Search results for: brain signals

Authors: Mohammad Khazaei, Reza Rostami, Hasan Gharayagh Zandi, Rouhollah Basatnia, Mahbubeh Ghayour Najafabadi

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Objective: Methylphenidate has been demonstrated to enhance attention and cognitive processes, and placebo treatments have also been found to improve attention and cognitive processes. Additionally, methylphenidate may have positive effects on motion perception and sports performance. Nevertheless, additional research is needed to fully comprehend the neural mechanisms underlying the effects of methylphenidate and placebo on cognitive and motor functions. Methods: In this randomized controlled trial, 18 young semi-professional basketball players aged 18-23 years were randomly and equally assigned to either a Ritalin or Placebo group. The participants performed 20 consecutive free throws; their scores were recorded on a 0-3 scale. The participants’ brain activity was recorded using electroencephalography (EEG) for 5 minutes seated with their eyes closed. The Ritalin group received a 10 mg dose of methylphenidate, while the Placebo group received a 10mg dose of placebo. The EEG was obtained 90 minutes after the drug was administere Results: There was no significant difference in the absolute power of brain waves between the pre-test and post-tests in the Placebo group. However, in the Ritalin group, a significant difference in the absolute power of brain waves was observed in the Theta band (5-6 Hz) and Beta band (21-30 Hz) between pre- and post-tests in Fp2, F8, and Fp1. In these areas, the absolute power of Beta waves was higher during the post-test than during the pre-test. The Placebo group showed a more significant difference in free throw scores than the Ritalin group. Conclusions: In conclusion, these results suggest that Ritalin effect on brain activity in areas associated with attention and cognitive processes, as well as improve basketball free throws. However, there was no significant placebo effect on brain activity performance, but it significantly affected the improvement of free throws. Further research is needed to fully understand the effects of methylphenidate and placebo on cognitive and motor functions.

Keywords: methylphenidate, placebo effect, electroencephalography, basketball free throw

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Authors: Mariam G. Eshak, Ahmed Abbas, M. I. El-Sabry, M. M. Mashaly

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This investigation was conducted to determine the physiological response and evaluate the expression of inflammatory and cell death genes and DNA damage induced by endotoxic shock in laying hens. Endotoxic shock was induced by a single intravenous injection of 107 Escherichia coli (E. coli,) colony/hen. In the present study, 240 forty-week-old laying hens (H&N) were randomly assigned into 2 groups with 3 replicates of 40 birds each. Hens were reared in battery cages with wire floors in an open-sided housing system under natural conditions. Housing and general management practices were similar for all groups. At 42-wk of age, 45 hens from the first group (15 replicate) were infected with E. coli, while the same number of hens from the second group was injected with saline and served as a control. Heat shock protein-70 (HSP-70) expression, plasma corticosterone concentration, body temperature, and the gene expression of bax, caspase-3 activity, P38, Interlukin-1β (Il-1β), and tumor necrosis factor alpha (TNF-α) genes and DNA damage in the brain and liver were measured. Hens treated with E. coli showed significant (P≤0.05) increase of body temperature by 1.2 ᴼC and plasma corticosterone by 3 folds compared to the controls. Further, hens injected with E.Coli showed markedly over-expression of HSP-70 and increase DNA damage in brain and liver. These results were synchronized with activating cell death program since our data showed significant (P≤0.05) high expression of bax and caspase-3 activity genes in the brain and liver. These results were related to remarkable over-inflammation gene expression of P38, IL-1β, and TNF-α in brain and liver. In conclusion, our results indicate that endotoxic shock induces inflammatory physiological response and triggers cell death program by promoting P38, IL-1β, and TNF-α gene expression in the brain and liver.

Keywords: chicken, DNA damage, Escherichia coli, gene expression, inflammation

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Authors: Igwenagu Chinelo Mercy

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This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

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Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: brain activity, EEG, information processing model, model human processor

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Authors: Clea Southall

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Many children leave primary school having formed preconceptions about their relationship with science. Thus, primary school represents a critical window for stimulating scientific interest in younger children. Engagement relies on the provision of hands-on activities coupled with an ability to capture a child’s innate curiosity. This requires children to perceive science topics as interesting and relevant to their everyday life. Teachers and pupils alike have suggested the school curriculum be tailored to help stimulate scientific interest. Young children are naturally inquisitive about the human body; the brain is one topic which frequently engages pupils, although it is not currently included in the UK primary curriculum. Teaching children about the brain could have wider societal impacts such as increasing knowledge of neurological disorders. However, many primary school teachers do not receive formal neuroscience training and may feel apprehensive about delivering lessons on the nervous system. This is exacerbated by a lack of educational neuroscience resources. One solution is for undergraduates to form partnerships with schools - delivering engaging lessons and supplementing teacher knowledge. The aim of this project was to evaluate the success of a short lesson on the brain delivered by an undergraduate neuroscientist to primary school pupils. Prior to entering schools, semi-structured online interviews were conducted with teachers to gain pedagogical advice and relevant websites were searched for neuroscience resources. Subsequently, a single lesson plan was created comprising of four hands-on activities. The activities were devised in a top-down manner, beginning with learning about the brain as an entity, before focusing on individual neurons. Students were asked to label a ‘brain map’ to assess prior knowledge of brain structure and function. They viewed animal brains and created ‘pipe-cleaner neurons’ which were later used to depict electrical transmission. The same session was delivered by an undergraduate student to 570 key stage 2 (KS2) pupils across five schools in Leeds, UK. Post-session surveys, designed for teachers and pupils respectively, were used to evaluate the session. Children in all year groups had relatively poor knowledge of brain structure and function at the beginning of the session. When asked to label four brain regions with their respective functions, older pupils labeled a mean of 1.5 (± 1.0) brain regions compared to 0.8 (± 0.96) for younger pupils (p=0.002). However, by the end of the session, 95% of pupils felt their knowledge of the brain had increased. Hands-on activities were rated most popular by pupils and were considered the most successful aspect of the session by teachers. Although only half the teachers were aware of neuroscience educational resources, nearly all (95%) felt they would have more confidence in teaching a similar session in the future. All teachers felt the session was engaging and that the content could be linked to the current curriculum. Thus, a short fifty-minute session can successfully enhance pupils’ knowledge of a new topic: the brain. Partnerships with an undergraduate student can provide an alternative method for supplementing teacher knowledge, increasing their confidence in delivering future lessons on the nervous system.

Keywords: education, neuroscience, primary school, undergraduate

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Authors: Loïc Warscotte, Jehan Boreux

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The scientific literature of changepoint detection is vast. Today, a lot of methods are available to detect abrupt changes or slight drift in a signal, based on CUSUM or EWMA charts, for example. However, these methods rely on strong assumptions, such as the stationarity of the stochastic underlying process, or even the independence and Gaussian distributed noise at each time. Recently, the breakthrough research on locally stationary processes widens the class of studied stochastic processes with almost no assumptions on the signals and the nature of the changepoint. Despite the accurate description of the mathematical aspects, this methodology quickly suffers from impractical time and space complexity concerning the signals with high-rate data collection, if the characteristics of the process are completely unknown. In this paper, we then addressed the problem of making this theory usable to our purpose, which is monitoring a high-speed weigh-in-motion system (HS-WIM) towards direct enforcement without supervision. To this end, we first compute bounded approximations of the initial detection theory. Secondly, these approximating bounds are empirically validated by generating many independent long-run stochastic processes. The abrupt changes and the drift are both tested. Finally, this relaxed methodology is tested on real signals coming from a HS-WIM device in Belgium, collected over several months.

Keywords: changepoint, weigh-in-motion, process, non-parametric

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: David Edgar

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To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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Authors: Argyris Karapetsas, Maria Bampou, Andriani Mitropoulou

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The aim of the present work is to study the role of brain function in social awareness processing. Mind controls all the psychosomatic functions. Mind’s functioning enables individual not only to recognize one's own self and propositional attitudes, but also to assign such attitudes to other individuals, and to consider such observed mental states in the elucidation of behavior. Participants and Methods: Twenty (n=20) undergraduate students (mean age 18 years old) were involved in this study. Students participated in a clinical assessment, being conducted in Laboratory of Neuropsychology, at University of Thessaly, in Volos, Greece. Assessment included both electrophysiological (i.e.Event Related Potentials (ERPs) esp.P300 waveform) and neuropsychological tests (Raven's Progressive Matrices (RPM) and Sally-Anne test). Results: Initial assessment’s results confirmed statistically significant differences between the males and females, as well as in score performance to the tests applied. Strong correlations emerged between prefrontal lobe functioning, RPM, Sally-Anne test and P300 latencies. Also, significant dysfunction of mind has been found, regarding its three dimensions (straight, circular and helical). At the end of the assessment, students received consultation and appropriate guidelines in order to improve their intrapersonal and interpersonal skills. Conclusions: Mind and social awareness phenomena play a vital role in human development and may act as determinants of the quality of one’s own life. Meanwhile, brain function is highly correlated with social awareness and it seems that different set of brain structures are involved in social behavior.

