNet152, ResNet152 V2, Inception V3, Inception ResNet V2, Xception, MobileNetV2, DenseNet 121, DenseNet 169, NasNetMobile and NasNetLarge. Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. Nonetheless, upon carrying out a robustness test on new data, it was demonstrated that the CWT-NasNetMobile-kNN pipeline yielded the best performance. Therefore, it could be concluded that the proposed CWT-NasNetMobile-k-NN pipeline is suitable to be adopted to classify -winkbased EEG signals for BCI applications,for instance a grasping exoskeleton