AN INVESTIGATION OF ELECTROMYOGRAPHIC (EMG) CONTROL OF DEXTROUS HAND PROSTHESES FOR TRANSRADIAL AMPUTEES

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services.There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.Ministry of Higher Education and Scientific Research and Baghdad University- Baghdad/Ira

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio

Studies on the assessment of the adequacy of anesthesia

Several hypnosis monitoring systems based on the processed electroencephalogram (EEG) have been developed for use during general anesthesia. The assessment of the analgesic component (antinociception) of general anesthesia is an emerging field of research. This study investigated the interaction of hypnosis and antinociception, the association of several physiological variables with the degree of intraoperative nociception, and aspects of EEG Bispectral Index Scale (BIS) monitoring during general anesthesia. In addition, EEG features and heart rate (HR) responses during desflurane and sevoflurane anesthesia were compared. A propofol bolus of 0.7 mg/kg was more effective than an alfentanil bolus of 0.5 mg in preventing the recurrence of movement responses during uterine dilatation and curettage (D C) after a propofol-alfentanil induction, combined with nitrous oxide (N2O). HR and several HR variability-, frontal electromyography (fEMG)-, pulse plethysmography (PPG)-, and EEG-derived variables were associated with surgery-induced movement responses. Movers were discriminated from non-movers mostly by the post-stimulus values per se or normalized with respect to the pre-stimulus values. In logistic regression analysis, the best classification performance was achieved with the combination of normalized fEMG power and HR during D C (overall accuracy 81%, sensitivity 53%, specificity 95%), and with the combination of normalized fEMG-related response entropy, electrocardiography (ECG) R-to-R interval (RRI), and PPG dicrotic notch amplitude during sevoflurane anesthesia (overall accuracy 96%, sensitivity 90%, specificity 100%). ECG electrode impedances after alcohol swab skin pretreatment alone were higher than impedances of designated EEG electrodes. The BIS values registered with ECG electrodes were higher than those registered simultaneously with EEG electrodes. No significant difference in the time to home-readiness after isoflurane-N2O or sevoflurane-N2O anesthesia was found, when the administration of the volatile agent was guided by BIS monitoring. All other early and intermediate recovery parameters were also similar. Transient epileptiform EEG activity was detected in eight of 15 sevoflurane patients during a rapid increase in the inspired volatile concentration, and in none of the 16 desflurane patients. The observed transient EEG changes did not adversely affect the recovery of the patients. Following the rapid increase in the inhaled desflurane concentration, HR increased transiently, reaching its maximum in two minutes. In the sevoflurane group, the increase was slower and more subtle. In conclusion, desflurane may be a safer volatile agent than sevoflurane in patients with a lowered seizure threshold. The tachycardia induced by a rapid increase in the inspired desflurane concentration may present a risk for patients with heart disease. Designated EEG electrodes may be superior to ECG electrodes in EEG BIS monitoring. When the administration of isoflurane or sevoflurane is adjusted to maintain BIS values at 50-60 in healthy ambulatory surgery patients, the speed and quality of recovery are similar after both isoflurane-N2O and sevoflurane-N2O anesthesia. When anesthesia is maintained by the inhalation of N2O and bolus doses of propofol and alfentanil in healthy unparalyzed patients, movement responses may be best avoided by ensuring a relatively deep hypnotic level with propofol. HR/RRI, fEMG, and PPG dicrotic notch amplitude are potential indicators of nociception during anesthesia, but their performance needs to be validated in future studies. Combining information from different sources may improve the discrimination of the level of nociception.Useita aivosähkökäyrään (EEG) perustuvia unen syvyyden valvontamenetelmiä on kehitetty yleisanestesian aikana käytettäväksi. Kudosvaurion aiheuttamia ärsykkeitä vaimentavan yleisanestesian osan arviointimenetelmien tutkimus on alkamassa. Tässä väitöskirjatyössä tutkittiin unen syvyyden ja kudosvaurion aiheuttamien ärsykkeiden vaimennuksen vuorovaikutusta, useiden elimistön toimintaa kuvaavien muuttujien ja kudosvaurion aiheuttaman ärsytyksen suhdetta sekä EEG:n bispektraali-indeksin (BIS) käyttöä yleisanestesian valvonnassa. Lisäksi verrattiin desfluraani- ja sevofluraanianestesiaan liittyviä EEG-muutoksia ja sykevasteita. Kun anestesia oli aloitettu propofolilla, alfentaniililla ja typpioksiduulilla (N2O), propofoliannos 0.7 mg/kg esti liikevasteiden toistumista kohdun kaavinnan aikana tehokkaammin kuin alfentaniiliannos 0.5 mg. Sydämen syke ja useat sykevaihtelusta, otsalihastoiminnasta, pulssiaallon mittauksesta ja EEG:sta johdetut muuttujat olivat yhteydessä leikkauksen aiheuttamiin liikevasteisiin. Pääsääntöisesti ärsykkeen jälkeen mitatut arvot sellaisenaan tai suhteutettuna ärsykettä edeltäviin arvoihin (normalisoituna) erottelivat liikkuvat potilaat liikkumattomista potilaista. Paras luokittelu saavutettiin kohdun kaavinnan aikana yhdistämällä tieto normalisoidusta otsalihastoiminnasta ja sykkeestä (herkkyys 53%, tarkkuus 95%), ja sevofluraanianestesian aikana yhdistämällä tieto normalisoidusta (otsalihastoimintaan liittyvästä) vaste-entropiasta, sykkeestä ja pulssiaallon muodosta (herkkyys 90%, tarkkuus 100%). Kun iho valmisteltiin vain pyyhkäisemällä alkoholilla, sydänsähkökäyrän (EKG) mittaukseen tarkoitettujen elektrodien sähköinen vastus oli suurempi kuin EEG:n mittaukseen tarkoitettujen elektrodien. EKG-elektrodeilla mitatut BIS-arvot olivat korkeampia kuin samanaikaisesti EEG-elektrodeilla mitatut BIS-arvot. Kun höyrystyviä anestesia-aineita annosteltiin BIS-arvojen mukaan, toipuminen oli yhtä nopeaa isofluraani-N2O-anestesian jälkeen kuin sevofluraani-N2O-anestesian jälkeen. Kun höyrystyvän anestesia-aineen pitoisuutta nostettiin nopeasti viiden minuutin ajaksi, ohimenevää epileptistä aivosähkötoimintaa havaittiin kahdeksalla viidestätoista sevofluraanipotilaasta, mutta ei yhdelläkään kuudestatoista desfluraanipotilaasta. Havaitut ohimenevät EEG-muutokset eivät vaikuttaneet haitallisesti potilaiden toipumiseen. Desfluraanipitoisuuden nopean noston jälkeen syke kiihtyi ohimenevästi ja saavutti huippunsa kahdessa minuutissa. Sevofluraaniryhmässä sykenousu oli hitaampi ja lievempi. Tulosten perusteella voidaan todeta, että desfluraani saattaa olla sevofluraania turvallisempi anestesia-aine potilailla joiden kouristuskynnys on alentunut. Nopeaan desfluraanipitoisuuden nostoon liittyvä sykenousu voi olla haitallinen sydänsairaille potilaille. EEG-elektrodit ovat BIS-mittauksessa parempia kuin halvemmat EKG-elektrodit. Kun höyrystyvä anestesia-aine annostellaan siten että BIS-arvo pysyy välillä 50-60, toipuminen on samankaltainen sekä isofluraani-N2O- että sevofluraani-N2O-anestesian jälkeen terveillä päiväkirurgisilla potilailla. Kun anestesiaa ylläpidetään N2O:lla sekä propofoli- ja alfentaniiliannoksilla terveillä potilailla, joiden lihastoimintaa ei ole lamattu, liikevasteita estetään mahdollisesti parhaiten pitämällä uni verrattain syvänä propofolin avulla. Sydämen syke, otsalihastoiminta ja pulssiaallon muoto saattavat soveltua kudosvaurion aiheut-taman ärsytyksen valvontaan anestesian aikana, mutta lisätutkimuksia aiheesta tarvitaan. Kudosvaurion aiheuttaman ärsytyksen arviointia voidaan mahdollisesti tarkentaa yhdistämällä tietoa useista elimistön toimintaa kuvaavista muuttujista

The clinical and electrophysiological investigation of tremor

The various forms of tremor are now classified in two axes: clinical characteristics (axis 1) and etiology (axis 2). Electrophysiology is an extension of the clinical exam. Electrophysiologic tests are diagnostic of physiologic tremor, primary orthostatic tremor, and functional tremor, but they are valuable in the clinical characterization of all forms of tremor. Electrophysiology will likely play an increasing role in axis 1 tremor classification because many features of tremor are not reliably assessed by clinical examination alone. In particular, electrophysiology may be needed to distinguish tremor from tremor mimics, assess tremor frequency, assess tremor rhythmicity or regularity, distinguish mechanical-reflex oscillation from central neurogenic oscillation, determine if tremors in different body parts, muscles, or brain regions are strongly correlated, document tremor suppression or entrainment by voluntary movements of contralateral body parts, and document the effects of voluntary movement on rest tremor. In addition, electrophysiologic brain mapping has been crucial in our understanding of tremor pathophysiology. The electrophysiologic methods of tremor analysis are reviewed in the context of physiologic tremor and pathologic tremors, with a focus on clinical characterization and pathophysiology. Electrophysiology is instrumental in elucidating tremor mechanisms, and the pathophysiology of the different forms of tremor is summarized in this review

Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses

Desenvolvimento de metodologia baseada em aprendizado por reforço e Sistema de Inferência Fuzzy para identificação e minimização de contaminantes em sinais de sEMG com aplicação em identificação de movimentos do segmento mão-braço

A incessante busca por novas tecnologias que proporcionem aumento da qualidade de vida do ser humano tem norteado a pesquisa acadêmica ao longo da história. Isso é observado na evolução dos meios de transporte, dos dispositivos de comunicação e até mesmo de serviços como o bancário. No entanto, para pessoas com deficiência motora, em especial aquelas que sofreram amputação ou não possuem parte do membro superior, a conquista de melhores condições de vida está potencialmente relacionada com liberdade e independência. Visando suprir esta necessidade, muitos pesquisadores têm trabalhado no desenvolvimento de algoritmos preditores de movimento do segmento mão-braço a partir de sinais de eletromiografia para o controle de próteses na expectativa de aumentar o número de graus de liberdade do dispositivo. Contudo, para que se obtenha sistemas eficientes e que tenham elevados índices de assertividade, é imprescindível que o nível de interferência e ruído, os quais inevitavelmente estão presentes nos registros de eletromiografia devido à instrumentação, ambiente, aspectos fisiológicos, dentre outros, seja o menor possível. Neste contexto, alguns trabalhos foram desenvolvidos visando a minimização do efeito de interferências no classificador, contudo todos aqueles abrangidos pela pesquisa realizada demandam um estágio de treinamento off-line, não são adaptáveis às variações do sinal de EMG e/ou dependem do sinal dos outros canais de medição para a minimização do efeito degradador. Diante disso, a presente proposta de tese apresenta uma metodologia baseada em aprendizagem por reforço (Reinforcement Learning) e Sistema de Inferência Fuzzy para detecção, identificação do tipo e atenuação do efeito de contaminantes em registros de eletromiografia, com aplicação em sistemas de reconhecimento de gestos do membro superior. O mesmo está fundamentado em um modelo de agente e ambiente, sendo constituído dos seguintes elementos: ambiente (atividade elétrica muscular), estado (conjunto de 6 características extraídas do sinal de EMG), ações (aplicação de filtros/procedimentos específicos para a redução do impacto de cada interferência) e agente (controlador que fará a identificação do tipo da contaminação e executará a ação adequada). Para cada ação exercida pelo agente será atribuída uma recompensa a qual, por sua vez, é determinada em virtude do