Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

Application of biosignal-driven intelligent systems for multifunction prosthesis control

University of Technology, Sydney. Faculty of Engineering and Information Technology.Prosthetic devices aim to provide an artificial alternative to missing limbs. The controller for such devices is usually driven by the biosignals generated by the human body, particularly Electromyogram (EMG) or Electroencephalogram (EEG) signals. Such a controller utilizes a pattern recognition approach to classify the EMG signal recorded from the human muscles or the EEG signal from the brain. The aim of this thesis is to improve the EMG and EEG pattern classification accuracy. Due to the fact that the success of pattern recognition based biosignal driven systems highly depends on the quality of extracted features, a number of novel, robust, hybrid and innovative methods are proposed to achieve better performance. These methods are developed to effectively tackle many of the limitations of existing systems, in particular feature representation and dimensionality reduction. A set of knowledge extraction methods that can accurately and rapidly identify the most important attributes for classifying the arm movements are formulated. This is accomplished through the following: 1. Developing a new feature extraction technique that can identify the most important features from the high-dimensional time-frequency representation of the multichannel EMG and EEG signals. For this task, an information content estimation method using fuzzy entropies and fuzzy mutual information is proposed to identify the optimal wravelet packet transform decomposition for classification. 2. Developing a powerful variable (feature or channel) selection paradigm to improve the performance of multi-channel EMG and EEG driven systems. This will eventually lead to the development of a combined channel and feature selection technique as one possible scheme for dimensionality reduction. Two novel feature selection methods are developed under this scheme utilizing the ant colony arid differential evolution optimization techniques. The differential evolution optimization technique is further modified in a novel attempt in employing a float optimizer for the combinatorial task of feature selection, proving powerful performance by both methods. 3. Developing two feature projection techniques that extract a small subset of highly informative discriminant features, thus acting as an alternative scheme for dimensionality reduction. The two methods represent novel variations to fuzzy discriminant analysis based projection techniques. In addition, an extension to the non-linear discriminant analysis is proposed based on a mixture of differential evolution and fuzzy discriminant analysis. The testing and verification process of the proposed methods on different EMG and EEG datasets provides very encouraging results

Epileptic seizure detection and prediction based on EEG signal

Epilepsy is a kind of chronic brain disfunction, manifesting as recurrent seizures which is caused by sudden and excessive discharge of neurons. Electroencephalogram (EEG) recordings is regarded as the golden standard for clinical diagnosis of epilepsy disease. The diagnosis of epilepsy disease by professional doctors clinically is time-consuming. With the help artificial intelligence algorithms, the task of automatic epileptic seizure detection and prediction is called a research hotspot. The thesis mainly contributes to propose a solution to overfitting problem of EEG signal in deep learning and a method of multiple channels fusion for EEG features. The result of proposed method achieves outstanding performance in seizure detection task and seizure prediction task. In seizure detection task, this paper mainly explores the effect of the deep learning in small data size. This thesis designs a hybrid model of CNN and SVM for epilepsy detection compared with end-to-end classification by deep learning. Another technique for overfitting is new EEG signal generation based on decomposition and recombination of EEG in time-frequency domain. It achieved a classification accuracy of 98.8%, a specificity of 98.9% and a sensitivity of 98.4% on the classic Bonn EEG data. In seizure prediction task, this paper proposes a feature fusion method for multi-channel EEG signals. We extract a three-order tensor feature in temporal, spectral and spatial domain. UMLDA is a tensor-to-vector projection method, which ensures minimal redundancy between feature dimensions. An excellent experimental result was finally obtained, including an average accuracy of 95%, 94% F1-measure and 90% Kappa index

Evaluation of time-domain features for motor imagery movements using FCM and SVM

Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about the input data. Feature extraction is to highlight the properties of signal that make it distinct from the signal of the other mental tasks. Performance of BMIs directly depends on the effectiveness of the feature extraction and classification algorithms. If a feature provides large interclass difference for different classes, the applied classifier exhibits a better performance. In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length are used for feature extraction of EEG signals. Two classifiers are applied to assess the performance of each feature-subject. SVM with polynomial kernel is one of the applied nonlinear classifier and supervised FCM is the other one. The performance of each feature for input data are evaluated with both classifiers and classification accuracy is the considered common comparison parameter

An Optimized SWCSP Technique for Feature Extraction in EEG-based BCI System

Brain-computer interface (BCI) is an evolving technology having huge potential for rehabilitation of patients suffering from disorders of the nervous system, besides  many other nonmedical applications. Multichannel electroencephalography (EEG) is widely used to provide input signals to a BCI system. Significant research in methodology employed to implement different stages of BCI system, has led to discovery of new issues and challenges. The raw EEG data includes artifacts from environmental and physiological sources, which is eliminated in preprocessing phase of BCI system. It is then followed by a feature extraction stage to isolate a few relevant features for further classification to a particular motor imagery (MI) activity. A feature extraction approach based on spectrally weighted common spatial pattern (SWCSP) is proposed in this paper to improve overall accuracy of a BCI system. The reported literature uses SWCSP for feature extraction, as it has outperformed other techniques. The proposed approach enhances its performance by optimizing its parameters. The independent component analysis (ICA) method is used for detection and removal of irrelevant data, while linear discriminant analysis (LDA) is used as a classifier. The proposed approach is executed on benchmark data-set 2a of BCI competition IV. It yielded classification accuracy of 70.6% across nine subjects, which is higher than all the reported approaches.&nbsp

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed