11,692 research outputs found
Exploring differences in electromyography and force production between front and back squats before and after fatigue and how this differs between the sexes
Limited research has been conducted to explore sex differences in biomechanical and physiological demands of the front and back squat, especially in response to fatigue where technique may be altered. Therefore, this study investigated differences in electromyography and force production in performance of back and front squats before and after a fatigue protocol and how this differed between males and females. 35 participants (5 female, 30 male) performed a fatigue protocol for back and front squats with measures of maximal performance pre and post. Main findings were that mean and peak activation of the semitendinosus was greater in the back squat than the front squat suggesting that the back squat has greater hamstring activation possibly for hip stabilisation and knee flexion (p < 0.05). There were no differences in quadricep activation between back and front squats, disputing the notion that front squats have a greater quadricep focus, however, lending support to the hypothesis that quadricep activation equal to the back squat can be achieved with lighter absolute load in a front squat. There were no differences in electromyography as a result of fatigue however force production decreased for back squats following fatigue (p < 0.01). This decrease could result from decreased acceleration out of the bottom position and into the concentric phase. This study also presents preliminary findings of greater mean and peak rectus femoris activation in females compared to males in both front (p < 0.01) and back squats (p < 0.05). This was suggested to be in order to support the knee and in an attempt to prevent knee valgus and excess hip adduction. These findings have implications in programming for both high performance sport and for rehabilitation of lower limb injuries
Underwater optical wireless communications in turbulent conditions: from simulation to experimentation
Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel.
This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated.
Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link.
Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions
The Role of Transient Vibration of the Skull on Concussion
Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bonesâ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
Influence of sensorimotor ” rhythm phase and power on motor cortex excitability and plasticity induction, assessed with EEG-triggered TMS
In dieser Arbeit werden zwei Experimente vorgestellt, bei denen EEG-getriggerte
transkranielle Magnetstimulation (TMS) an gesunden Probanden eingesetzt wurde,
um die Rolle des sensomotorischen 8-14Hz ”-Rhythmus auf die kortikospinale
Erregbarkeit (CSE) und die Induktion positiver PlastizitÀt zu untersuchen. Unser
Ziel war es, fĂŒr PlastizitĂ€tsinduktion gĂŒnstige Zeitpunkte im EEG zu identifizieren,
um in Zukunft die EffektivitÀt solcher zurzeit oft noch unzuverlÀssigen Anwendungen zu steigern. Unser EEG-TMS System interpretierte Oszillationen im EEG in
Echtzeit und löste einen Stimulus aus, wenn bestimmte, vorher festgelegte Eigenschaften zutrafen. Die âGehirnwellenâ im EEG entstehen durch synchronisierte
Fluktuationen des Membranpotentials kortikaler Neurone, welche aufgrund ihrer
intrakortikalen Kommunikationsfunktion wertvolle Informationen ĂŒber neuronale
Erregbarkeit vermitteln. Im Gegensatz zu âopen-loopâ TMS ermöglicht EEG-TMS
nicht nur eine prÀzisere Erforschung der Funktion von Gehirnwellen, sondern
auch die Umsetzung der gewonnenen Erkenntnisse in effizientere therapeutische Anwendungen. Speziell Oszillationen im Alpha-Frequenzbereich (8-14Hz)
spielen eine bedeutsame Rolle, indem sie den Informationsfluss im Gehirn durch
Hemmung aktuell irrelevanter Areale steuern, und zwar laut einer fĂŒhrenden Theorie als âasymmetrisch gepulste Inhibitionâ mit einem Maximum der Hemmung
wĂ€hrend der Hochpunkte (âPeaksâ) und wĂ€hrend hoher âPowerâ (⌠Amplitude).
Der â”-Rhythmusâ, Wellen in alpha-Frequenz ĂŒber dem sensomotorischen Kortex, scheint fĂŒr diese Areale eine analoge Rolle wie das okzipitale Alpha fĂŒr den
visuellen Kortex zu spielen. Die CSE lÀsst sich durch die Amplitude der ausgelösten kontralateralen Muskelzuckungen (MEPs im EMG) quantifizieren.