impacto da primeira nas características do sinal (estado) por meio de um Sistema de Inferência Fuzzy. O treinamento, realizado através do método Ator-Crítico, consiste na obtenção de uma política de ações que maximize a recompensa percebida a longo prazo. Por meio de um experimento realizado de forma off-line conseguiu-se taxas de acerto de 92,96% na identificação de 4 tipos de contaminantes (interferência por eletrocardiografia (ECG), artefato de movimento, interferência eletromagnética oriunda da rede de energia elétrica e ruído branco gaussiano) e 69,5% quando se considerou também sinal íntegro. Além disso, por meio de um estudo de caso simulando-se o treinamento online do agente evidenciou-se que o modelo de Transfer Learning adotado foi eficaz na dispensa da necessidade do uso de dados adquiridos previamente do usuário além de acelerar o processo de aprendizado. Estas propriedades são fundamentais para a implementação de qualquer sistema de forma online. Logo, verificou-se indícios de que o SIF-ACRL tem, de fato, potencial para ser implementado de forma online.The incessant search for new technologies that provide increased quality of life for human beings has guided academic research throughout history. This is observed in the evolution of transports, communication devices and even services such as banking. However, for people with motor disabilities, especially those who have had an amputation or do not have part of the upper limb, achieving better living conditions is potentially related to freedom and independence. To meet this need, many researchers have been working on the development of hand-arm segment movement predictors algorithms from electromyography signals for the control of prostheses in the hope of increasing the device's degrees of freedom. However, to obtain efficient systems that have high levels of assertiveness, it is essential that the interference and noise level, which are inevitably present in the electromyography records due to the instrumentation, environment, physiological aspects, among others, is the lowest possible. In this context, some works were developed aiming at minimizing the effect of interference in the classifier, however, all those covered by the performed research demand an offline training stage, are not adaptable to the EMG signal variations, and/or depend on the signal of others measurement channels to minimize the degrading effect. In view of this, the present thesis proposal presents a methodology based on Reinforcement Learning and Fuzzy Inference System for detection, identification of the type and mitigation of the effect of contaminants in electromyography records, with application in gesture recognition systems of the upper limb. It is based on an agent and environment model, consisting of the following elements: environment (muscle electrical activity), state (set of 6 characteristics extracted from the EMG signal), actions (application of specific filters/procedures to reduce impact of each interference) and agent (controller who will identify the type of contamination and take the appropriate action). For each action performed by the agent, a reward will be attributed which, in turn, is determined by the impact of the actions on the signal features (state) by means of a Fuzzy Inference System. The training, carried out through the Actor-Critic method, consists of obtaining an action policy that maximizes the long term perceived reward. Through an experiment carried out offline, success rates of 92.96% were achieved in the identification of 4 types of contaminants (interference by electrocardiography (ECG), motion artifact, electromagnetic interference from the electricity network and Gaussian white noise) and 69.5% when a clean signal class was added. In addition, a case study simulating the agent's online training showed that the Transfer Learning model adopted was effective in dispensing with the need to use data previously acquired from the user, in addition to accelerating the learning process. These properties are fundamental for the implementation of any system online. Therefore, there were indications that the SIF-ACRL has the potential to be implemented online

