Subjective and objective measures

One of the greatest challenges in the study of emotions and emotional states is their measurement. The techniques used to measure emotions depend essentially on the authors’ definition of the concept of emotion. Currently, two types of measures are used: subjective and objective. While subjective measures focus on assessing the conscious recognition of one’s own emotions, objective measures allow researchers to quantify and assess the conscious and unconscious emotional processes. In this sense, when the objective is to evaluate the emotional experience from the subjective point of view of an individual in relation to a given event, then subjective measures such as self-report should be used. In addition to this, when the objective is to evaluate the emotional experience at the most unconscious level of processes such as the physiological response, objective measures should be used. There are no better or worse measures, only measures that allow access to the same phenomenon from different points of view. The chapter’s main objective is to make a survey of the main measures of evaluation of the emotions and emotional states more relevant in the current scientific panorama.info:eu-repo/semantics/acceptedVersio

An overview of current approaches and future challenges in physiological monitoring

Sufficient evidence exists from laboratory studies to suggest that physiological measures can be useful as an adjunct to behavioral and subjective measures of human performance and capabilities. Thus it is reasonable to address the conceptual and engineering challenges that arise in applying this technology in operational settings. Issues reviewed include the advantages and disadvantages of constructs such as mental states, the need for physiological measures of performance, areas of application for physiological measures in operational settings, which measures appear to be most useful, problem areas that arise in the use of these measures in operational settings, and directions for future development

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)

INVESTIGATION, DEVELOPMENT AND APPLICATION OF KNOWLEDGE BASED DIGITAL SIGNAL PROCESSING METHODS FOR ENHANCING HUMAN EEGsJ

This thesis details the development of new and reliable techniques for enhancing the human Electroencephalogram {EEGI. This development has involved the incorporation of adaptive signal processing (ASP) techniques, within an artificial intelligence (Al) paradigm, more closely matching the implicit signal analysis capabilities of the EEG expert. The need for EEG enhancement, by removal of ocular artefact (OA) , is widely recognised. However, conventional ASP techniques for OA removal fail to differentiate between OAs and some abnormal cerebral waveforms, such as frontal slow waves. OA removal often results in the corruption of these diagnostically important cerebral waveforms. However, the experienced EEG expert is often able to differentiate between OA and abnormal slow waveforms, and between different types of OA. This EEG expert knowledge is integrated with selectable adaptive filters in an intelligent OA removal system (tOARS). The EEG is enhanced by only removing OA when OA is identified, and by applying the OA removal algorithm pre-set for the specific OA type. Extensive EEG data acquisition has provided a database of abnormal EEG recordings from over 50 patients, exhibiting a variety of cerebral abnormalities. Structured knowledge elicitation has provided over 60 production rules for OA identification in the presence of abnormal frontal slow waveforms, and for distinguishing between OA types. The lOARS was implemented on personal computer (PCI based hardware in PROLOG and C software languages. 2-second, 18-channel, EEG signal segments are subjected to digital signal processing, to extract salient features from time, frequency, and contextual domains. OA is identified using a forward/backward hybrid inference engine, with uncertainty management, using the elicited expert rules and extracted signal features. Evaluation of the system has been carried out using both normal and abnormal patient EEGs, and this shows a high agreement (82.7%) in OA identification between the lOARS and an EEG expert. This novel development provides a significant improvement in OA removal, and EEG signal enhancement, and will allow more reliable automated EEG analysis. The investigation detailed in this thesis has led to 4 papers, including one in a special proceedings of the lEE, and been subject to several review articles.Department of Neurophysiology, Derriford Hospital, Plymouth, Devo

Study, definition and analysis of pilot/system performance measurements for planetary entry experiments

Definition analysis for experimental prediction of pilot performance during planetary entr

