Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multi-wavelets

A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The time-varying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation

Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning

It is of paramount importance to track the cognitive activity or cognitve attenion of the service personnel in a Prognostics and Health Monitoring (PHM) service related training or operation environment. The electroencephalography (EEG) data is one of the good candidates for cognitive activity recognition of the user. Analyzing electroencephalography (EEG) data in an unconstrained (natural) environment for understanding cognitive state and classifying human activity is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline availability and uncontrolled mixing of various tasks. This paper proposes a framework based on an emerging tool named deep learning that monitors human activity by fusing multiple EEG sensors and also selects a smaller sensor suite for a lean data collection system. Real-time classification of human activity from spatially non collocated multi-probe EEG is executed by applying deep learning techniques without performing any significant amount of data preprocessing and manual feature engineering. Two types of deep neural networks, deep belief network (DBN) and deep convolutional neural network (DCNN) are used at the core of the proposed framework, which automatically learns necessary features from EEG for a given classification task. Validation on extensive amount of data, which was collected from several subjects while they were performing multiple tasks (listening and watching) in PHM service training session, is presented and significant parallels are drawn from existing domain knowledge on EEG data understanding. Comparison with machine learning benchmark techniques shows that deep learning based tools are better at understanding EEG data for task classification. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite

Low cost EEG signal acquisition for health care and person identification

The task of monitoring human health invasively and maintaining the security of any system is challenging in the current scenario. The proposed system integrates the health monitoring and biometric authentication for a healthy as well as secure world. The initial module comprises of analysing the change in EEG signals when blood pressure increases for the sick and elderly people while the latter module for security purposes. EEG signal is acquired from the subject. The acquiring of the signal undergoes several steps which include the filtering and amplification. The alpha wave which is unique is extracted, that avoids spoofing attacks is considered for biometry while the beta waves which alter the state according to the human mind state is considered for health monitoring. Data reduction along with SVM classifier and Hilbert transform is implemented in the proposed model

A Performance Comparison of Neural Network and SVM Classifiers Using EEG Spectral Features to Predict Epileptic Seizures

Epilepsy is one of the most common neurological disorders, and afflicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to significant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artificial neural network and sup port vector machine classifiers, using EEG spectral features to predict the onset of epileptic seizures. In addition, the study also examines the influence of EEG window size, feature selection, and data sampling on classification performance. A total of 216 generalised models were trained and tested on a public seizure database, which contained over 1300 hours of EEG data from 7 subjects. The results showed that ANN outperform SVM, when using spectral features (p = 0.035). The beta and gamma frequency bands were shown to be the best predictors of seizure onset. No significant differences in performance were determined for the dif ferent window sizes, or for the feature selection methods. The data sampling method significantly influenced the performance (p \u3c 0.001), and highlighted the importance of treating class imbalance in EEG datasets

Automatic seizure detection based on Machine Learning and EEG

The diagnosis and treatment of epilepsy depend on accurate seizure detection. In clinical practice, the evaluation of seizures is done by visual inspection of an electroencephalogram (EEG). it is very time­consuming and requires trained experts. Automatic seizure detection is important. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre­screened results for neurologists. This work proposes a variety of experiments with different machine­learning architectures (support vector machine SVM, K nearest neighbour KNN, random forest RF, feef forward neural network FFNN and convolutional neural network CNN) for the detection of epileptic seizures using multichannel EEG signals from the CHBT­MIT Scalp EEG Database. The best model built in this work contains a combination of a feed­forward neural network (FFNN) and a convolutional neural network (CNN). CNN input images are constructed by applying short­time Fourier transform (STFT) to electroencephalography (EEG) signals and then merged with statistical metrics into a FFNN. The best model of this project showed an outstanding performance of 98.615% accuracy, 98.737% sensitivity and 98.425% specificity. This work also includes a discussion of other exciting ideas that could lead to future research investigations