A Performance Study of the Wavelet-Phase Stability in the Quantification of Neural Correlates of Auditory Selective Attention

Large–scale neural correlates of auditory selective attention reflected in the electroencephalogram (EEG) have been identified by using the complex wavelet–phase stability measure (WPS). In this paper, we study the feasibility of using the WPS in extracting the correlates of selective attention by comparing its performance to the widely used linear interdependency measures, i.e., the wavelet coherence and the correlation coefficient. The outcome reveals that the phase measure outperforms the others in discriminating the attended and unattended single sweep auditory late responses (ALRs). Particularly, the number of response sweeps that are needed to perform the differentiation is largely reduced by using the proposed measure. It is concluded that a faster (in terms of using fewer sweeps) and more robust objective quantification of selective attention can be achieved by using the phase stability measure

Analysis of worker performances using statistical process control in fish paste otak-otak food industries

This research focuses on the improvement of Small and Medium Enterprises through the used of Process Statistical Control (SPC). An industry that focuses on the fish paste (known as “otak-otak”) production was taken as the case study in this research and the problems analysed are based on the real industrial experiences. The data collection for control charts were recorded for two weeks consisting of working time for each operator. The data were collected in subgroup of 16 with sample size of 5. The collection of data for weight of product was recorded randomly for the whole production line, while data collection of working time of operation was taken randomly from each operator every 30 minutes of the working hour. From this study, there are several problems had been detected in the process that been categories in six element that is people, method, measurement, machine, environment and materials. There were lack of motivation, lack of skill, lack of supervision, manual operation, lack of standard of procedure, waiting time in process, weight-based operator, lack of quality check, not using weight scale, conveyer that sometimes got stuck, spoon for tools, no automation, poor layout arrangement, talking while working, small working space, lack of hygiene, waiting time for material and easily spoiled. The findings can be used as the guideline to the industries for future production improvement. The industries would focus on elimination or reduction of the problems through their innovative solution

Analysis of the structure of time-frequency information in electromagnetic brain signals

This thesis encompasses methodological developments and experimental work aimed at revealing information contained in time, frequency, and time–frequency representations of electromagnetic, specifically magnetoencephalographic, brain signals. The work can be divided into six endeavors. First, it was shown that sound slopes increasing in intensity from undetectable to audible elicit event-related responses (ERRs) that predict behavioral sound detection. This provides an opportunity to use non-invasive brain measures in hearing assessment. Second, the actively debated generation mechanism of ERRs was examined using novel analysis techniques, which showed that auditory stimulation did not result in phase reorganization of ongoing neural oscillations, and that processes additive to the oscillations accounted for the generation of ERRs. Third, the prerequisites for the use of continuous wavelet transform in the interrogation of event-related brain processes were established. Subsequently, it was found that auditory stimulation resulted in an intermittent dampening of ongoing oscillations. Fourth, information on the time–frequency structure of ERRs was used to reveal that, depending on measurement condition, amplitude differences in averaged ERRs were due to changes in temporal alignment or in amplitudes of the single-trial ERRs. Fifth, a method that exploits mutual information of spectral estimates obtained with several window lengths was introduced. It allows the removal of frequency-dependent noise slopes and the accentuation of spectral peaks. Finally, a two-dimensional statistical data representation was developed, wherein all frequency components of a signal are made directly comparable according to spectral distribution of their envelope modulations by using the fractal property of the wavelet transform. This representation reveals noise buried processes and describes their envelope behavior. These examinations provide for two general conjectures. The stability of structures, or the level of stationarity, in a signal determines the appropriate analysis method and can be used as a measure to reveal processes that may not be observable with other available analysis approaches. The results also indicate that transient neural activity, reflected in ERRs, is a viable means of representing information in the human brain.reviewe

Brain function assessment in different conscious states

Background: The study of brain functioning is a major challenge in neuroscience fields as human brain has a dynamic and ever changing information processing. Case is worsened with conditions where brain undergoes major changes in so-called different conscious states. Even though the exact definition of consciousness is a hard one, there are certain conditions where the descriptions have reached a consensus. The sleep and the anesthesia are different conditions which are separable from each other and also from wakefulness. The aim of our group has been to tackle the issue of brain functioning with setting up similar research conditions for these three conscious states.Methods: In order to achieve this goal we have designed an auditory stimulation battery with changing conditions to be recorded during a 40 channel EEG polygraph (Nuamps) session. The stimuli (modified mismatch, auditory evoked etc.) have been administered both in the operation room and the sleep lab via Embedded Interactive Stimulus Unit which was developed in our lab. The overall study has provided some results for three domains of consciousness. In order to be able to monitor the changes we have incorporated Bispectral Index Monitoring to both sleep and anesthesia conditions.Results: The first stage results have provided a basic understanding in these altered states such that auditory stimuli have been successfully processed in both light and deep sleep stages. The anesthesia provides a sudden change in brain responsiveness; therefore a dosage dependent anesthetic administration has proved to be useful. The auditory processing was exemplified targeting N1 wave, with a thorough analysis from spectrogram to sLORETA. The frequency components were observed to be shifting throughout the stages. The propofol administration and the deeper sleep stages both resulted in the decreasing of N1 component. The sLORETA revealed similar activity at BA7 in sleep (BIS 70) and target propofol concentration of 1.2 μg/mL.Conclusions: The current study utilized similar stimulation and recording system and incorporated BIS dependent values to validate a common approach to sleep and anesthesia. Accordingly the brain has a complex behavior pattern, dynamically changing its responsiveness in accordance with stimulations and states. © 2010 Ozgoren et al; licensee BioMed Central Ltd

