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Gotowe bibliografie tematyczne / Brain signal acquisition

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Gotowa bibliografia na temat „Brain signal acquisition”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "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 (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 (

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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 acquisitio

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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 (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

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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 (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 sam

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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 (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 uni

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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 micr

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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 (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 o

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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 (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 (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 ex

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Vajravelu, Ashok, Muhammad Mahadi Bin Abdul Jamil, Mohd Helmy Bin Abd Wahab, et al. "Nanocomposite-Based Electrode Structures for EEG Signal Acquisition." Crystals 12, no. 11 (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 provid

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