Relevant bibliographies by topics / Brain signal acquisition

Academic literature on the topic 'Brain signal acquisition'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Brain signal acquisition"

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Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
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Wang, Jiu Hui, and Qiang Ji. "Research on Signal Acquisition Based on Wireless Sensor for Foot Compressive Characteristics on Basketball Movement." Applied Mechanics and Materials 483 (December 2013): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amm.483.401.

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The signal acquisition system (SAS) operated by battery is designed in this paper. SAS includes signal acquisition and statistics function based on movement joints of basketball player. SAS is a recording of the electrical activity of the brain and pulse from the scalp and the recorded waveforms provide insights into the dynamic aspects of brain activity. The amplified SAS signals are digitized by an A/D converter. The digitized signal is transmitted to PC by a wireless serial port or stored in secure digital memory card. Experimental result shows that the system could implement the acquisition and storage of the foot compressive mechanical characteristics signals efficiently. This system would be of benefit to all involved in the use of SAS for sports training.
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Yuan, Lixue, Yinyan Fan, Quanxi Gan, and Huibin Feng. "Clinical Diagnosis of Psychiatry Based on Electroencephalography." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 955–63. http://dx.doi.org/10.1166/jmihi.2021.3338.

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At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.
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Edison, Rizki Edmi, Rohmadi Rohmadi, Sra Harke Pratama, Muhammad Fathul Ihsan, Almusfi Saputra, and Warsito Purwo Taruno. "Design of Brain Activity Measurement for Brain ECVT Data Acquisition System." International Journal of Innovative Research in Medical Science 6, no. 10 (October 1, 2021): 630–34. http://dx.doi.org/10.23958/ijirms/vol06-i10/1223.

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Brain Electrical Capacitance Volume Tomography (ECVT) has been developing as an alternative non-invasive brain imaging method. In this study, brain ECVT consisting of two channels, namely a capacitance sensor, is investigated. As a comparison, EEG sensor is used to measure brain activity simultaneously with the brain ECVT. Brain activity measurements were carried out at the pre-frontal lobe of Fp1 and Fp2 locations. The resulting signal was processed by filtering method and Power Spectral Density (PSD). The result of signal analysis shows that the measurement between EEG and ECVT shows the same activity of the two modalities.
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Wang, Shinmin, Ovid J. L. Tzeng, and Richard N. Aslin. "Predictive brain signals mediate association between shared reading and expressive vocabulary in infants." PLOS ONE 17, no. 8 (August 3, 2022): e0272438. http://dx.doi.org/10.1371/journal.pone.0272438.

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The ability to predict upcoming information is crucial for efficient language processing and enables more rapid language learning. The present study explored how shared reading experience influenced predictive brain signals and expressive vocabulary of 12-month-old infants. The predictive brain signals were measured by fNIRS responses in the occipital lobe with an unexpected visual-omission task. The amount of shared reading experience was correlated with the strength of this predictive brain signal and with infants’ expressive vocabulary. Importantly, the predictive brain signal explained unique variance of expressive vocabulary beyond shared reading experience and maternal education. A further mediation analysis showed that the effect of shared reading experience on expressive vocabulary was explained by the infants’ predictive brain signal. This is the first evidence indicating that richer shared reading experience strengthens predictive signals in the infant brain and in turn facilitates expressive vocabulary acquisition.
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Lin, Jzau Sgeng, and Sun Ming Huang. "An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs." Applied Mechanics and Materials 284-287 (January 2013): 1616–21. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1616.

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A wireless EEG-based brain-computer interface (BCI) and an FPGA-based system to control electric wheelchairs through a Bluetooth interface was proposed in this paper for paralyzed patients. Paralytic patients can not move freely and only use wheelchairs in their daily life. Especially, people getting motor neuron disease (MND) can only use their eyes and brain to exercise their willpower. Therefore, real-time EEG and winking signals can help these patients effectively. However, current BCI systems are usually complex and have to send the brain waves to a personal computer or a single-chip microcontroller to process the EEG signals. In this paper, a simple BCI system with two channels and an FPGA-based circuit for controlling DC motor can help paralytic patients easily to drive the electric wheelchair. The proposed BCI system consists of a wireless physiological with two-channel acquisition module and an FPGA-based signal processing unit. Here, the physiological signal acquisition module and signal processing unit were designed for extracting EEG and winking signals from brain waves which can directly transformed into control signals to drive the electric wheelchairs. The advantages of the proposed BCI system are low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed feasible action for the proposed BCI system and drive circuit with a practical operating in electric wheelchair applications.
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Ranjandish, Reza, and Alexandre Schmid. "A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control." Sensors 20, no. 19 (October 8, 2020): 5716. http://dx.doi.org/10.3390/s20195716.