Keywords: brain activity, emotions, ERP's, social awareness

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Authors: Ravi Kiran Pothamsetty, Radha Rani Ghosh, Baby Paul Thaliath

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Radiation therapy has undergone many advancements and evloved from 2D to 3D. Recently, with rapid pace of drug discoveries, cutting edge technology, and clinical trials has made innovative advancements in computer technology and treatment planning and upgraded to intensity modulated radiotherapy (IMRT) which delivers in homogenous dose to tumor and normal tissues. The present study was a hospital-based experience comparing two different conformal radiotherapy techniques for brain tumors. This analytical study design has been conducted at Regional Cancer Centre, India from January 2014 to January 2015. Ten patients have been selected after inclusion and exclusion criteria. All the patients were treated on Artiste Siemens Linac Accelerator. The tolerance level for maximum dose was 6.0 Gyfor lenses and 54.0 Gy for brain stem, optic chiasm and optical nerves as per RTOG criteria. Mean and standard deviation values of PTV98%, PTV 95% and PTV 2% in IMRT were 93.16±2.9, 95.01±3.4 and 103.1±1.1 respectively; for 3DCRT were 91.4±4.7, 94.17±2.6 and 102.7±0.39 respectively. PTV max dose (%) in IMRT and 3D-CRT were 104.7±0.96 and 103.9±1.0 respectively. Maximum dose to the tumor can be delivered with IMRT with acceptable toxicity limits. Variables such as expertise, location of tumor, patient condition, and TPS influence the outcome of the treatment.

Keywords: brain tumors, intensity modulated radiotherapy (IMRT), three dimensional conformal radiotherapy (3D-CRT), radiation therapy oncology group (RTOG)

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Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

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We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

Procedia PDF Downloads 249

Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

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This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

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Authors: Metee Pigultong

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The research aims at 1) composition of meditation music, 2) study of the meditation time reliability. The population is the older adults who meditated practitioners in the Thepnimitra Temple, Don Mueang District, Bangkok. The sample group was the older persons who meditated practitioners from the age of 60 with five volunteers. The research methodology was time-series to conduct the research progression. The research instruments included: 1) meditation music, 2) brain wave recording form. The research results found that 1) the music combines the binaural beats suitable for the meditation of the older persons, consisting of the following features: a) The tempo rate of the meditation music is no more than 60 beats per minute. b) The musical instruments for the meditation music arrangement include only 4-5 pieces. c) The meditation music arrangement needs to consider the nature of the right instrument. d) Digital music instruments are suitable for composition. e) The pure-tone sound combined in music must generate a brain frequency at the level of 10 Hz. 2) After the researcher conducted a 3-weeks brain training procedure, the researcher performed three tests for the reliability level using Cronbach's Alpha method. The result showed that the meditation reliability had the level = .475 as a moderate concentration.

Keywords: binaural beats, music therapy, meditation, older person, the Buddhist meditated practitioners

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Authors: R. Gautam, P. Sastha Kanagasabai, G. N. Rathna

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The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Keywords: brain computer interface, Intel Edison, Emotiv EPOC, IoT analytics, electroencephalogram

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Authors: Takuro Kida, Yuichi Kida

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In this paper, it is shown a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detail algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output-signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory, and it is indicated that introducing conversations with feedback do not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: matrix filterbank, optimum signal approximation, category theory, simultaneous minimization

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Authors: Ana Beatriz C. G. Silva, Luiz Carlos Wrobel, Fernando Luiz B. Ribeiro

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This paper presents a thermoregulation model using the finite element method to perform numerical analyses of brain cooling procedures as a contribution to the investigation on the use of therapeutic hypothermia after ischemia in adults. The use of computational methods can aid clinicians to observe body temperature using different cooling methods without the need of invasive techniques, and can thus be a valuable tool to assist clinical trials simulating different cooling options that can be used for treatment. In this work, we developed a FEM package applied to the solution of the continuum bioheat Pennes equation. Blood temperature changes were considered using a blood pool approach and a lumped analysis for intravascular catheter method of blood cooling. Some analyses are performed using a three-dimensional mesh based on a complex geometry obtained from computed tomography medical images, considering a cooling blanket and a intravascular catheter. A comparison is made between the results obtained and the effects of each case in brain temperature reduction in a required time, maintenance of body temperature at moderate hypothermia levels and gradual rewarming.