INVESTIGATION OF OCULAR ARTEFACTS IN THE HUMAN EEG AND THEIR REMOVAL BY A MICROPROCESSOR-BASED INSTRUMENT

The Electroencephalogram (EEG) is widely used in clinical and psychological situations, but it is often seriously obscured by ocular artefacts (OAs) resulting from movements in the ocular system (eyeball, eyelids etc). 'The work described in this thesis is concerned with the problems of OAs in the human EEG, their removal both off-line and on-line, and the design and development of an on-line OA removal system, together with a critical review of the literature on the subject. The work of Jervis and his co-workers was extended to further study OAs, to obtain improved measures of the effectiveness of OA removal, and to find the most effective model for removing OA on-line. A number of criteria were devised to compare the performance of several models, including a more reliable pictorial method. It was found unnecessary to use the vertical and horizontal EOGs for both eyes (ie. four EOGs) in a removal model, as previously reported. This was shown to be due to strong correlation between the EOGs. It was shown that the assumption of uncorrelated error terms, implicit in present removal models, is invalid. To remedy this, the error terms were modelled as an autoregressive series. New on-line removal algorithms based on numerically stable factorization algorithms were developed. Compared to the present on-line methods the algorithms are superior, requiring no subjective manual adjustments, or the co-operation of subjects which cannot always be guarranteed. The algorithms were shown to give similar results to their off-line equivalents. A simpler algorithm based on the present on-line method is also proposed as an alternative, but may lead to a reduced performance. An important part of this research lay in the application of the results to the design and development of a new automatic OA removal system utilizing the algorithms described above.Department of Neurological Sciences, Freedom Fields Hospital, Plymout

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Toward Simulation-Based Training Validation Protocols: Exploring 3d Stereo with Incremental Rehearsal and Partial Occlusion to Instigate and Modulate Smooth Pursuit and Saccade Responses in Baseball Batting

“Keeping your eye on the ball” is a long-standing tenet in baseball batting. And yet, there are no protocols for objectively conditioning, measuring, and/or evaluating eye-on-ball coordination performance relative to baseball-pitch trajectories. Although video games and other virtual simulation technologies offer alternatives for training and obtaining objective measures, baseball batting instruction has relied on traditional eye-pitch coordination exercises with qualitative “face validation”, statistics of whole-task batting performance, and/or subjective batter-interrogation methods, rather than on direct, quantitative eye-movement performance evaluations. Further, protocols for validating transfer-of-training (ToT) for video games and other simulation-based training have not been established in general ― or for eye-movement training, specifically. An exploratory research study was conducted to consider the ecological and ToT validity of a part-task, virtual-fastball simulator implemented in 3D stereo along with a rotary pitching machine standing as proxy for the live-pitch referent. The virtual-fastball and live-pitch simulation couple was designed to facilitate objective eye-movement response measures to live and virtual stimuli. The objective measures 1) served to assess the ecological validity of virtual fastballs, 2) informed the characterization and comparison of eye-movement strategies employed by expert and novice batters, 3) enabled a treatment protocol relying on repurposed incremental-rehearsal and partial-occlusion methods intended to instigate and modulate strategic eye movements, and 4) revealed whether the simulation-based treatment resulted in positive (or negative) ToT in the real task. Results indicated that live fastballs consistently elicited different saccade onset time responses than virtual fastballs. Saccade onset times for live fastballs were consistent with catch-up saccades that follow the smooth-pursuit maximum velocity threshold of approximately 40-70˚/sec while saccade onset times for virtual fastballs lagged in the order of 13%. More experienced batters employed more deliberate and timely combinations of smooth pursuit and catch-up saccades than less experienced batters, enabling them to position their eye to meet the ball near the front edge of home plate. Smooth pursuit and saccade modulation from treatment was inconclusive from virtual-pitch pre- and post-treatment comparisons, but comparisons of live-pitch pre- and post-treatment indicate ToT improvements. Lagging saccade onset times from virtual-pitch suggest possible accommodative-vergence impairment due to accommodation-vergence conflict inherent to 3D stereo displays

Characterization of sleep EEG

The physiological relationship between the various components of sleep and its variation due to drug administration has been used as one of the primary tools to analyze the performance of drug. A number of studies have been performed in recent years in this direction. Electroencephalogram (EEG) has been characterized with the help of variables ranging from measurements of the duration of different sleep stages to the activities that define the stages themselves. Advances in computer hardware and software have improved the methods of data acquisition and storage. Analysis of long stretch of data has always been a problem considering the time and storage. The present study is aimed at characterizing sleep data from a subject suffering from neurologic disorder. It also aims at identifying the effect of Oxycodon a Narcotic drug on the subject during sleep. The data considered for analysis is the output of a whole night recording. It is for a duration of six hours. EEG signals are analyzed using random data analysis procedures. The assumption of stationarity will be used as the basis of analysis. However the fact that analysis on long stretch of data introducing nonstationarity will not be ruled out. The analysis will be performed using Fast Fourier Transformation. Spectral analysis will be used as the primary tool in identifying the activities of various frequency components and its variation with time. The three parameters that will be considered are Mean square values and correlation function in time domain, Spectral analysis application in frequency domain and the probability density and distribution functions in the amplitude domain. Algorithms will be developed for computing these parameters and other statistical properties

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships