Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task.

Driver fatigue is a prevalent problem and a major risk for road safety accounting for approximately 20-40% of all motor vehicle accidents. One strategy to prevent fatigue related accidents is through the use of countermeasure devices. Research on countermeasure devices has focused on methods that detect physiological changes from fatigue, with the fast temporal resolution from brain signals, using the electroencephalogram (EEG) held as a promising technique. This paper presents the results of nonlinear analysis using sample entropy and second-order difference plots quantified by central tendency measure (CTM) on alert and fatigue EEG signals from a driving simulated task. Results show that both sample entropy and second-order difference plots significantly increases the regularity and decreases the variability of EEG signals from an alert to a fatigue state

Prediction Accuracy of Eye-Open State using WEKA Algorithms

Purpose: Brain diseases are reflected in the pattern of brain-waves recorded using electroencephalography (EEG). We aimed to evaluate the prediction accuracy of machine learning algorithms embedded in WEKA software tool applied to the EEG eye-state signal dataset.  Methods: The eye-state dataset was retrieved from UCI ML repository, and it consists of 14980 samples (instances), 15 attributes (electrodes), and each instance was one continuous EEG measurement made within 117 seconds. The two classes in the dataset are '1', indicating the eye-closed state and '0' the eye-open state. The prediction accuracy of eye-closed and eye-open was done with machine learning algorithms incorporated in WEKA software tool. Results: The best statistical performance evaluation measure was observed in this study for the classifiers viz., Random Forest, Random Tree, J48, Bagging and Decision table. Random Forest predicted the edited test dataset in the ratio of 7:3 (correct : incorrect). Conclusion: Among the five classifiers, Random Forest and Bagging gave significant performance (‘v’) while analyzed in the ‘experimenter’ environment in WEKA

Analysis of eyes open, eye closed EEG signals using second-order difference plot

An assistive technology developed for "hands free" control of electrical devices to be used by severely impaired people within their environment, relies upon using signal processing techniques for analyzing eyes closed (EC) and eyes open (EO) states in the electroencephalography (EEG) signal. Here, we apply a signal processing technique used in continuous chaotic modeling to investigate differences in the EEG time series between EC and EO states. This method is used to detect the degree of variability from a second-order difference plot, and quantifying this using a central tendency measures. The study used EEG time series of EO and EC states from 33 able-bodied and 17 spinal cord injured participants. The results found an increased EEG variability in brain activity during EC compared to EO. This increased EEG variability occurred in the O2 electrode, which overlays the primary visual cortex V1, and could be a result of the replacement of the coherent information obtained during EO by noise. A continuous measure of the variability was then used to demonstrate that this technique has the potential to be used as a switching mechanism for assistive technologies. © International Federation for Medical and Biological Engineering 2007

Interpreting EEG alpha activity

Exploring EEG alpha oscillations has generated considerable interest, in particular with regards to the role they play in cognitive, psychomotor, psycho-emotional and physiological aspects of human life. However, there is no clearly agreed upon definition of what constitutes ‘alpha activity’ or which of the many indices should be used to characterize it. To address these issues this review attempts to delineate EEG alpha-activity, its physical, molecular and morphological nature, and examine the following indices: (1) the individual alpha peak frequency; (2) activation magnitude, as measured by alpha amplitude suppression across the individual alpha bandwidth in response to eyes opening, and (3) alpha “auto-rhythmicity” indices: which include intra-spindle amplitude variability, spindle length and steepness. Throughout, the article offers a number of suggestions regarding the mechanism(s) of alpha activity related to inter and intra-individual variability. In addition, it provides some insights into the various psychophysiological indices of alpha activity and highlights their role in optimal functioning and behavior

Depth of anaesthesia assessment based on time and frequency features of simplified electroencephalogram (EEG)

Anaesthesiology is a medical subject focusing on the use of drugs and other methods to deprive patients’ sensation for discomfort in painful medical diagnosis or treatment. It is important to assess the depth of anaesthesia (DoA) accurately since a precise as- sessment is helpful for avoiding various adverse reactions such as intraoperative awareness with recall (underdosage), prolonged recovery and an increased risk of post- operative complications for a patient (overdosage). Evidence shows that the depth of anaesthesia monitoring using electroencephalograph (EEG) improves patient treat- ment outcomes by reducing the incidences of intra-operative awareness, minimizing anaesthetic drug consumption and resulting in faster wake-up and recovery. For an accurate DoA assessment, intensive research has been conducted in finding 'an ulti- mate index', and various monitors and DoA algorithms were developed. Generally, the limitations of the existing DoA monitors or latest DoA algorithms include unsatis- factory data filtering techniques, time delay and inflexible. The focus of this dissertation is to develop reliable DoA algorithms for accurate DoA assessment. Some novel time-frequency domain signal processing techniques, which are better suited for non-stationary EEG signals than currently established methods, have been proposed and applied to monitor the DoA based on simplified EEG signals based on plenty of programming work (including C and other programming language). The fast Fourier transform (FFT) and the discrete wavelet transforms are applied to pre-process EEG data in the frequency domain. The nonlocal mean, mobility, permu- tation entropy, Lempel-Ziv complexity, second order difference plot and interval feature extraction methods are modified and applied to investigate the scaling behaviour of the EEG in the time domain. We proposed and developed three new indexes for identifying, classifying and monitoring the DoA. The new indexes are evaluated by comparing with the most popular BIS index. Simulation results demonstrate that our new methods monitor the DoA in all anaesthesia states accurately. The results also demonstrate the advantages of proposed indexes in the cases of poor signal quality and the consistency with the anaesthetists’ records. These new indexes show a 3.1-59.7 seconds earlier time response than BIS during the change from awake to light anaesthesia and a 33-264 seconds earlier time response than BIS during the change from deep anaesthesia to moderate anaesthesia

Strategies for Combining Tree-Based Ensemble Models

Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is to develop a strategy for combining ensemble classifiers that results in higher classification accuracy than the constituent ensemble models. Three of the best performing tree-based ensemble methods – random forest, extremely randomized tree, and eXtreme gradient boosting model – were used to generate a set of base models. Outputs from classifiers generated by these methods were then combined to create an ensemble classifier. This dissertation systematically investigated methods for (1) selecting a set of diverse base models, and (2) combining the selected base models. The methods were evaluated using public domain data sets which have been extensively used for benchmarking classification models. The research established that applying random forest as the final ensemble method to integrate selected base models and factor scores of multiple correspondence analysis turned out to be the best ensemble approach