Human skill capturing and modelling using wearable devices

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution

Development of nonlinear techniques based on time-frequency representation and information theory for the analysis of EEG signals to assess different states of consciousness

Electroencephalogram (EEG) recordings provide insight into the changes in brain activity associated with various states of anesthesia, epilepsy, brain attentiveness, sleep disorders, brain disorders, etc. EEG's are complex signals whose statistical properties depend on both space and time. Their randomness and non-stationary characteristics make them impossible to be described in an accurate way with a simple technique, requiring analysis and characterization involves techniques that take into account their non-stationarity. For that, new advanced techniques in order to improve the efficiency of the EEG based methods used in the clinical practice have to be developed. The main objective of this thesis was to investigate and implement different methods based on nonlinear techniques in order to develop indexes able to characterize the frequency spectrum, the nonlinear dynamics and the complexity of the EEG signals recorded in different state of consciousness. Firstly, a new method for removing peak and spike in biological signal based on the signal envelope was successfully designed and applied to simulated and real EEG signals, obtaining performances significantly better than the traditional adaptive filters. Then, several studies were carried out in order to extract and evaluate EEG measures based on nonlinear techniques in different contexts such as the automatic detection of sleepiness and the characterization and prediction of the nociceptive stimuli and the assessment of the sedation level. Four novel indexes were defined by calculating entropy of the Choi-Williams distribution (CWD) with respect to time or frequency, by using the probability mass function at each time instant taken independently or by using the probability mass function of the entire CWD. The values of these indexes tend to decrease, with different proportion, when the behavior of the signals evolved from chaos or randomness to periodicity and present differences when comparing EEG recorded in eyes-open and eyes-closed states and in ictal and non-ictal states. Measures obtained with time-frequency representation, mutual information function and correntropy, were applied to EEG signals for the automatic sleepiness detection in patients suffering sleep disorders. The group of patients with excessive daytime sleepiness presented more power in ¿ band than the group without sleepiness, which presented higher spectral and cross-spectral entropy in the frontal zone in d band. More complexity in the occipital zone was found in the group of patients without sleepiness in ß band, while a stronger nonlinear coupling between the occipital and frontal regions was detected in patients with excessive daytime sleepiness, in ß band. Time-frequency representation and non-linear measures were also used in order to study how adaptation and fatigue affect the event-related brain potentials to stimuli of different modalities. Differences between the responses to infrequent and frequent stimulation in different recording periods were found in series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation. Nonlinear measures calculated on EEG filtered in the traditional frequency bands and in higher frequency bands improved the assessment of the sedation level. These measures were obtained by applying all the developed techniques on signals recorded from patients sedated, in order to predict the responses to pain stimulation such as nail bad compression and endoscopy tube insertion. The proposed measures exhibit better performances than the bispectral index (BIS), a traditional indexes used for hypnosis assessment. In conclusion, nonlinear measures based on time-frequency representation, mutual information functions and correntropy provided additional information that helped to improve the automatic sleepiness detection, the characterization and prediction of the nociceptive responses and thus the assessment of the sedation level.El registro de la señal Electroencefalografíca (EEG) proporciona información sobre los cambios en la actividad cerebral asociados con varios estados de la anestesia, la epilepsia, la atención cerebral, los trastornos del sueño, los trastornos cerebrales, etc. Los EEG son señales complejas cuyas propiedades estadísticas dependen del espacio y del tiempo. Sus características aleatorias y no estacionarias hacen imposible que el EEG se describa de forma precisa con una técnica sencilla requiriendo un análisis y una caracterización que implica técnicas que tengan en cuenta su no estacionariedad. Todo esto aumenta la necesidad de desarrollar nuevas técnicas avanzadas con el fin de mejorar la eficiencia de los métodos utilizados en la práctica clínica que son basados en el análisis de EEG. En esta tesis se han investigado y aplicado diferentes métodos utilizando técnicas no lineales con el fin de desarrollar índices capaces de caracterizar el espectro de frecuencias, la dinámica no lineal y la complejidad de las señales EEG registradas en diferentes estados de conciencia. En primer lugar, se ha desarrollado un nuevo algoritmo basado en la envolvente de la señal para la eliminación de ruido de picos en las señales biológicas. Este algoritmo ha sido aplicado a señales simuladas y reales obteniendo resultados significativamente mejores comparados con los filtros adaptativos tradicionales. Seguidamente, se han llevado a cabo varios estudios con el fin de extraer y evaluar las medidas de EEG basadas en técnicas no lineales en diferentes contextos. Se han definido nuevos índices mediante el cálculo de la entropía de la distribución de Choi-Williams (DCW) con respecto al tiempo o la frecuencia. Se ha observado que los valores de estos índices tienden a disminuir, en diferentes proporciones, cuando el comportamiento de las señales evoluciona de caótico o aleatorio a periódico. Además, se han encontrado valores diferentes de estos índices aplicados a la señal EEG registrada en diferentes estados. Diferentes medidas basadas en la representación tiempo-frecuencia, la función de información mutua y la correntropia se han aplicado al EEG para la detección automática de la somnolencia en pacientes que sufren trastornos del sueño. Se ha observado en la zona frontal que la potencia en la banda θ es mayor en los pacientes con somnolencia diurna excesiva, mientras que la entropía espectral y la entropía espectral cruzada en la banda δ es mayor en los pacientes sin somnolencia. En el grupo sin somnolencia se ha encontrado más complejidad en la zona occipital, mientras que el acoplamiento no lineal entre las regiones occipital y frontal ha resultado más fuerte en pacientes con somnolencia diurna excesiva, en la banda β. La representación tiempo-frecuencia y las medidas no lineales se han utilizado para estudiar cómo la adaptación y la fatiga afectan a los potenciales cerebrales relacionados con estímulos térmicos, eléctricos y auditivos. Analizando el promedio de varias épocas de EEG grabadas después de la estimulación, se han encontrado diferencias entre las respuestas a la estimulación frecuente e infrecuente en diferentes períodos de registro. Todas las técnicas que se han desarrollado, se han aplicado a señales EEG registradas en pacientes sedados, con el fin de predecir las respuestas a la estimulación del dolor. Un conjunto de medidas calculadas en señales EEG filtradas en diferentes bandas de frecuencia ha permitido mejorar la evaluación del nivel de sedación. Las medidas propuestas han presentado un mejor rendimiento comparado con el índice bispectral, un indicador de hipnosis tradicional. En conclusión, las medidas no lineales basadas en la representación tiempofrecuencia, funciones de información mutua y correntropia han proporcionado informaciones adicionales que contribuyeron a mejorar la detección automática de la somnolencia, la caracterización y predicción de las respuestas nociceptivas y por lo tanto la evaluación del nivel de sedación