RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in sleep medicine. However, manual detection limits their study because it is time-consuming and affected by significant inter-expert variability, motivating automatic approaches. We propose a deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED). RED uses one of two input representations: a) the time-domain EEG signal, or b) a complex spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT). Unlike previous approaches, a fixed time window is avoided and temporal context is integrated to better emulate the visual criteria of experts. When evaluated on the MASS dataset, our detectors outperform the state of the art in both sleep spindle and K-complex detection with a mean F1-score of at least 80.9% and 82.6%, respectively. Although the CWT-domain model obtained a similar performance than its time-domain counterpart, the former allows in principle a more interpretable input representation due to the use of a spectrogram. The proposed approach is event-agnostic and can be used directly to detect other types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN 2020

EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

High level security has nurtured the arisen of Electroencephalograms (EEG) signals as a noteworthy biometrics modality for person authentication modelling. Modelling distinctive characteristics among individuals, especially in a dynamic environment involves incremental knowledge updates from time to time. K-Nearest Neighbour (KNN) is a well-known incremental learning method which applies First-In-First-Out (FIFO) knowledge update strategy. However, it is not suitable for person authentication modelling because it cannot preserve the representative EEG signals patterns when individual characteristics changes over time. Fuzzy-Rough Nearest Neighbours (FRNN) technique is an outstanding technique to model uncertainty under an imperfect data condition. The current implementation of FRNN technique is not designed for incremental learning problem because there is no update function to incrementally reshape and reform the existing knowledge granules. Thus, this research aims to design an Incremental FRNN (IncFRNN) technique for person authentication modelling using feature extracted EEG signals from VEP electrodes. The IncFRNN algorithm updates the training set by employing a heuristic update method to maintain representative objects and eliminate rarely used objects. The IncFRNN algorithm is able to control the size of training pool using predefined window size threshold. EEG signals such as visual evoked potential (VEP) is unique but highly uncertain and difficult to process.There exists no consistant agreement on suitable feature extraction methods and VEP electrodes in the past literature. The experimental comparison in this research has suggested eight significant electrodes set located at the occipital area. Similarly, six feature extraction methods, i.e. Wavelet Packet Decomposition (WPD), mean of amplitude, coherence, crosscorrelation, hjorth parameter and mutual information were used construct the proposed person authentication model. The correlation-based feature selection (CFS) method was used to select representative WPD vector subset to eliminate redundancy before combining with other features. The electrodes, feature extraction, and feature selection analysis were tested using the benchmarking dataset from UCI repositories. The IncFRNN technique was evaluated using a collected EEG data from 37 subjects. The recorded datasets were designed in three different conditions of ambient noise influence to evaluate the performance of the proposed solution. The proposed IncFRNN technique was compared with its predecessor, the FRNN and IBk technique. Accuracy and area under ROC curve (AUC) were used to measure the authentication performance. The IncFRNN technique has achieved promising results. The results have been further validated and proven significant statistically using paired sample ttest and Wilcoxon sign-ranked test. The heuristic incremental update is able to preserve the core set of individual biometrics characteristics through representative EEG signals patterns in person authentication modelling. Future work should focus on the noise management in data acquisition and modelling process to improve the robustness of the proposed person authentication model

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.Comment: Published as a conference paper at the IEEE European Signal Processing Conference (EUSIPCO), 201

Components of Soft Computing for Epileptic Seizure Prediction and Detection

Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

EEG signal classification using wavelet feature extraction and neural networks

Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals