The brain is a highly complex network of nonlinear systems with internal dynamic states that are not easily quantified. As a result, it is essential to understand the properties of the connectivity network linking disparate parts of the brain used in complex cognitive processes, such as working memory. Working memory is the system in control of temporary retention and online organization of thoughts for successful goal directed behavior. Individuals exhibit a typically small capacity limit on the number of items that can be simultaneously retained in working memory. To modify network connections and thereby augment working memory capacity, researchers have targeted brain areas using a variety of noninvasive stimulation interventions. However, few existing methods take advantage of the brain's own structure to actively generate and entrain internal oscillatory modulations in locations deep within the auditory pathways. One technique is known as binaural beats, which arises from the brain's interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. The mismatch between these tones is perceived as a so-called beat frequency which can be used to modulate behavioral performance and cortical connectivity. Currently, all binaural stimulation therapeutic systems are open-loop "one-size-fits-all" approaches. However, these methods can prove not as effective because each person's brain responds slightly differently to exogenous stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI), which causes cognitive impairments beyond those expected based on age. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, I will present a comparison of algorithms to determine brain connectivity, results of open-loop based binaural stimulation, the development of a closed-loop brain network simulation platform, and finally an experimental study to determine the effectiveness of closed-loop control to modulate brain networks hence influencing cognitive abilities. / Ph. D. / In order to do complex tasks, such as creating a memory, multiple regions of the brain must interact to become a network. Specifically for this work, we are looking at working memory which is the system that allows us to remember and manipulate information in the presence of additional incoming information. Working memory capacity, which is the number of items we can remember, is dependent upon synchronization between particular regions of the brain, particularly the frontal and parietal lobes. Higher synchronization means that people will, on average, respond with higher accuracy during a working memory task. To modify the connections in the network and thereby augment working memory capacity, a non-invasive brain stimulation technique called binaural beats can be used. Binaural beats take advantage of the brain’s response to two pure tones, delivered independently to each ear, when those tones have a small frequency mismatch. The mismatch between the tones is interpreted as a beat frequency, which may act to synchronize brain waves. This research seeks to answer the question of whether binaural beats can be used to identify and control working memory. Currently, nearly all therapeutic stimulation systems are open-loop “one-size-fits-all” approaches. However, these methods can prove not as effective because each person’s brain responds slightly differently to external stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI) which is considered a precursor to Alzheimer’s. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, we have showed that we can successfully increase the connectivity in the brain using binaural beats in a closed-loop system.

Binaural Beats är ett fenomen som undersöks av diverse forskare kring dess påverkan på hjärnaktivitet och välmående. Kan detta påverka vår prestation i spel? Denna undersökning fokuserar på Theta-zonen som är en av hjärnans tillståndszoner. Med hjälp av specifika ljudfrekvenser (Binaural Beats) kan hjärnvågor stimuleras och bestämt börja arbeta i valfritt tillstånd, i det här fallet Theta-zonen somrepresenterar det lugna och djupt avslappnande stadiet hos människan. För att ta reda på svaret till denna frågeställning skapades en artefakt/ett spel som mätte spelarens prestation genom poänginsamling. Undersökningen samlade in värden i poäng och puls under spelsessionen, som därefter avslutades med en intervju. Resultaten analyserades och sammanfattades till en slutsats i koppling till arbetets frågeställning. Undersökningen och slutsatsen diskuteras och dras vidare till framtida arbeten om förbättringar och utökning av metoden, samt fortsatta studier inom liknande områden.

Syftet med denna studie var att se ifall man med hjälp utav binaural beats kan känna av berusning i form av ett psykedeliskt rus liknande substansen DMT (N, NDimetyltryptamine). Med hjälp av en kvalitativ metod i form av ett lyssningstest, tillsammans med forskning som finns kring substansen så visar det övergripande resultatet på att det inte är riktig berusning som upplevs, utan försökspersonerna visar symptom på det förändrade medvetandetillståndet som upplevs mellan vaka och sömn, det som även kallas för hypnagoga.

<p>Syftet med denna studie var att undersöka effekten av Bani, som representant för Binaural Beat-teknologi, på stress. Som delsyfte undersöktes även ångest och depressivitet. Samplet utgjordes av 27 universitetsstuderande på Örebro universitet. Binaural Beats är ljudfrekvenser med vilka hjärnans EEG sägs synkroniseras och kan påverka medvetandetillståndet. Genom en experimentell design med</p><p>upprepade mätningar och en kontrollgrupp undersöktes</p><p>förändringar i stress, ångest och depressivitet under fyra veckors lyssnande av Bani. Signifikanta effekter över tid påträffades gällande psykisk stress och depressivitet för interventionsgruppen. Vissa gruppskillnader påträffades även för ångest och fysisk stress, vilka dock verkade vara av mer naturligt fluktuerande karaktär. Resultaten indikerade att Bani har en lindrande effekt på psykisk stress och depressivitet.</p><p>Nyckelord. Binaural Beats, stress, hjärnsynkronisering, Bani,</p><p>ångest, depressivitet.</p> / <p>The purpose of this study was to investigate the effect of Bani, as a representative of Binaural Beat technology, on stress. As a secondary purpose anxiety and depression were examined. The sample consisted of 27 university students at the Örebro University. Binaural Beats are sound frequencies with which the brain is said to synchronize and can</p><p>affect states of conciousness. Changes in stress, anxiety and depression were examined through an experimental design with repeated measures and a control group for a period of four weeks of listening to Bani. Significant effects were found over time concerning psychological stress and depression for the intervention group. Some group differences were</p><p>also found for anxiety and physiological stress, although they seemed to be more affected by natural fluctuations. The results indicated that Bani has a decreasing effect on psychological stress and depression.</p><p>Keywords: Binaural Beats, stress, brainwave synchronization, Bani, anxiety, depression.</p>

Syftet med denna studie var att undersöka effekten av Bani, som representant för Binaural Beat-teknologi, på stress. Som delsyfte undersöktes även ångest och depressivitet. Samplet utgjordes av 27 universitetsstuderande på Örebro universitet. Binaural Beats är ljudfrekvenser med vilka hjärnans EEG sägs synkroniseras och kan påverka medvetandetillståndet. Genom en experimentell design med upprepade mätningar och en kontrollgrupp undersöktes förändringar i stress, ångest och depressivitet under fyra veckors lyssnande av Bani. Signifikanta effekter över tid påträffades gällande psykisk stress och depressivitet för interventionsgruppen. Vissa gruppskillnader påträffades även för ångest och fysisk stress, vilka dock verkade vara av mer naturligt fluktuerande karaktär. Resultaten indikerade att Bani har en lindrande effekt på psykisk stress och depressivitet. Nyckelord. Binaural Beats, stress, hjärnsynkronisering, Bani, ångest, depressivitet. / The purpose of this study was to investigate the effect of Bani, as a representative of Binaural Beat technology, on stress. As a secondary purpose anxiety and depression were examined. The sample consisted of 27 university students at the Örebro University. Binaural Beats are sound frequencies with which the brain is said to synchronize and can affect states of conciousness. Changes in stress, anxiety and depression were examined through an experimental design with repeated measures and a control group for a period of four weeks of listening to Bani. Significant effects were found over time concerning psychological stress and depression for the intervention group. Some group differences were also found for anxiety and physiological stress, although they seemed to be more affected by natural fluctuations. The results indicated that Bani has a decreasing effect on psychological stress and depression. Keywords: Binaural Beats, stress, brainwave synchronization, Bani, anxiety, depression.

No description available.

Previous research suggests that larger context effects are observed when participants are required to search a scene in order to find the to-be-remembered stimuli. Similarly, animal research on brain oscillations has shown theta wave activation when animals are searching their environment. These theta wave oscillations are positively correlated with learning. However, theta activation can also occur in response to sensory stimulation, for example, auditory stimulation with binaural beats or visual stimulation with a checkerboard pattern reversal. The results of several studies suggest that while a visual search task seems to reliably improve free recall performance, the effects of passive sensory stimulation on memory are less consistent. Implications and suggestions for future research are discussed.

Exponering till Binaurala Svävningar (BinSv) kan enligt tidigare forskning orsaka ändringar i vårt sinnestillstånd. Hittills har BinSv använts exklusivt i avslappnings- och meditationsmusik men hur kan BinSv användas som ett ramverk inom populärmusik, och hur uppfattas det? Denna studietillämpar forskning genom design metodiken för att redogöra för hur man kan applicera BinSv påolika sätt i skapande av populärmusik och undersöka i kvalitativa semistrukturerade intervjuer hurdetta uppfattas. I resultatet redovisas 3 olika sätt att applicera BinSv-frekvenser: på en basgitarr, påen tillagd statisk sinuston och på en sinuston som följer grundtonen i musiken. Deltagarna i undersökningen har uttryckt utökad avslappning och upprymdhet efter att ha lyssnat på musiken. Vidare diskuteras olika affordanser som BinSv kan erbjuda båda musikskaparen och lyssnaren.

The goal of this study was to explore the potential of using video games with binaural beats as an alternative to regular binaural beat therapy. As previous studies have shown, music therapy, binaural beat therapy, and video games have been used for relaxation purposes to varying success. To prove this, three types of therapies were held in order to test their effectiveness. 16 students participated in the test in which their goal was to partake in one of the three therapy sessions, with two interviews before and after the therapy session. The results showed that a video game therapy with binaural beats could work as an alternative to a standard music therapy session, and that the binaural beats improved the relaxing factor in a video game. However, this area of study is quite new, which means more studies would be needed to ascertain the effectiveness of this kind of therapy. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

This dissertation aims to develop a formal foundation to design an adaptive output feedback predictor for a class of unknown systems where parameters and order are unknown or high-dimensional. We present a reduced-order adaptive output-predictor scheme based on modal reduction and Lyapunov's method. Moreover, the credibility of the proposed reduced-order adaptive output-predictor scheme is validated by mathematical proof, and numerical and experimental studies, such as single pendulum, double pendulum, six-link pendulum, rope as a high-dimensional rope, and EEG data. Then the dissertation goal is to experimentally validate the proposed reduced-order model parameterization technique for tracking uncertain linear time-invariant (LTI) single-input, single-output (SISO) systems. The proposed theory focuses on parameterizing a high-dimensional, uncertain model and introduces a reduced-order adaptive output predictor capable of forecasting the system's output. This predictor utilizes auto-regressive filtered vectors, incorporating the input and output history. The adaptive output predictor is a simplified and known model, making it suitable for controlling high-dimensional, uncertain SISO systems without access to full-state measurements. Specifically, this work establishes the foundation for parameterizing uncertain models, creating a virtual structure that emulates the actual system, and offering a more manageable model for control when the objective is solely to regulate the system's output. The primary focus of this research is to assess the effectiveness and output-tracking capabilities of the proposed approach. These capabilities are extensively examined across diverse platforms and hardware configurations, relying solely on input and output data from the models without incorporating any additional information on the system dynamics. In the first experiment, the predictor's ability to track the angle of a single pendulum, including additional dynamics, is evaluated using only input-output data. The second experiment targets tracking the endpoint of a rope connected to a single pendulum, where the rope emulates a high-dimensional model. A vision system is designed and employed to acquire the rope endpoint position data. Before the rope experiment, a set of experiments is conducted on single pendulum hardware to ensure the accuracy of the vision system's data collection. Comparative analysis between data from object tracking via vision and data acquired through an encoder demonstrates negligible error. Finally, the input and the endpoint output data from the rope experiment are fed into the predictor to assess its capability to track the rope endpoint position without utilizing specific knowledge of the experimental hardware. Achieving negligible error in tracking implies that the predictor provides a simple and accurate representation of the rope dynamics. Consequently, designing a controller for this known model is equivalent to designing a controller for the actual rope system dynamics. The predictor, by closely emulating the behavior of the rope, becomes a reliable surrogate model for control design, simplifying the task of controller design for the complex and uncertain high-dimensional system. Finally, this study introduces a novel approach to enhance controller design for complex brain dynamics by employing a reduced-order adaptive output predictor proposed in [1], fine-tuned with chirp binaural beats. The proposed technique is promising for developing closed-loop controllers in non-invasive brain stimulation therapies, such as binaural beats stimulation, to improve working memory. The study focuses on parameterizing uncertain models and creates a predictor that utilizes auto-regressive filtered vectors to forecast mean phase lock values generated by binaural beats stimulation. The simplified and known model of the predictor proves effective in tracking brain responses, as demonstrated in experiments evaluating its ability to track mean phase locking values. The results indicate negligible tracking error, suggesting the predictor's reliability in representing brain dynamics and simplifying the task of controller design for the complex and uncertain high-dimensional system. / Doctor of Philosophy / This dissertation explores the development of a reduced-order adaptive output predictor for unknown systems with unknown or high-dimensional parameters and order. A reduced-order adaptive output predictor scheme is introduced, validated through mathematical proof, and tested in diverse scenarios, including pendulum systems and EEG data. The focus is on parameterizing uncertain models and creating a simplified adaptive output predictor capable of forecasting system output, specifically for SISO systems. Experimental validation involves tracking the angle of a single pendulum and the endpoint of a high-dimensional rope, demonstrating the predictor's accuracy without detailed knowledge of system dynamics. The study extends its application to complex brain dynamics, using the predictor fine-tuned with chirp binaural beats. Results show promise for developing closed-loop controllers in non-invasive brain stimulation therapies, offering a novel approach to improve working memory via helping to design closed-loop controllers.