Adquisición y procesamiento de señales electromiográficas para el control de un vehículo virtual en tiempo real

This work presents the registration and classification of the electromyographic (EMG) signals of the lower extremities, specifically of the gross muscle, in order to control a virtual vehicle designed in Blender. The system has 4 channels, with a graphic interface, which allows the control of a virtual vehicle. For the processing of the signals, different mathematical tools were used such as: Fourier analysis and wavelet analysis. These techniques were used in order to compress data, obtain characteristic patterns in each set of signals and perform digital filtering. The control of the car consists of 4 commands such as: accelerate, stop, right turn and left turn, which are the basic instructions for the real operation of a car. The results showed that it is possible to use biological signals to perform virtual controls (video game). Likewise, it was verified that the parameterization found for each group of EMG signals was satisfactory, since the percentage of errors of the 4 variables studied was 0.04% for a total of 400 executions. This error percentage corroborates that the system has great potential for possible future applications.Este trabajo, se presenta el registro y clasificación de las señales electromiográficas (EMG) de las extremidades inferiores, específicamente del musculo basto, con el fin de controlar un vehículo virtual diseñado en Blender. El sistema tiene de 4 canales, con una interfaz gráfica, que permite el control de un vehículo virtual. Para el procesamiento de las señales, se utilizaron diferentes herramientas matemáticas tales como: análisis de Fourier y análisis wavelet. Estas técnicas se usaron con el objetivo de comprimir datos, obtener patrones característicos en cada conjunto de señales y realizar un filtrado digital. El control del automóvil consta de 4 comandos como: acelerar, detenerse, giro derecha y giro izquierda, las cuales son las instrucciones básicas para el manejo real de un automóvil. Los resultados mostraron que es posible usar señales biológicas para realizar controles virtuales (video juego). Así mismo, se verificó que la parametrizar encontrada de cada grupo de señales EMG, fue satisfactoria, ya que el porcentaje de errores de las 4 variables estudiadas fue del 0.04% para un total de 400 ejecuciones. Este porcentaje de error corrobora que el sistema tiene gran potencialidad para posibles aplicaciones futuras

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.</p

AUTOMATED INTERPRETATION OF THE BACKGROUND EEG USING FUZZY LOGIC

A new framework is described for managing uncertainty and for deahng with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis

A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate

Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.Comment: In Proceedings of the 15th International Conference on Machine Vision (ICMV), Rome, Italy, 18-20 November 2022. arXiv admin note: text overlap with arXiv:2206.0322

A multilayered approach to the automatic analysis of the multifocal electroretinogram

The multifocal electroretinogram (mfERG) provides spatial and temporal information on the retina’s function in an objective manner, making it a valuable tool for monitoring a wide range of retinal abnormalities. Analysis of this clinical test can however be both difficult and subjective, particularly if recordings are contaminated with noise, for example muscle movement or blinking. This can sometimes result in inconsistencies in the interpretation process. An automated and objective method for analysing the mfERG would be beneficial, for example in multi-centre clinical trials when large volumes of data require quick and consistent interpretation. The aim of this thesis was therefore to develop a system capable of standardising mfERG analysis. A series of methods aimed at achieving this are presented. These include a technique for grading the quality of a recording, both during and after a test, and several approaches for stating if a waveform contains a physiological response or no significant retinal function. Different techniques are also utilised to report if a response is within normal latency and amplitude values. The integrity of a recording was assessed by viewing the raw, uncorrelated data in the frequency domain; clear differences between acceptable and unacceptable recordings were revealed. A scale ranging from excellent to unreportable was defined for the recording quality, first in terms of noise resulting from blinking and loss of fixation, and secondly, for muscle noise. 50 mfERG tests of varying recording quality were graded using this method with particular emphasis on the distinction between a test which should or should not be reported. Three experts also assessed the mfERG recordings independently; the grading provided by the experts was compared with that of the system. Three approaches were investigated to classify a mfERG waveform as ‘response’ or ‘no response’ (i.e. whether or not it contained a physiological response): artificial neural networks (ANN); analysis of the frequency domain profile; and the signal to noise ratio. These techniques were then combined using an ANN to provide a final classification for ‘response’ or ‘no response’. Two methods were studied to differentiate responses which were delayed from those within normal timing limits: ANN; and spline fitting. Again the output of each was combined to provide a latency classification for the mfERG waveform. Finally spline fitting was utilised to classify responses as ‘decreased in amplitude’ or ‘not decreased’. 1000 mfERG waveforms were subsequently analysed by an expert; these represented a wide variety of retinal function and quality. Classifications stated by the system were compared with those of the expert to assess its performance. An agreement of 94% was achieved between the experts and the system when making the distinction between tests which should or should not be reported. The final system classified 95% of the 1000 mfERG waveforms correctly as ‘response’ or ‘no response’. Of those said to represent an area of functioning retina it concurred with the expert for 93% of the responses when categorising them as normal or abnormal in terms of their P1 amplitude and latency. The majority of misclassifications were made when analysing waveforms with a P1 amplitude or latency close to the boundary between normal and abnormal. It was evident that the multilayered system has the potential to provide an objective and automated assessment of the mfERG test; this would not replace the expert but can provide an initial analysis for the expert to review