Keywords: brain cooling, finite element method, hypothermia treatment, thermoregulation

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: Tatiana N. Astakhova, Alexander E. Saprygin, Tatyana A. Golovko, Alexander N. Savostyanov, Mikhail S. Vlasov, Natalia V. Borisova, Alexandera G. Karpova, Urana N. Kavai-ool, Elena D. Mokur-ool, Nikolay A. Kolchanov, Lubomir I. Aftanas

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The aim of the study is to compare behaviorally and EEG reactions in Turkic-speaking inhabitants of Siberia (Tuvinians and Yakuts) and Russians during the recognition of syntax errors in native and foreign languages. 63 healthy aboriginals of the Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and 55 Russians from Novosibirsk participated in the study. All participants completed a linguistic task, in which they had to find a syntax error in the written sentences. Russian participants completed the task in Russian and in English. Tuvinian and Yakut participants completed the task in Russian, English, and Tuvinian or Yakut, respectively. EEG’s were recorded during the solving of tasks. For Russian participants, EEG's were recorded using 128-channels. The electrodes were placed according to the extended International 10-10 system, and the signals were amplified using ‘Neuroscan (USA)’ amplifiers. For Tuvinians and Yakuts EEG's were recorded using 64-channels and amplifiers Brain Products, Germany. In all groups 0.3-100 Hz analog filtering, sampling rate 1000 Hz were used. Response speed and the accuracy of recognition error were used as parameters of behavioral reactions. Event-related potentials (ERP) responses P300 and P600 were used as indicators of brain activity. The accuracy of solving tasks and response speed in Russians were higher for Russian than for English. The P300 amplitudes in Russians were higher for English; the P600 amplitudes in the left temporal cortex were higher for the Russian language. Both Tuvinians and Yakuts have no difference in accuracy of solving tasks in Russian and in their respective national languages (Tuvinian and Yakut). However, the response speed was faster for tasks in Russian than for tasks in their national language. Tuvinians and Yakuts showed bad accuracy in English, but the response speed was higher for English than for Russian and the national languages. With Tuvinians, there were no differences in the P300 and P600 amplitudes and in cortical topology for Russian and Tuvinian, but there was a difference for English. In Yakuts, the P300 and P600 amplitudes and topology of ERP for Russian were the same as Russians had for Russian. In Yakuts, brain reactions during Yakut and English comprehension had no difference and were reflected foreign language comprehension -while the Russian language comprehension was reflected native language comprehension. We found out that the Tuvinians recognized both Russian and Tuvinian as native languages, and English as a foreign language. The Yakuts recognized both English and Yakut as a foreign language, only Russian as a native language. According to the inquirer, both Tuvinians and Yakuts use the national language as a spoken language, whereas they don’t use it for writing. It can well be a reason that Yakuts perceive the Yakut writing language as a foreign language while writing Russian as their native.

Keywords: EEG, language comprehension, native and foreign languages, Siberian inhabitants

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Authors: Katsuyuki Umezawa, Makoto Nakazawa, Manabu Kobayashi, Yutaka Ishii, Michiko Nakano, Shigeichi Hirasawa

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This research aims to develop a self-study system equipped with an artificial teacher who gives advice to students by detecting the learners and to evaluate language learning in a unified framework. 'Detecting the learners' means that the system understands the learners' learning conditions, such as each learner’s degree of understanding, the difference in each learner’s thinking process, the degree of concentration or boredom in learning, and problem solving for each learner, which can be interpreted from learning behavior. In this paper, we propose a system to efficiently collect brain waves from learners by focusing on only the brain waves among the biological information for 'detecting the learners'. The conventional Electroencephalograph (EEG) measurement method during learning using a simple EEG has the following disadvantages. (1) The start and end of EEG measurement must be done manually by the experiment participant or staff. (2) Even when the EEG signal is weak, it may not be noticed, and the data may not be obtained. (3) Since the acquired EEG data is stored in each PC, there is a possibility that the time of data acquisition will be different in each PC. This time, we developed a system to collect brain wave data on the server side. This system overcame the above disadvantages.