Im Vorexperiment erforschten wir den Einfluss der Power der ”-Wellen auf die
CSE. 16 Teilnehmer wurden in einer Sitzung mit Einzelpuls-TMS des linken M1
stimuliert. Die Pulse wurden durch die momentane Power ausgelöst, 10 Dezile
des individuellen ”-Powerspektrums wurden in pseudorandomisierter Reihenfolge angesteuert, verteilt auf 4 Stimulationsblöcke. Nach BerĂŒcksichtigung der
âInter-Trial-Intervalleâ (ITIs, bekannter âConfounderâ) und Normalisierung pro Block
zeigten unsere Daten eine schwache positiv-lineare Korrelation zwischen ” Power
und MEP-Amplitude, welche somit im Widerspruch zur angenommenen hemmenden Wirkung von ” steht, aber mittlerweile in mehreren anderen Studien
repliziert wurde. Diese Diskrepanz kann z.B. durch eine tatsÀchlich fazilitatorische
Wirkung erklÀrt werden, oder auch durch eine anatomisch dem sensorischen
Kortex (S1) zuzuordnende Quelle der angesteuerten ”-Wellen, was ĂŒber hem-
83mende Interneurone von S1 auf M1 zu einer âVorzeichenumkehrungâ der Effektrichtung fĂŒhren könnte. Weiterhin wird eine AbhĂ€ngigkeit der âerregbarstenâ
Power-Werte von der StimulusstÀrke diskutiert.
Im Hauptexperiment sollte mit âpaarig-assoziativer Stimulationâ (PAS) (intervallsensitive Kombination von Elektrostimulation des rechten Nervus medianus mit TMS
des linken M1) positive PlastizitĂ€t (die Intervention ĂŒberdauernde StĂ€rkung von
Synapsen) induziert werden. Dem ging ein umfangreiches âScreeningâ zur Identifikation geeigneter Probanden mit ausgeprĂ€gtem ”-Rhythmus (fĂŒr prĂ€zise EEGTriggerung) voraus. Letztlich absolvierten 16 Teilnehmer je 4 Sitzungen (eine pro
Trigger-Bedingung). Unsere Hypothese war hierbei, mehr PlastizitĂ€t nach Stimulation wĂ€hrend der Tiefpunkte (âTroughsâ) als wĂ€hrend der Peaks zu erzielen,
also mehr synaptische âFormbarkeitâ wĂ€hrend höherer Erregbarkeit. In Anbetracht der schwachen Ergebnisse des Vorexperiments sowie einer widersprĂŒchlichen Beweislage bezĂŒglich einer fazilitatorischen oder inhibitorischen Funktion
wurden hohe und niedrige Power nicht explizit miteinander verglichen. TMS
wÀhrend PAS wurde durch (1) ”-Peaks, (2) ”-Troughs, (3) mittlere ”-Power und
(4) open-loop getriggert. (3) und (4) dienten jeweils als Kontrollbedingung. PAS
konnte, unabhÀngig von der EEG-Bedingung, keine signifikante VerÀnderung der
MEP-Amplituden vom Ausgangswert hervorrufen. Die fehlende Wirkung könnte durch intra- und interindividuelle Schwankungen gewisser Parameter zwischen den Sitzungen erklÀrt werden (z.B. MEP-Ausgangswerte, absolute ”-Power
wÀhrend PAS), die sich jedoch nicht als systematische Confounder zwischen
EEG-Bedingungen herausstellten.
Die, im Gegensatz zu open-loop-Studien, schwankenden ITIs wÀhrend der PAS
könnten die Wirkung ebenfalls beeintrÀchtigt haben. Weiterhin waren zwei verschiedene Kortexareale (S1 und M1) am Protokoll beteiligt, was die Identifikation
einer relevanten EEG-Eigenschaft erschwerte.
GegenwÀrtig rufen PlastizitÀts-induzierende TMS-Protokolle in der Forschung und
in Studien mit Schlaganfallpatienten schwankende und zeitlich begrenzte Wirkungen hervor. Durch EEG-Triggerung und / oder die Kombination mit klassischer
Physiotherapie könnte eine verbesserte EffektivitĂ€t und somit eine routinemĂ€Ăige
Anwendung erreicht werden. Trotz unserer negativen Ergebnisse bleibt EEG-getriggerte TMS ein vielversprechendes Instrument in Forschung und Klinik.This thesis presents two experiments employing real-time EEG-triggered transcranial magnetic stimulation (TMS) on healthy volunteers to investigate the role
of sensorimotor 8-14Hz ” rhythm in EEG at rest on corticospinal excitability and
induction of positive plasticity. We intended to identify brain states favorable to
induction of positive plasticity to inform development of more efficient TMS protocols for clinical application e.g. in stroke patients.