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Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks of the signal acquisition chain are presented with a focus on the circuit architecture and a careful attention to solutions to issues related to data acquisition from multi-channel arrays of cortical sensors.
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Perman, William H., Mokhtar H. Gado, Kenneth B. Larson, and Joel S. Perlmutter. "Simultaneous MR Acquisition of Arterial and Brain Signal-Time Curves." Magnetic Resonance in Medicine 28, no. 1 (November 1992): 74–83. http://dx.doi.org/10.1002/mrm.1910280108.

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Chenane, Kathia, Youcef Touati, Larbi Boubchir, and Boubaker Daachi. "Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications." Computers 8, no. 4 (December 4, 2019): 87. http://dx.doi.org/10.3390/computers8040087.

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The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain–computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user’s thoughts in order to translate them into commands, such as controlling wheelchairs, cursor movement, or spelling. The proposed method follows a functional model, as is the case for any BCI, and can be achieved through three main phases: data acquisition and preprocessing, feature extraction, and classification of brains activities. For this purpose, we propose an interpretation model implementing a quantization method using both fast Fourier transform with root mean square error for feature extraction and a self-organizing-map-based neural network to generate classifiers, allowing better interpretation of brain activities. In order to show the effectiveness of the proposed methodology, an experimental study was conducted by exploiting five mental activities acquired by a G.tec BCI system containing 16 simultaneously sampled bio-signal channels with 24 bits, with experiments performed on 10 randomly chosen subjects.
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Vajravelu, Ashok, Muhammad Mahadi Bin Abdul Jamil, Mohd Helmy Bin Abd Wahab, Wan Suhaimizan Bin Wan Zaki, Vibin Mammen Vinod, Karthik Ramasamy Palanisamy, and Gousineyah Nageswara Rao. "Nanocomposite-Based Electrode Structures for EEG Signal Acquisition." Crystals 12, no. 11 (October 27, 2022): 1526. http://dx.doi.org/10.3390/cryst12111526.

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Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.
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Dissertations / Theses on the topic "Brain signal acquisition"

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Dvořák, Jiří. "Biofeedback a jeho použití." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217977.

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The aim of this work is describe common methods of biological feedback therapy that is used to treat some psychosomatic diseases. Subsequently, the description is focused on minimal brain dysfunction treatment by the help of EEG biofeedback. Properties and technical requirements for this therapy are concretized. The last part of this thesis is dedicated to the design and realization of practical software tool for EEG biofeedback therapy which is made in LabView 7.1. The M535 acquisition unit and NI USB-6221 measuring device are used for hardware solution.
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CHEN, ZHI-PING, and 陳治平. "Data acquisition system for extracellular neuronal signals of brain slice under effect of voltammetric signal." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/11452405166578878061.

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Gau, Shir-Cheng, and 高士政. "Development of Dual-Core-Processor based Real-Time Wireless Embedded Brain Signal Acquisition / Processing System and its Application on Driver's Drowsiness Estimation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/77390002178270000538.

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碩士
國立交通大學
電機與控制工程系所
93
In this thesis, a portable Real-Time Wireless Embedded Brain Signal Acquisition / Processing System is developed. It combines electroencephalogram signal amplifier technique, wireless transimission technique, and embedded real-time system. This system is convenient for people used in daily life. The developed strategy contain three parts: First, the bluetooth protocol is used as a transmission interface and integrated with the bio-signal amplifier to transmit the measured physiological signals wirelessly. Then, the OMAP is used as a development platform and an embedded operating system for OMAP is also designed. Finally, DSP Gateway is developed as the mechanism in the embedded system to deal with the brain- signal analyzing tasks shared by ARM and DSP. An driver’s cognitive-state estimation has been developed and implementation on the proposed dual-core-processor based real time wireless embedded system for demonstration.
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Hsieh, Chang-Wei, and 謝長倭. "A Combined Data Acquisition and Compression Method for Neurotransmission Signals in Brain Slice." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/09083784890826997413.

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碩士
國立成功大學
電機工程研究所
84
In the past, it is difficult to observe and study the compound changes of neuronal electric activity and electrochemical variation representing the neurotransmitter efflux simultaneously. The reason is that there is a big difference between on the output formats and frequencies of the recorded signals, and the measuring instruments used. In addition, the two kinds of signals will interfere each other. So that these signals are difficult to detect and record, simultaneously. Moreover, the high sampling frequency for electrophysiological signal will result in the storage problem of a large amount of experimental data for long-term recording. This made the research field and the corresponding instrumentation difficult to break through. The study of a combined data acquisition and compression method for neurotransmission signals in brain slice is tried to solve the problems mentioned above, and divided into three parts: 1. Traditional combined data acquisition architecture for neurotransmission signals in brain slice. 2. Real time data compression method. 3. Combined data acquisition architecture with Virtual Instrument for neurotransmission signals in brain slice. With the study in the paper, it not only made a user friendly environment for the experimentalist of neurotransmission signals in brain slice, but also usefully for the measuring the other physiological signals.
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Books on the topic "Brain signal acquisition"

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Language, cognition, and the brain: Insights from sign language research. Mahwah, N.J: Lawrence Erlbaum Associates, 2002.