Keywords: artificial teacher, e-learning, self-study system, simple EEG

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Mohammad Javad Behzadnia, Amir Bahador Boroumand

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Introduction: Limited facility situations require allocating the most available resources for most casualties. Accordingly, Traumatic Brain Injury (TBI) is the one that may need to transport the patient as soon as possible. In a mass casualty event, deciding when the facilities are restricted is hard. The Extended Glasgow Outcome Score (GOSE) has been introduced to assess the global outcome after brain injuries. Therefore, we aimed to evaluate the prognostic factors associated with GOSE. Materials and Methods: In a multicenter cross-sectional study conducted on 144 patients with TBI admitted to trauma emergency centers. All the patients with isolated TBI who were mentally and physically healthy before the trauma entered the study. The patient’s information was evaluated, including demographic characteristics, duration of hospital stays, mechanical ventilation on admission laboratory measurements, and on-admission vital signs. We recorded the patients’ TBI-related symptoms and brain computed tomography (CT) scan findings. Results: GOSE assessments showed an increasing trend by the comparison of on-discharge (7.47 ± 1.30), within a month (7.51 ± 1.30), and within three months (7.58 ± 1.21) evaluations (P < 0.001). On discharge, GOSE was positively correlated with Glasgow Coma Scale (GCS) (r = 0.729, P < 0.001) and motor GCS (r = 0.812, P < 0.001), and inversely with age (r = −0.261, P = 0.002), hospitalization period (r = −0.678, P < 0.001), pulse rate (r = −0.256, P = 0.002) and white blood cell (WBC). Among imaging signs and trauma-related symptoms in univariate analysis, intracranial hemorrhage (ICH), interventricular hemorrhage (IVH) (P = 0.006), subarachnoid hemorrhage (SAH) (P = 0.06; marginally at P < 0.1), subdural hemorrhage (SDH) (P = 0.032), and epidural hemorrhage (EDH) (P = 0.037) were significantly associated with GOSE at discharge in multivariable analysis. Conclusion: Our study showed some predictive factors that could help to decide which casualty should transport earlier to a trauma center. According to the current study findings, GCS, pulse rate, WBC, and among imaging signs and trauma-related symptoms, ICH, IVH, SAH, SDH, and EDH are significant independent predictors of GOSE at discharge in TBI patients.

Keywords: field, Glasgow outcome score, prediction, traumatic brain injury.

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: Mohammad Khazaei, Reza Rostami, Hassan Gharayagh Zandi, Ruhollah Basatnia, Mahboubeh Ghayour Najafabadi

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Introduction: This study delves into the influence of diverse interventions on brain functional connectivity among young male basketball players. Given the significance of understanding how interventions affect cognitive functions in athletes, particularly in the context of basketball, this research contributes to the growing body of knowledge in sports neuroscience. Methods: Three distinct groups were selected for comprehensive investigation: the Motivational Interview Group, Placebo Consumption Group, and Ritalin Consumption Group. The study involved assessing brain functional connectivity using various frequency bands (Delta, Theta, Alpha, Beta1, Beta2, Gamma, and Total Band) before and after the interventions. The participants were subjected to specific interventions corresponding to their assigned groups. Results: The findings revealed substantial differences in brain functional connectivity across the studied groups. The Motivational Interview Group exhibited optimal outcomes in PLI (Total Band) connectivity. The Placebo Consumption Group demonstrated a marked impact on PLV (Alpha) connectivity, and the Ritalin Consumption Group experienced a considerable enhancement in imCoh (Total Band) connectivity. Discussion: The observed variations in brain functional connectivity underscore the nuanced effects of different interventions on young male basketball players. The enhanced connectivity in specific frequency bands suggests potential cognitive and performance improvements. Notably, the Motivational Interview and Placebo Consumption groups displayed unique patterns, emphasizing the multifaceted nature of interventions. These findings contribute to the understanding of tailored interventions for optimizing cognitive functions in young male basketball players. Conclusion: This study provides valuable insights into the intricate relationship between interventions and brain functional connectivity in young male basketball players. Further research with expanded sample sizes and more sophisticated statistical analyses is recommended to corroborate and expand upon these initial findings. The implications of this study extend to the broader field of sports neuroscience, aiding in the development of targeted interventions for athletes in various disciplines.