Applying TMS triggered by pre-determined EEG brain states in real time (opposed to open-loop TMS with post-hoc trial sorting) offers not only more precise
research into the role of certain brain waves, but also translation into more efficient therapies. The membrane potential of superficial cortical neurons fluctuates
rhythmically, visible as oscillations in surface EEG. Different brain areas seem to
communicate through these synchronized fluctuations. âBrain wavesâ therefore
convey valuable information about the excitability of said areas.
Oscillations in the alpha frequency range (8-14Hz) play a crucial role in this, gating information by inhibiting brain areas irrelevant to the current task. According to
an influential hypothesis, this function is exerted as an âasymmetric pulsed inhibitionâ, with a maximum of inhibition during the peaks and during high alpha power
(⌠amplitude). Sensorimotor alpha frequency waves (” rhythm) play a similar role
as the well-researched occipital alpha does for the visual cortex. The primary motor cortex (M1) provides a quantifiable measure of (corticospinal) excitability, the
amplitude of TMS-elicited contralateral muscle twitches (appearing as MEPs in
the EMG).
The first experiment investigated the role of ” power for M1 excitability. 16 participants underwent one session of single-pulse TMS of the left M1, triggered by
overall 10 individual power deciles in pseudorandomized order, partitioned into
4 âblocksâ of stimulation over time. The data revealed, after stratification for confounding inter-trial-intervals (ITIs) and normalization to block average, a weak
positive linear relationship contrary to the proposed inhibitory role of ”, which has
however since been replicated several times in other studies. This discrepancy
can be explained e.g. by an in fact facilitatory nature of ”, by a postcentral and
thus sensory cortical (S1) source of the targeted oscillations, reversing the inhibitory effect in sign to a facilitatory one through S1-to-M1 feedforward inhibition,
or by a shift of most excitable power values dependent on stimulus strength.
For the main experiment, we applied a paired associative stimulation (PAS) pro-
81tocol intended to induce positive plasticity (strengthening of synaptic connection
outlasting the intervention), combining electrical stimulation of the right median
nerve at the wrist with a TMS of the left M1 in a temporally sensitive manner. After an extensive screening to pre-select suitable subjects with a sufficiently strong
” rhythm (to ensure accurate performance of the real-time EEG targeting), 16
participants completed 4 sessions (one condition each). We expected to induce
more positive plasticity during more excitable brain states, i.e., ” troughs rather
than ” peaks. In light of our findings on ” power from the first experiment (weak
influence as compared to ITIs and intrinsic variability over time) and overall contradictory evidence as to its (facilitatory versus inhibitory) role, high vs. low power
were not explicitly compared. TMS during PAS was applied at (1) ” peaks, (2)
” troughs, (3) at medium ” powers and (4) open-loop. (3) and (4) both served
as controls. The intervention failed to evoke a significant change in MEP amplitudes from baseline irrespective of condition. Possible explanations can be found
in the intra- and interindividual variability of decisive parameters across sessions
(e.g. baseline amplitudes and absolute ” powers during PAS), which however did
not significantly depend on the targeted condition and were thus not true confounders. The number of sessions might still have introduced a further measure
of variability. Varying PAS ITIs (due to EEG-triggering) could have also impeded
plasticity induction, and the involvement of two cortical regions (S1 and M1) might
have complicated the identification of one relevant brain state.
Currently, plasticity-inducing TMS protocols in research and clinical trials evoke
variable and transient effects. Improvements to enable routine application might
come from EEG-triggering and/or combining with traditional motor training (physiotherapy). Regardless of our nil results in plasticity induction, EEG-triggered
TMS remains a promising instrument in research and therapy
Application of wearable sensors in actuation and control of powered ankle exoskeletons: a Comprehensive Review
Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Humanâmachine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the userâs locomotion intention and requirement, whereas machineâmachine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environmentâmachine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided
Review on biomedical sensors, technologies, and algorithms for diagnosis of sleep-disordered breathing: Comprehensive survey
This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB
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