Efficient Parallel Carrier Recovery for Ultrahigh Speed Coherent QAM Receivers with Application to Optical Channels

This work presents a new efficient parallel carrier recovery architecture suitable for ultrahigh speed intradyne coherent optical receivers (e.g., ≥100 Gb/s) with quadrature amplitude modulation (QAM). The proposed scheme combines a novel low-latency parallel digital phase locked loop (DPLL) with a feedforward carrier phase recovery (CPR) algorithm. The new low-latency parallel DPLL is designed to compensate not only carrier frequency offset but also frequency fluctuations such as those induced by mechanical vibrations or power supply noise. Such carrier frequency fluctuations must be compensated since they lead to higher phase error variance in traditional feedforward CPR techniques, significantly degrading the receiver performance. In order to enable a parallel-processing implementation in multigigabit per second receivers, a new approximation to the DPLL computation is introduced. The proposed technique reduces the latency within the feedback loop of the DPLL introduced by parallel processing, while at the same time it provides a bandwidth and capture range close to those achieved by a serial DPLL. Simulation results demonstrate that the effects caused by frequency deviations can be eliminated with the proposed low latency parallel carrier recovery architecture.Fil: Gianni, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Electrónica. Laboratorio de Comunicaciones Digitales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ferster, Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Electrónica. Laboratorio de Comunicaciones Digitales; ArgentinaFil: Corral Briones, Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Electrónica. Laboratorio de Comunicaciones Digitales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hueda, Mario Rafael. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Electrónica. Laboratorio de Comunicaciones Digitales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Auditory deep sleep stimulation in older adults at home: a randomized crossover trial

Background Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings. Methods We present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition. Results Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof. Conclusions While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology

Boosting Recovery During Sleep by Means of Auditory Stimulation

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1–4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep

Benchmarking Real-Time Algorithms for In-Phase Auditory Stimulation of Low Amplitude Slow Waves With Wearable EEG Devices During Sleep

OBJECTIVE: In-phase stimulation of EEG slow waves (SW) during deep sleep has shown to improve cognitive function. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration. However, existing algorithms to estimate the up-phase of EEG suffer from a poor phase accuracy at low amplitudes and when SW frequencies are not constant. METHODS: We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices, a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 μV, and SW frequencies above 1 Hz on 324 home-based recordings from healthy older adults and Parkinson disease (PD) patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated in simulation and with a PD patient. RESULTS: All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The hardware testing revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower. Active stimulation did not influence the phase tracking. CONCLUSION: This work demonstrated that phase-accurate auditory stimulation can also be delivered during fully remote sleep interventions in populations with low-amplitude SW

Modulating slow wave sleep at home with a mobile EEG system

Since ancient times sleep has fascinated philosophers and researchers alike. But it was not until the beginning of the 20th century that, with the introduction of electroencephalography (EEG), researchers and scientists were able to study sleep through the non-invasive measurements of brain activity. The introduction of EEG has enabled a better understanding of sleep and its relation to health and diseases. Considering the importance of sleep for health and well-being several methods to improve sleep, and more specific slow wave sleep (SWS), through external stimulation have been studied. Auditory stimulation delivered in-phase with slow waves during non-rapid eye movement sleep has shown to be an effective technique to enhance SWS with improvements in memory consolidation and cognition. So far, research using this approach was limited to complex and costly laboratory set-ups that required controlled human interaction. For this reason, lab-based sleep studies have so far included a low number of participants, and have been restricted to very few recording nights, therefore limiting their impact. Thus, the study of sleep in a more natural unobtrusive environment is desired. With the proliferation of mobile EEG technologies, brain activity recordings outside clinical or laboratory settings have become possible allowing for realistic long-term monitoring at larger scale and lower costs. However, available consumer devices for sleep monitoring and interventions, such as auditory stimulation, do not provide enough flexibility and data transparency to be used for research. To address these challenges, we developed a configurable and mobile 8-channel EEG system that enables sleep biosignal recording and autonomous auditory SWS stimulation. The system provides data transparency of the recorded biosignal and implemented algorithms' output for post-processing analysis and flexible electrode selection and position. In addition, it features a general-purpose connection that enables connectivity to other sensors. SWS enhancement has been observed only when the acoustic tones were played during the SW up-phase as compared to nights when the tones were delivered during SW down-phase or no stimulation was presented. Therefore, we propose two novel methodologies to improve the accuracy of state-of-the-art real-time slow wave phase detection, especially in the presence of low-amplitude slow waves. This represents an important contribution, especially for populations whose sleep is hallmarked by low amplitude slow waves. Therefore, elderly or Parkinson patients could benefit most from such non-invasive interventions. Additionally, we propose a novel method to automatically select a filter design for EEG signal preprocessing based on minimum filter-based distortions in the temporal, frequency, or both domains. The results of this thesis contribute to the transition of sleep research to home-based settings and constitute a step toward more robust and accurate in-phase auditory stimulation with resource-constrained, battery-powered devices while maintaining reliable performance

Description and Prediction of Freezing of Gait in Parkinson's Disease from Wearable Inertial Measurements Units

Parkinson's disease (PD) is a neuro-degenerative disorder, the second most common after Alzheimer's disease. After diagnosis, treatments can help to relieve the symptoms, but there is no known cure for PD. PD is characterized by a combination of motor and no-motor dysfunctions. Among the motor symptoms there is the so called Freezing of Gait (FoG). The FoG is a phenomenon in PD patients in which the feet stock to the floor and is difficult for the patient to initiate movement. FoG is a severe problem, since it is associated with falls, anxiety, loss of mobility, accidents, mortality and it has substantial clinical and social consequences decreasing the quality of life in PD patients. Medicine can be very successful in controlling movements disorders and dealing with some of the PD symptoms. However, the relationship between medication and the development of FoG remains unclear. Several studies have demonstrated that visual or auditory rhythmical cuing allows PD patients to improve their motor abilities. Rhythmic auditory stimulation (RAS) was shown to be particularly effective at improving gait, specially with patients that manifest FoG. While RAS allows to reduce the time and the effects of FoGs occurrence in PD patients after the FoG is detected, it can not avoid the episode due to the latency of detection. An improvement of the system would be the prediction of the FoG. This thesis was developed following two main objectives: (1) the finding of specifics properties during pre FoG periods different from normal walking context and other walking events like turns and stops using the information provided by the inertial measurements units (IMUs) and (2) the formulation of a model for automatically detect the pre FoG patterns in order to completely avoid the upcoming freezing event in PD patients. The first part focuses on the analysis of different methods for feature extraction which might lead in the FoG occurrence