Keywords: electroencephalography, Ritalin, Placebo effect, motivational interview

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Authors: K. Padmavathi, K. Sri Ramakrishna

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This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database.

Keywords: bundle block, SC, LMNN classifier, welch method, PSD, MIT-BIH, arrhythmia database

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Authors: Alexandre H. Okano, Pedro M. D. Agrícola, Daniel G. Da S. Machado, Luiz I. Do N. Neto, Luiz F. Farias Junior, Paulo H. D. Nascimento, Rickson C. Mesquita, John F. Araujo, Eduardo B. Fontes, Hassan M. Elsangedy, Shinsuke Shimojo, Li M. Li

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The prefrontal cortex is involved in the reward system and the insular cortex integrates the afferent inputs arriving from the body’ systems and turns into feelings. Therefore, modulating neuronal activity in these regions may change individuals’ perception in a given situation such as exercise. We tested whether transcranial direct current stimulation (tDCS) change cerebral oxygenation and pleasure during exercise. Fourteen volunteer healthy adult men were assessed into five different sessions. First, subjects underwent to a maximum incremental test on a cycle ergometer. Then, subjects were randomly assigned to a transcranial direct current stimulation (2mA for 15 min) intervention in a cross over design in four different conditions: anode and cathode electrodes on T3 and Fp2 targeting the insular cortex, and Fpz and F4 targeting prefrontal cortex, respectively; and their respective sham. These sessions were followed by 30 min of moderate intensity exercise. Brain oxygenation was measured in prefrontal cortex with a near infrared spectroscopy. Perceived exertion and pleasure were also measured during exercise. The asymmetry in prefrontal cortex oxygenation before the stimulation decreased only when it was applied over this region which did not occur after insular cortex or sham stimulation. Furthermore, pleasure was maintained during exercise only after prefrontal cortex stimulation (P > 0.7), while there was a decrease throughout exercise (P < 0.03) during the other conditions. We conclude that tDCS over the prefrontal cortex changes brain oxygenation in ventromedial prefrontal cortex and maintains perceived pleasure during exercise. Therefore, this technique might be used to enhance effective responses related to exercise.

Keywords: affect, brain stimulation, dopamine neuromodulation, pleasure, reward, transcranial direct current stimulation

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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Authors: Djamila Meghraoui, Bachir Boudraa, Thouraya Meksen, M.Boudraa

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Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.

Keywords: Parkinson’s disease recognition, PCA, MDVP, multinomial Naive Bayes

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Authors: Mohammed A. Arishy, Sultan M. Alharbi, Mohammed A. Hakami, Farid M. Abualsail, Mohammad A. Attafi, Riyadh M. Tobaiqi, Hussain M. Alsalem, Ibraheem M. Attafi

Abstract:

Lidocaine is the most common local anesthetics used for para cervical block to reduce pain associated with surgical abortion. A 25-year-old pregnant woman who. She died before reaching hospital, and she was undergoing criminal abortion during the first trimester. In post-mortem investigations and autopsy shows no clear finding; therefore, toxic substances must be suspected and searched for routinely toxicology analysis. In this case report, the postmortem concentration of lidocaine was detected blood, brain, liver, kidney, and stomach. For lidocaine identification and quantification, sample was extracted using solid phase extraction and analyzed by GC-MS (Shimadzu, Japan). Initial screening and confirmatory analysis results showed that only lidocaine was detected in all collected samples, and no other toxic substances or alcohol were detected. The concentrations of lidocaine in samples were 19, 17, 14, 7, and 3 ug/m in the brain, blood, kidney, liver, and stomach, respectively. Lidocaine blood concentration (17 ug/ml) was toxic level and may result in death. Among the tissues, brain showed the highest level of lidocaine, followed by the kidney, liver, and stomach.

Keywords: forensic toxicology, GC-MS, lidocaine, postmortem

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Authors: Periklis Brakatsoulas

Abstract:

Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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