Data reduction algorithms to enable long-term monitoring from low-power miniaturised wireless EEG systems

Objectives: The weight and volume of battery-powered wireless electroencephalography (EEG) systems are dominated by the batteries. Battery dimensions are in turn determined by the required energy capacity, which is derived from the system power consumption and required monitoring time. Data reduction may be carried out to reduce the amount of data transmitted and thus proportionally reduce the power consumption of the wireless transmitter, which dominates system power consumption. This thesis presents two new data selection algorithms that, in addition to achieving data reduction, also select EEG containing epileptic seizures and spikes that are important in diagnosis. Methods: The algorithms analyse short EEG sections, during monitoring, to determine the presence of candidate seizures or spikes. Phase information from different frequency components of the signal are used to detect spikes. For seizure detection, frequencies below 10 Hz are investigated for a relative increase in frequency and/or amplitude. Significant attention has also been given to metrics in order to accurately evaluate the performance of these algorithms for practical use in the proposed system. Additionally, signal processing techniques to emphasize seizures within the EEG and techniques to correct for broad-level amplitude variation in the EEG have been investigated. Results: The spike detection algorithm detected 80% of spikes whilst achieving 50% data reduction, when tested on 992 spikes from 105 hours of 10-channel scalp EEG data obtained from 25 adults. The seizure detection algorithm identified 94% of seizures selecting 80% of their duration for transmission and achieving 79% data reduction. It was tested on 34 seizures with a total duration of 4158 s in a database of over 168 hours of 16-channel scalp EEG obtained from 21 adults. These algorithms show great potential for longer monitoring times from miniaturised wireless EEG systems that would improve electroclinical diagnosis of patients

Applying laser irradiation and intelligent concepts to identify grinding phenomena

The research discussed in this thesis explores a new method for the detection of grinding burn temperature using a laser irradiation acoustic emission (AE) sensing technique. This method is applicable for the grinding process monitoring system, providing an early warning for burn detection on metal alloy based materials (specifically nickel alloy based materials: Inconel718 and MarM002). The novelty in this research is the laser irradiation induced thermal AE signal that represents the grinding thermal behaviour and can be used for grinding burn detection. A set of laser irradiation experiments were conducted to identify key process characteristics. By controlling the laser power, the required grinding temperatures were simulated on alloy test materials. The thermal features of the extracted AE signal were used to identify the high, medium and low temperature signatures in relation to the off-focal laser distances. Grinding experiments were also conducted to investigate burn conditions. The extracted AE data was used to identify grinding burn and no burn signatures in relation to the depth of cuts. A new approach using an artificial neural network (ANN) was chosen as the pattern recognition tool for classifying grinding burn detection and was used to classify grinding temperatures by extracting the mechanical-thermal grinding AE signal. The results demonstrated that the classification accuracy achieved was 66 % for Inconel718 and 63 % for MarM002 materials. The research established that the wheel wear has a large influence on the creation of burn within the workpiece surface. The results demonstrated that the AE signals in each grinding trial presents different levels of high, medium and low temperature scales. This type of information provides a foundation for a new method for monitoring of grinding burn and wheel wear

New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method

Localizing the bioelectric phenomena originating from the cerebral cortex and evoked by auditory and somatosensory stimuli are clear objectives to both understand how the brain works and to recognize different pathologies. Diseases such as Parkinson’s, Alzheimer’s, schizophrenia and epilepsy are intensively studied to find a cure or accurate diagnosis. Epilepsy is considered the disease with major prevalence within disorders with neurological origin. The recurrent and sudden incidence of seizures can lead to dangerous and possibly life-threatening situations. Since disturbance of consciousness and sudden loss of motor control often occur without any warning, the ability to predict epileptic seizures would reduce patients’ anxiety, thus considerably improving quality of life and safety. The common procedure for epilepsy seizure detection is based on brain activity monitorization via electroencephalogram (EEG) data. This process consumes a lot of time, especially in the case of long recordings, but the major problem is the subjective nature of the analysis among specialists when analyzing the same record. From this perspective, the identification of hidden dynamical patterns is necessary because they could provide insight into the underlying physiological mechanisms that occur in the brain. Time-frequency distributions (TFDs) and adaptive methods have demonstrated to be good alternatives in designing systems for detecting neurodegenerative diseases. TFDs are appropriate transformations because they offer the possibility of analyzing relatively long continuous segments of EEG data even when the dynamics of the signal are rapidly changing. On the other hand, most of the detection methods proposed in the literature assume a clean EEG signal free of artifacts or noise, leaving the preprocessing problem opened to any denoising algorithm. In this thesis we have developed two proposals for EEG signal processing: the first approach consists in electrooculogram (EOG) removal method based on a combination of ICA and RLS algorithms which automatically cancels the artifacts produced by eyes movement without the use of external “ad hoc” electrode. This method, called ICA-RLS has been compared with other techniques that are in the state of the art and has shown to be a good alternative for artifacts rejection. The second approach is a novel method in EEG features extraction called tracks extraction (LFE features). This method is based on the TFDs and partial tracking. Our results in pattern extractions related to epileptic seizures have shown that tracks extraction is appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost

Applying laser irradiation and intelligent concepts to identify grinding phenomena

The research discussed in this thesis explores a new method for the detection of grinding burn temperature using a laser irradiation acoustic emission (AE) sensing technique. This method is applicable for the grinding process monitoring system, providing an early warning for burn detection on metal alloy based materials (specifically nickel alloy based materials: Inconel718 and MarM002). The novelty in this research is the laser irradiation induced thermal AE signal that represents the grinding thermal behaviour and can be used for grinding burn detection. A set of laser irradiation experiments were conducted to identify key process characteristics. By controlling the laser power, the required grinding temperatures were simulated on alloy test materials. The thermal features of the extracted AE signal were used to identify the high, medium and low temperature signatures in relation to the off-focal laser distances. Grinding experiments were also conducted to investigate burn conditions. The extracted AE data was used to identify grinding burn and no burn signatures in relation to the depth of cuts. A new approach using an artificial neural network (ANN) was chosen as the pattern recognition tool for classifying grinding burn detection and was used to classify grinding temperatures by extracting the mechanical-thermal grinding AE signal. The results demonstrated that the classification accuracy achieved was 66 % for Inconel718 and 63 % for MarM002 materials. The research established that the wheel wear has a large influence on the creation of burn within the workpiece surface. The results demonstrated that the AE signals in each grinding trial presents different levels of high, medium and low temperature scales. This type of information provides a foundation for a new method for monitoring of grinding burn and wheel wear