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Ramani, Ramachandran, ed. Functional MRI. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.001.0001.

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Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.
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Emmorey, Karen. Language, Cognition, and the Brain: Insights from Sign Language Research. Taylor & Francis Group, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights from Sign Language Research. Taylor & Francis Group, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights from Sign Language Research. Taylor & Francis Group, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights From Sign Language Research. Lawrence Erlbaum, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights from Sign Language Research. Taylor & Francis Group, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights from Sign Language Research. Taylor & Francis Group, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights from Sign Language Research. Taylor & Francis Group, 2001.

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Emmorey, Karen. Language, Cognition, and the Brain: Insights From Sign Language Research. Lawrence Erlbaum, 2001.

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Book chapters on the topic "Brain signal acquisition"

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Maurer, Konrad, and Thomas Dierks. "Data Acquisition and Signal Analysis." In Atlas of Brain Mapping, 23–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76043-3_5.

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Bazán, Paulo Rodrigo, and Edson Amaro. "fMRI and fNIRS Methods for Social Brain Studies: Hyperscanning Possibilities." In Social and Affective Neuroscience of Everyday Human Interaction, 231–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08651-9_14.

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AbstractRecently, the “social brain” (i.e., how the brain works in social context and the mechanisms for our social behavior) has gained focus in neuroscience literature – largely due to the fact that recently developed techniques allow studying different aspects of human social cognition and its brain correlates. In this context, hyperscanning techniques (Montague et al., Neuroimage 16(4):1159–1164, 2002) open the horizon for human interaction studies, allowing for the evaluation of interbrain connectivity. These techniques represent methods for simultaneously recording signals from different brains when subjects are interacting. In this chapter, we will explore the potentials of functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), which are techniques based on blood-oxygen-level-dependent (BOLD) signal. We will start with a brief explanation of the BOLD response basic principles and the mechanisms involved in fMRI and fNIRS measurements related to brain function. We will then discuss the foundation of the social brain, based on the first studies, with one subject per data acquisition, to allow for understanding the new possibilities that hyperscanning techniques offer. Finally, we will focus on the scientific literature reporting fMRI and fNIRShyperscanning contribution to understand the social brain.
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Grundy, John G., and Ashley Chung-Fat-Yim. "Chapter 12. Domain-general electrophysiological changes associated with bilingualism." In Studies in Bilingualism, 245–71. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/sibil.64.12gru.

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Electroencephalogram (EEG) has been instrumental in research examining the effects of bilingualism on cognition, and much of this research has been inspired and spearheaded by Ellen Bialystok. In this chapter, we explore recent developments in EEG research that highlight the complexity of the bilingual experience and its impact on domain-general cognitive outcomes. First, even in the absence of behavioral differences, ERP studies consistently show that bilinguals are more efficient at processing information than monolinguals on executive function tasks. Second, resting-state EEG studies demonstrate a link between several brain frequency bands (e.g. beta waves) and learning outcomes during second-language acquisition. Third, recent advances in EEG techniques have demonstrated that brain signal complexity is more than just noise, and that greater complexity is associated with better performance. Preliminary evidence suggests that bilingualism modifies brain signal complexity in regions associated with automatic processing. Finally, there is a recent shift to focus more on attentional than inhibitory mechanisms, and on continuous rather than dichotomous classifications of bilingualism, in assessing how bilingualism affects executive function processing in the brain. The final section concludes with several recommendations and future directions for EEG studies to investigate how language experience impacts the neural correlates of attentional control.
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Kegl, Judy A. "Language emergence in a language-ready brain." In Directions in Sign Language Acquisition, 207–54. Amsterdam: John Benjamins Publishing Company, 2002. http://dx.doi.org/10.1075/tilar.2.12keg.

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Paszkiel, Szczepan. "Data Acquisition Methods for Human Brain Activity." In Analysis and Classification of EEG Signals for Brain–Computer Interfaces, 3–9. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30581-9_2.

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Galíndez-Floréz, Iván, Andrés Coral-Flores, Edna Moncayo-Torres, Dagoberto Mayorca-Torres, and Herman Guerrero-Chapal. "Biopotential Signals Acquisition from the Brain Through the MindWave Device: Preliminary Results." In Communications in Computer and Information Science, 139–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42517-3_11.

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Holzer, Peter. "Interoception and Gut Feelings: Unconscious Body Signals’ Impact on Brain Function, Behavior and Belief Processes." In Processes of Believing: The Acquisition, Maintenance, and Change in Creditions, 435–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50924-2_31.

