Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.by the Spanish Ministry of Science and Innovation, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund in the framework of the project Walk—Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (RTI2018-096677-B-I00);and by the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana) and the European Social Fund in the framework of the project Desarrollo de nuevas interfaces cerebro-máquina para la rehabilitación de miembro inferior (GV/2019/009Authors would like to thank specially Kevin Nathan and the rest of the laboratory of JC-V for their help during the experimental trials and Atilla Kilicarslan for his help with the implementation of H∞ algorithm

Analysis of the biceps brachii muscle by varying the arm movement level and load resistance band

Biceps brachii muscle illness is one of the common physical disabilities that requires rehabilitation exercises in order to build up the strength of the muscle after surgery. It is also important to monitor the condition of the muscle during the rehabilitation exercise through electromyography (EMG) signals. The purpose of this study was to analyse and investigate the selection of the best mother wavelet (MWT) function and depth of the decomposition level in the wavelet denoising EMG signals through the discrete wavelet transform (DWT) method at each decomposition level. In this experimental work, six healthy subjects comprised of males and females (26 ± 3.0 years and BMI of 22 ± 2.0) were selected as a reference for persons with the illness. The experiment was conducted for three sets of resistance band loads, namely, 5 kg, 9 kg, and 16 kg, as a force during the biceps brachii muscle contraction. Each subject was required to perform three levels of the arm angle positions (30°, 90°, and 150°) for each set of resistance band load. The experimental results showed that the Daubechies5 (db5) was the most appropriate DWT method together with a 6-level decomposition with a soft heursure threshold for the biceps brachii EMG signal analysis

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

EEG and ECG nonlinear and spectral multiband analysis to explore the effect of videogames against anxiety

Currently, the use of video games has purposes that go beyond entertainment and has been gaining prominence in the health area. In this sense, it was hypothesized that it is possible to discriminate biological signals, namely electrocardiographic and electroencephalographic signals, collected from different participants stimulated through three different commercial video games, Tetris, Bejeweled and Energy. To test this hypothesis, a protocol was developed with the Trier Social Stress Test to induce and dose stress in the subjects to similar levels before each game session, in order to observe the effects of the three test games (3 study groups) at the physiological level. Initially collected at 2000 Hz, the signals were resampled to 500 Hz and filtered using a Butterworth low-pass filter. After filtering the signals, several representative features of the study signals were collected. These features consisted of a series of nonlinear metrics such as the Lyapunov exponent and Correlation Dimension, self-similarity metrics such as the Hurst exponent, and detrended fluctuation analysis, fractal dimensions - such as the Katz and Higuchi fractal dimensions - and metrics of signal chaos and activity, such as signal energy, Logarithmic entropy and Shannon entropy, and a number of spectral metrics for the EEG signal, which should be able to help identify any differences in the stress response. As a final result, a discrimination accuracy of 100% was obtained to discriminate the three study groups, using the top 20% of features selected by the F-score technique, using the coarse K Nearest Neighbor classifier.Atualmente, o uso de videojogos tem propósitos que vão além do entretenimento e tem vindo a ganhar destaque na área da saúde. Nesse sentido, foi formulada a hipótese de que é possível discriminar sinais biológicos, nomeadamente os sinais eletrocardiográficos e eletroencefalográficos, recolhidos de diferentes participantes estimulados através de três videojogos comerciais diferentes, Tetris, Bejeweled e Energy. Para testar esta hipótese foi desenvolvido um protocolo com o Trier Social Stress Test para induzir e dosear o stress nos sujeitos para níveis semelhantes antes de cada sessão de jogo, de forma a observar os efeitos dos três jogos de teste (3 grupos de estudo) a nível fisiológico. Recolhidos inicialmente a 2000 Hz, os sinais foram reamostrados a 500 Hz e filtrados utilizando um filtro passa-baixo de Butterworth. Após filtragem dos sinais, recolheram-se várias características representativas dos sinais de estudo. Estas características consistiram numa série de métricas não lineares, como o expoente de Lyapunov e a Dimensão de Correlação, métricas de auto similaridade como o exponente de Hurst e a análise de flutuação com trends removidas, dimensões fractais - como as dimensões fractais de Katz e Higuchi - e métricas de caos e atividade dos sinais, como a energia dos sinais, a entropia Logarítmica e a entropia de Shannon, e uma série de métricas espectrais para o sinal EEG, que devem ser capazes de ajudar a identificar qualquer diferença na resposta ao stress. Como resultado final obteve-se uma precisão de discriminação de 100% para discriminar os três grupos de estudo, utilizando as 20% das melhores características selecionadas pela técnica de F-score, recorrendo ao classificador coarse K Nearest Neighbor