Objective Quantification Of Selective Attention In Schizophrenia : A Hybrid TMS-EEG Approach

Schizophrenia is a brain disorder that exhibits effects on perception, way of thinking and behavior. Often, schizophrenia patients suffer from attention deficiency. Currently clinical interview is used to diagnose schizophrenia by doctors. There is no alternative way to diagnose schizophrenia in present. Thus, an objective approach by employing transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) is proposed. The aim of the study is to quantify objectively the neural correlate of selective attention that reflected in auditory late responses (ALRs) using signal processing techniques. TMS provides a means of stimulating neuronal structures within the cortex using brief time-varying magnetic pulses generated by a coil positioned over the scalp. Integrating it with electroencephalogram provides real-time information on cortical reactivity and connectivity through the analysis of TMS evoked potentials or induced oscillations. In this project, auditory oddball paradigm was used throughout the experiment. The experiment involved three sessions; 1) without TMS, 2) single pulse TMS (sTMS) and 3) repetitive TMS (rTMS). All sessions were conducted in attended (attention) and unattended (no attention) conditions. It is found that the amplitude of the grand averaged of ALR (the N1-P2 wave) is higher in control compared to schizophrenia in without TMS session at both conditions. However, the amplitude of ALR in schizophrenia subjects is higher than control subjects in sTMS and rTMS at both conditions. The attention level measure, i.e., the Wavelet Phase Stability (WPS) was used to extract and quantify the neural correlates of auditory selective attention reflected in ALRs. In particular, Complex Morlet was implemented (scales 50-100 corresponding to 4-8Hz). There are significant differences of the ALR between schizophrenia and control groups in without TMS (p<0.05) and sTMS at the attended condition (frontal electrodes). Meanwhile at the unattended condition, Significance difference is found between two groups of the subjects in without TMS but no significant difference in sTMS (frontal electrodes). Particularly, the WPS of controls are larger than schizophrenia patients for without TMS and sTMS at attended for frontal electrodes. These results were consistent for temporal electrodes. It is worth to note that the phase stability of ALR in single pulse TMS is lower than without TMS for controls during attended but showed reversed pattern in unattended. Besides, it is found that a large phase stability difference between without TMS and sTMS in schizophrenia (frontal and temporal electrodes) at unattended compared to attended. For control subjects, this difference is small at frontal and temporal electrodes in both conditions. In a further investigation, the C4.5 decision tree algorithm was implemented to classify the N1-P2 wave of control and schizophrenia subjects elicited by sTMS and rTMS. Four features (energy, power, variance and entropy) were extracted by continuous wavelet transform (CWT). The result shows high classification accuracy which is above 83% in all three sessions at both attended and unattended conditions. In conclusion, the combined TMS-EEG approach shows a promising way to study the selective attention in schizophrenia. By successfully quantifying the neural correlates of auditory selective attention reflected in ALRs using the WPS and discriminating the control and patient groups using C4.5 decision tree provides an objective way to diagnose schizophrenia in compliment to the current subjective method

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

PID control of depth of hypnosis in anesthesia for propofol and remifentanil coadministration

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasThe purpose of general anesthesia is to deeply sedate a person so that they lose consciousness, sensitivity, and body reflexes, and so that surgeries can be safely performed without the patient feeling pain or discomfort during the procedure. General anesthesia is a combination of the effect of three components, namely hypnosis, analgesia, and neuromuscular blockade. Each component is regulated through the action of a specific drug, or through the combined effect of two or more drugs. In recent years there have been many advances in the field of automatic control systems for drug delivery during anesthesia, which can be implemented using a wide variety of controllers and process variables. The reason behind these advances is that an automatic control system can provide several benefits, such as a reduction in the anesthesiologist's workload, a reduction in the amount of medication used (which implies a faster and better recovery time for the patient in the postoperative phase), and, in fact, a more robust performance with fewer episodes of over- or under-dosing of the drug. A proportional-integral-derivative controller (PID) continuously calculates the error value that is the difference between the desired value and the measured process variable and applies a correction that is based on proportional, integral and derivative terms. In this dissertation, a specific PID control system for propofol and remifentanil is proposed to regulate the hypnosis component during anesthesia using the bispectral index (BIS) as the process variable. Infusion rates of both drugs are also controlled. The adjustment of the PID parameters, so that the BIS was closer to what was expected, was done using a genetic algorithm. The implementation of the control system was done in Simulink in order to simulate a surgery. The simulation scheme includes the patient models for both drugs, a disturbance profile, and two different PID controllers for the two phases of anesthesia - induction and maintenance. Aspects such as noise in the BIS signal and artifacts were taken into account in the system and a suitable noise filter was applied in the control algorithm. In addition, a ratio between the infusion rates of propofol and remifentanil has been introduced to allow the anesthesiologist to choose the appropriate opioid-hypnotic balance In the end, a performance analysis of the control system was made based on seven performance indices (namely the integrated absolute error, the settling time, the median performance error, the median absolute performance error, the wobble, and the above and below recommended BIS values). Although there are many types of control systems for the automatic control of hypnosis depth described in the literature, these are not usually used in clinical practice. Therefore, it is important to continue research to produce robust and user-friendly systems that integrate clinicians' clinical knowledge and meet their actual needs

AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method

Nociception level during anaesthesia : analysis and control

Tese de Programa Doutoral. Engenharia Biomédica. Universidade do Porto. Faculdade de Engenharia. 201