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Artykuły w czasopismach na temat "Noninvasive Scalp Electroencephalogram"

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Kang, Byeong-Cheol, and Tae-Jun Ha. "Noninvasive electroencephalogram sensors based on all-solution-processed trapezoidal electrode array." Applied Physics Letters 120, no. 21 (2022): 213301. http://dx.doi.org/10.1063/5.0087848.

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Conventional wet electrodes, such as a silver/silver chloride electrode, are limited for electroencephalogram (EEG) sensors directly attached to the scalp with existing hair due to their incomplete contact and increased impedance. In this study, an all-solution-processed trapezoidal electrode array is demonstrated for highly sensitive and reliable detection of EEG signals even when in direct contact with the scalp. The proposed noninvasive EEG sensors based on nanocomposites consisting of single-wall carbon nanotube random networks incorporated into a gelatin matrix exhibited a relatively low
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Liu, Junchen, Sen Lin, Wenzheng Li, et al. "Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes." Research 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.34133/2022/9830457.

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Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be
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Zhang, Tao, Jichi Chen, Enqiu He, and Hong Wang. "Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram." Applied Sciences 11, no. 21 (2021): 10279. http://dx.doi.org/10.3390/app112110279.

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Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity of the EEG signal is then quantified with the sample entropy method. Finally, we explore the performance of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals. Experimental results show that t
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Minnerly, Christopher, Steven L. Bressler, Ibrahim M. Shokry, and Rui Tao. "Estimating Mental Health Conditions of Patients with Opioid Use Disorder." Journal of Addiction 2019 (September 26, 2019): 1–15. http://dx.doi.org/10.1155/2019/8586153.

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Objectives. Noninvasive estimation of cortical activity aberrance may be a challenge but gives valuable clues of mental health in patients. The goal of the present study was to characterize specificity of electroencephalogram (EEG) electrodes used to assess spectral powers associated with mental health conditions of patients with opioid use disorder. Methods. This retrospective study included 16 patients who had been diagnosed with opioid use disorder in comparison with 16 sex- and age-matched healthy controls. EEG electrodes were placed in the frontal (FP1, FP2, F3, F4, F7, F8, and Fz), centr
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Rashid, Nasir, Javaid Iqbal, Amna Javed, Mohsin I. Tiwana, and Umar Shahbaz Khan. "Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis." BioMed Research International 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2695106.

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Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stag
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Li, Shujing, Zeng He, Yang Li, et al. "Association of Lateralized Interictal Epileptiform Discharges with Postsurgical Seizure Outcomes in Patients with Tuberous Sclerosis Complex: A 10-Year Retrospective Study at a Single Center." Acta Neurologica Scandinavica 2023 (May 25, 2023): 1–9. http://dx.doi.org/10.1155/2023/3756290.

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Objective. Tuberous sclerosis complex (TSC) is a rare neurodevelopmental disorder often associated with intractable epilepsy, and surgical resection is the effective therapeutic approach to alleviate seizures in TSC patients. Scalp electroencephalogram (EEG) is a noninvasive method used to diagnose epilepsy; however, the relationship between scalp video-EEG findings and postsurgical seizure outcomes has not been fully evaluated in TSC patients. Materials and Methods. The relationship between clinical characteristics and seizure outcomes of TSC patients with 1-year, 5-year, and 10-year follow-u
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Telenczuk, Bartosz, Stuart N. Baker, Andreas V. M. Herz, and Gabriel Curio. "High-frequency EEG covaries with spike burst patterns detected in cortical neurons." Journal of Neurophysiology 105, no. 6 (2011): 2951–59. http://dx.doi.org/10.1152/jn.00327.2010.

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Invasive microelectrode recordings measure neuronal spikes, which are commonly considered inaccessible through standard surface electroencephalogram (EEG). Yet high-frequency EEG potentials (hf-EEG, f > 400 Hz) found in somatosensory evoked potentials of primates may reflect the mean population spike responses of coactivated cortical neurons. Since cortical responses to electrical nerve stimulation vary strongly from trial to trial, we investigated whether the hf-EEG signal can also echo single-trial variability observed at the single-unit level. We recorded extracellular single-unit activi
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Itoh, Kosuke, Naho Konoike, Haruhiko Iwaoki, Hironaka Igarashi, and Katsuki Nakamura. "A novel “dip-in electrode” method for electrode application to record noninvasive scalp electroencephalograms and evoked potentials in an awake common marmoset." Neuroimage: Reports 2, no. 3 (2022): 100116. http://dx.doi.org/10.1016/j.ynirp.2022.100116.

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Virdi, Kulsheet Kaur, and Satish Pawar. "A Comprehensive Review on Brain-Computer Interface Controlled Movements." SMART MOVES JOURNAL IJOSCIENCE 5, no. 6 (2019): 3. http://dx.doi.org/10.24113/ijoscience.v5i6.243.

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A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface t
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MARTIS, ROSHAN JOY, JEN HONG TAN, CHUA KUANG CHUA, TOO CHEAH LOON, SHARON WAN JIE YEO, and LOUIS TONG. "EPILEPTIC EEG CLASSIFICATION USING NONLINEAR PARAMETERS ON DIFFERENT FREQUENCY BANDS." Journal of Mechanics in Medicine and Biology 15, no. 03 (2015): 1550040. http://dx.doi.org/10.1142/s0219519415500402.

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Epilepsy is a chronic neurological disorder with considerable incidence and affects the population everywhere in the world. It occurs due to recurrent unprovoked seizures which can be noninvasively diagnosed using electroencephalograms (EEGs) which are the neuronal electrical activity recorded on the scalp. The EEG signal is highly random, nonlinear, nonstationary and non-Gaussian in nature. The nonlinear features characterize the EEG more accurately than linear models. EEG comprsises of different activities like delta, theta, lower alpha, upper alpha, lower beta, upper beta and lower gamma wh
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Streszczenia konferencji na temat "Noninvasive Scalp Electroencephalogram"

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Baxter, Bryan S., Andrew Decker, and Bin He. "Noninvasive control of a robotic arm in multiple dimensions using scalp electroencephalogram." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6695867.

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