Multiple System Modelling and Analysis of Physiological and Brain Activity and Performance at Rest and During Exercise

One of the current interests of exercise physiologists is to understand the nature and control of fatigue related to physical activity to optimise athletic performance. Therefore, this research focuses on the mathematical modelling and analysis of the energy system pathways and the system control mechanisms to investigate the various human metabolic processes involved both at rest and during exercise. The first case study showed that the PCr utilisation was the highest energy contributor during sprint running, and the rate of ATP production for each anaerobic subsystem was similar for each athlete. The second study showed that the energy expenditure derived from the aerobic and anaerobic processes for different types of pacing were significantly different. The third study demonstrated the presence of the control mechanisms, and their characteristics as well as complexity differed significantly for any physiological organ system. The fourth study showed that the control mechanisms manifest themselves in specific ranges of frequency bands, and these influence athletic performance. The final study demonstrated a significant difference in both reaction time and accuracy of the responses to visual cues between the control and exercise-involved cognitive trials. Moreover, the difference in the EEG power ratio at specific regions of the brain; the difference in the ERP components’ amplitudes and latencies; and the difference in entropy of the EEG signals represented the physiological factors in explaining the poor cognitive performance of the participants following an exhaustive exercise bout. Therefore, by using mathematical modelling and analysis of the energy system pathways and the system control mechanisms responsible for homeostasis, this research has expanded the knowledge how performance is regulated during physical activity and together with the support of the existing biological control theories to explain the development of fatigue during physical activity

Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity

The risk of neurodegenerative disorders increases with age, due to reduced vascular nutrition and impaired neural function. However, the interactions between cardiovascular dynamics and neural activity, and how these interactions evolve in healthy aging, are not well understood. Here, the interactions are studied by assessment of the phase coherence between spontaneous oscillations in cerebral oxygenation measured by fNIRS, the electrical activity of the brain measured by EEG, and cardiovascular functions extracted from ECG and respiration effort, all simultaneously recorded. Signals measured at rest in 21 younger participants (31.1±6.9 years) and 24 older participants (64.9±6.9 years) were analysed by wavelet transform, wavelet phase coherence and ridge extraction for frequencies between 0.007 and 4 Hz. Coherence between the neural and oxygenation oscillations at ∼0.1 Hz is significantly reduced in the older adults in 46/176 fNIRSEEG probe combinations. This reduction in coherence cannot be accounted for in terms of reduced power, thus indicating that neurovascular interactions change with age. The approach presented promises a noninvasive means of evaluating the efficiency of the neurovascular unit in aging and disease

Discrimination of cardiac health and disease by assessment of heart rate variability: wavelet vs. fast Fourier transformation

The autonomic nervous system (ANS) modulation of the heart is of clinical importance because of its relevance to risk of life threatening arrhythmic events. Decomposition of heart rate variability (HRV) has been used to quantify ANS control of the heart. The traditional method for frequency analysis has involved the use of fast Fourier transformation (FFT). However, heart rate data typically violate assumptions of the FFT. Therefore, the assessment of HRV may benefit from other, potentially more suitable, mathematical approaches. For example, the discrete wavelet transformation (DWT) appears to have promise with respect to its ability to discriminate between healthy and diseased populations. Therefore, the purpose of this thesis was to examine the extent to which the FFT can discriminate between a control group and heart failure patients (CHF) in comparison to DWT. Seven CHF (mean +/- standard deviation, age: 51.9 +/- 17.6 yrs) and eight age-matched controls (49.5 +/- 17.9 yrs) were evaluated. HRV was evaluated during 5 minutes of supine spontaneous breathing (SB) and supine paced breathing (PB) (0.2Hz). The ECG data were sampled at 200 Hz, converted to heart rate tachograms, and subjected to frequency analysis via FFT and DWT. The FFT approach did not reveal group differences in HRV, while the DWT revealed group differences in LF/HF during SB (p\u3c0.05) and PB (p=0.053). With respect to breathing condition, only the FFT revealed that PB resulted in a decrease in low- to high-frequency ratios (p\u3c0.05), and an increase in standard deviation of normal R-R intervals. These results support further consideration of both methods of analysis, as they each appear to provide unique information about HRV

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Electroencephalography-Based Brain–Machine Interfaces in Older Adults: A Literature Review

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain–machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users’ needs are considered