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"Brain Signal Acquisition." In Deep Learning for EEG-Based Brain–Computer Interfaces, 9–26. WORLD SCIENTIFIC (EUROPE), 2021. http://dx.doi.org/10.1142/9781786349590_0002.

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Ai, Qingsong, Quan Liu, Wei Meng, and Sheng Quan Xie. "Brain Signal Acquisition and Preprocessing." In Advanced Rehabilitative Technology, 105–33. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-814597-5.00005-9.

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S., Vidhya, and Sharmila Nageswaran. "Medical Signal Processing." In Advances in Medical Technologies and Clinical Practice, 81–103. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8018-9.ch006.

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This chapter introduces sleep, the pattern of sleep, wakefulness, disorders associated with sleep, diseases of heart and lungs that can be identified by analysing one's sleep. Sleep is generally equated to the neurological system and the brain. It is believed that sleep can be identified only with EEG. This chapter also explores the usage of EEG in detecting the disorders associated with sleep, and more emphasis is given to the bio signals other than EEG, which includes ECG, PPG, acoustic signals that can be used in understanding the sleep and its related disorders. It explains the biomedical devices that are used for sleep-related studies. This chapter explores the stages of sleep signal processing where the authors have suggested how to reduce noises at the stage of data acquisition. Further topics explore various signal processing methods that need to be adapted in various stages, namely preprocessing, filtering, feature extraction, validation, and automated processing.
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Conference papers on the topic "Brain signal acquisition"

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Khan, M. Jawad, and Keum-Shik Hong. "Active brain area identification using EEG-NIRS signal acquisition." In 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT). IEEE, 2015. http://dx.doi.org/10.1109/icacomit.2015.7440145.

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Lee, Shuenn-Yuh, Jia-Hua Hong, and Liang-Hung Wang. "Wireless brain signal acquisition circuits for body sensor network." In 2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2012. http://dx.doi.org/10.1109/icci-cc.2012.6311129.

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Shi, Zhongyan, Xingyu Han, Bo Jiang, Jiangtao Zhang, Dingjie Suo, Guangying Pei, Tianyi Yan, Ye Wang, Jinglong Wu, and Jing Wang. "Wearable Multimodule Bio-signal Acquisition System: Brain Multi-Plus." In 2022 16th ICME International Conference on Complex Medical Engineering (CME). IEEE, 2022. http://dx.doi.org/10.1109/cme55444.2022.10063300.

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Hassani, Kaveh, and Won-Sook Lee. "An experimental study on semi-invasive acupuncture-based EEG signal acquisition." In 2015 3rd International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2015. http://dx.doi.org/10.1109/iww-bci.2015.7073048.

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Losonczi, Lajos, Laszlo F. Marton, Tihamer S. Brassai, Laszlo Bako, Lorand Farkas, and Lorand Farkas. "A novel bio-signal acquisition system for brain computer interfaces." In 2013 4th International Symposium on Electrical and Electronics Engineering (ISEEE). IEEE, 2013. http://dx.doi.org/10.1109/iseee.2013.6674347.

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Yong, Phoo Khai, and Eric Tatt Wei Ho. "Streaming brain and physiological signal acquisition system for IoT neuroscience application." In 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2016. http://dx.doi.org/10.1109/iecbes.2016.7843551.

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Logeswari, T., and M. Karnan. "Hybrid Self Organizing Map for Improved Implementation of Brain MRI Segmentation." In 2010 International Conference on Signal Acquisition and Processing (ICSAP). IEEE, 2010. http://dx.doi.org/10.1109/icsap.2010.56.

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Logeswari, T., and M. Karnan. "An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing." In 2010 International Conference on Signal Acquisition and Processing (ICSAP). IEEE, 2010. http://dx.doi.org/10.1109/icsap.2010.55.

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Kim, Seho, Jong Min Lim, Seokchan Yoon, Youngjin Choi, Jin Hee Hong, Wonshik Choi, and Minhaeng Cho. "The Effect of Aberration Correction on Coherent Raman Imaging of Mouse Brain Tissues." In 3D Image Acquisition and Display: Technology, Perception and Applications. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/3d.2022.jth2a.6.

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We observed that coherent Raman imaging signal enhancement by the aberration correction is approximately proportional to the Strehl ratio enhancement, not its third power. The Raman signal originating from out-of-focus planes explains this discrepancy.
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Jeyabalan, Vickneswaran, Andrews Samraj, and Loo Chu Kiong. "Classification of Motor Imaginary Signals for Machine Commmunication - A Novel Approach for Brain Machine Interface Design." In 2009 International Conference on Signal Acquisition and Processing, ICSAP. IEEE, 2009. http://dx.doi.org/10.1109/icsap.2009.29.

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