Electrophysiological correlates of perceptual prediction error are attenuated in dyslexia by Beach, Sara Dawley, Lim, Sung-Joo, Cardenas-Iniguez, Carlos, Eddy, Marianna D., Gabrieli, John D. E., Perrachione, Tyler Kent

“…Here we used electroencephalography (EEG) to interrogate the feedforward and feedback contributions to neural adaptation as adults with and without dyslexia viewed pairs of faces and words in a paradigm that manipulated whether there was a high probability of stimulus repetition versus a high probability of stimulus change. …”
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Robots as Minions, Sidekicks, and Apprentices: Using Wearable Muscle, Brain, and Motion Sensors for Plug-and-Play Human-Robot Interaction by DelPreto, Joseph

“…Classifiers also detect error judgments in brain signals via electroencephalography (EEG). Continuous motions are detected in two ways. …”
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Analyzing autonomic activity in neonatal seizures by Ramaswamy, Priya, M. Eng. Massachusetts Institute of Technology

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Speaker gaze increases information coupling between infant and adult brains by Leong, Victoria, Byrne, Elizabeth, Clackson, Kaili, Georgieva, Stanimira, Lam, Sarah, Wass, Sam

“…Here, we used dual-electroencephalography to assess whether direct gaze increases neural coupling between adults and infants during screen-based and live interactions. …”
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Construction of air traffic controller’s decision network using error-related potential by Goh, Sim Kuan, Tran, Ngoc Phu, Pham, Duc-Thinh, Alam, Sameer, Izzetoglu, Kurtulus, Duong, Vu

“…Electroencephalography based brain computer interface has enabled communication of human’s intention to a computer directly from the brain by decoding signatures that relay the intention information. …”
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EEG-based cross-subject mental fatigue recognition by Liu, Yisi, Lan, Zirui, Cui, Jian, Sourina, Olga, Müller-Wittig, Wolfgang

“…By directly measuring the neurophysiological activities happening in the brain, electroencephalography (EEG) signal can be used as a good indicator of mental fatigue. …”
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Parallel spatial-temporal self-attention CNN-based motor imagery classification for BCI by Liu, Xiuling, Shen, Yonglong, Liu, Jing, Yang, Jianli, Xiong, Peng, Lin, Feng

“…Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. …”
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A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG by Cui, Jian, Lan, Zirui, Liu, Yisi, Li, Ruilin, Li, Fan, Sourina, Olga, Müller-Wittig, Wolfgang

“…Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. …”
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Towards ARTEM-IS: design guidelines for evidence-based EEG methodology reporting tools by Styles, Suzy J., Ković, Vanja, Ke, Han, Šoškić, Anđela

“…As a first step in tool development, we present the ARTEM-IS Statement describing what action will be needed to create an Agreed Reporting Template for Electroencephalography Methodology - International Standard (ARTEM-IS), along with ARTEM-IS Design Guidelines for developing tools that use an evidence-based approach to error reduction. …”
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MIN2Net: end-to-end multi-task learning for subject-independent motor imagery EEG classification by Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit

“…Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. …”
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Tensor-CSPNet: a novel geometric deep learning framework for motor imagery classification by Ju, Ce, Guan, Cuntai

“…Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. …”
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Improving SSVEP-BCI performance through repetitive anodal tDCS-based neuromodulation: insights from fractal EEG and brain functional connectivity by Zhang, Shangen, Cui, Hongyan, Li, Yong, Chen, Xiaogang, Gao, Xiaorong, Guan, Cuntai

“…This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. …”
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Decoding subjective intensity of nociceptive pain from pre-stimulus and post-stimulus brain activities by Tu, Yiheng, Tan, Ao, Bai, Yanru, Hung, Yeung Sam, Zhang, Zhiguo

“…Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. …”
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Perceptual decision-making under uncertainty in humans by Ruesseler, M

“…This thesis examines how humans form decisions based on noisy information using electroencephalography (EEG), and how they make use of prior expectation in order to guide their decisions using magnetoencephalography (MEG). …”

How is information in working memory selected and prioritised for action? by Printzlau, FAB

“…</p> <p><p>Chapter 2-3 used behaviour, eye-tracking and electroencephalography (EEG) to investigate the consequences of selecting a single feature in WM for the representation of other features belonging to the same item. …”

Study protocol for “psilocybin as a treatment for anorexia nervosa: a pilot study” by Spriggs, MJ, Douglass, HM, Park, RJ, Read, T, Danby, JL, de Magalhães, FJC, Alderton, KL, Williams, TM, Blemings, A, Lafrance, A, Nicholls, DE, Erritzoe, D, Nutt, DJ, Carhart-Harris, RL

“…Neurophysiological outcome measures will be: (1) functional magnetic resonance imaging (fMRI) brain changes from baseline to 6-week endpoint and (2) post-acute changes in electroencephalography (EEG) activity, including an electrophysiological marker of neuronal plasticity.…”

Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa by Kariuki, S, Matuja, W, Akpalu, A, Kakooza-Mwesige, A, Chabi, M, Wagner, R, Connor, M, Chengo, E, Ngugi, A, Odhiambo, R, Bottomley, C, White, S, Sander, J, Neville, B, Newton, C

“…Methods We performed a detailed clinical and neurophysiologic description of ACE cases identified from a community survey of 584,586 people using medical history, neurologic examination, and electroencephalography (EEG) data from five sites in Africa: South Africa; Tanzania; Uganda; Kenya; and Ghana. …”

Time-varying source reconstruction by Timms, R

“…One way of imaging neural activity is through electrophysiological techniques such as magnetoencephalography and electroencephalography (M/EEG). However, for these data to be truly useful, the ill-posed inverse problem must first be tackled.…”

Whole-body hypothermia in mild neonatal encephalopathy: protocol for a multicentre phase III randomised controlled trial by Garegrat, R, Montaldo, P, Burgod, C, Pant, S, Mazlan, M, Palanisami, B, Chakkarapani, E, Woolfall, K, Johnson, S, Grant, PE, Land, S, Mahmoud, M, Brady, T, Cornelius, V, Adams, E, Dorling, J, Aladangadi, N, Fleming, P, Pressler, R, Shennan, A, Petrou, S, Soe, A, Basset, P, Shankaran, S

“…All neonates will have continuous amplitude integrated electroencephalography (aEEG) at least for the first 48 h to monitor for seizures. …”

The effect of EEG on assessment of cerebral autoregulation by Narinahally Krishnamurthy, SK

“…Following the impairment of autoregulation, such as under intensive care conditions, previous studies have found that Electroencephalography (EEG) signals also change in their frequency content in a manner that corresponds to decreases in CBF. …”