ライフサイエンスコーパス: EEG

1 EEG and EMG recordings revealed that ppDIO increases sle

2 EEG findings were used for the creation of a multistate

3 EEG monitoring continued for another month after cell ab

4 EEG risk factors were selected a priori.

5 EEG risk state is defined by emergence of epileptiform p

6 EEG sleep spindle correlates of intelligence, however, e

7 EEG was measured from 128 electrodes arranged over occip

8 EEG was recorded over leg and hand area of motor cortex.

9 EEG was recorded while male and female human subjects wa

10 EEG, EMG, blood pressure and WBP signals were simultaneo

11 EEG, EMG, body temperature (Tb), and locomotor activity

12 EEG, EMG, body temperature, and locomotor activity data

13 EEG, EOG, ECG and NIRS signals have been measured during

14 In 57 patients an additional EEG was recorded 14 +/- 1 days following onset of antide

15 htward-shifting prisms to selectively affect EEG signatures of motor but not attentional processes.

16 restimulus parietal, but no occipital, alpha EEG phase and power, as well as poststimulus alpha phase

17 An EEG-based speech envelope reconstruction method was appl

18 well characterized in 29 patients; 26 had an EEG-fMRI GM localization that was correct in 11, 22 pati

19 cts (n = 18), in behavioural tasks and in an EEG observation-execution task measuring mu suppression.

20 The frequency-following response (FFR) is an EEG signal that is used to explore how the auditory syst

21 We analysed EEG activities evoked during an expression matching task

22 Our data suggests that mouse behavior and EEG recordings are not sensitive to decreased Chrna7 cop

23 uM(vglut2)) produce sustained behavioral and EEG arousal when chemogenetically activated.

24 Converging behavioral and EEG results have shown that binocular rivalry and attent

25 h-frequency tones, using both behavioral and EEG techniques.

26 In the current behavioural and EEG study, we focused on the lateral prefrontal cortex i

27 ed cortical activity can explain the MEG and EEG patterns generated by deep sources.

28 s well as generalized and focal seizures and EEG abnormalities for patients with gain- and loss-of-fu

29 relates of these behavioural transitions and EEG signatures for monitoring the levels of consciousnes

30 ovaried positively with occipital gamma-band EEG, consistent with activation of cortical regions repr

31 nts after cardiac arrest and enables bedside EEG interpretation of unexperienced readers.

32 s on the Ube3a maternal deletion behavioral, EEG, and seizure threshold phenotypes.

33 romosome 3q26 were associated with fast beta EEG power at P<5 x 10(-8).

34 tion study (GWAS) on resting-state fast beta EEG power.

35 Interictal discharges were mapped using both EEG-fMRI hemodynamic responses and ESI.

36 gly correlated with a reduction in broadband EEG power after stimulus presentation.

37 We introduce a custom-built EEG toolbox to track data preprocessing with open-access

38 were not caused by seizures as indicated by EEG recordings.

39 k performance was more strongly predicted by EEG flicker responses during stimulus processing than du

40 llowing resection was correctly predicted by EEG-fMRI GM in 8 of 20 patients, and by the ESI maximum

41 s found that brain abnormalities revealed by EEG are a potential causal factor in childhood behaviora

42 database was developed by the Critical Care EEG Research Consortium and used data collected over 3 y

43 while simultaneously recording multi-channel EEG signals.

44 abnormalities compared to standard clinical EEG.

45 correct in 17, and 12 patients had combined EEG-fMRI and ESI that was correct in 11.

46 The combined EEG-fMRI/ESI region entirely predicted outcome in 9 of 9

47 Together, these combined EEG/fMRI results illuminate the dynamically interacting

48 To achieve this, we combined EEG methods that preferentially tag different levels in

49 calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessm

50 n participants (both sexes) using concurrent EEG-fMRI and a sustained selective listening task, in wh

51 We used near-threshold TMS with concurrent EEG recordings to measure how oscillatory brain dynamics

52 Continuous EEG monitoring was begun 2-3 months after pilocarpine tr

53 electrographic variables on 5427 continuous EEG sessions from eligible patients if they had continuo

54 rom eligible patients if they had continuous EEG for clinical indications, excluding epilepsy monitor

55 n acutely ill patients undergoing continuous EEG.

56 There were 5427 continuous EEGs performed on 4772 participants (2868 men, 49.9%; me

57 y of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients wh

58 ta modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain t

59 analytical approach to show that crossmodal EEG mu rhythm responses primarily index the somatosensor

60 In the current EEG study we investigated whether the brain's response t

61 stimuli, we repeatedly recorded high-density EEG after normal sleep and after sleep deprivation while

62 g graph theoretical analysis of high-density EEG during patient-titrated propofol-induced sedation.

63 Here, using high-density EEG recordings, we show that 4- to 6-month-old infants d

64 We recorded high-density EEG while participants performed an audiovisual simultan

65 g adult human participants with high-density EEG, we show that, already before the presentation of a

66 equency, and temporal aspects of mid-density EEG.

67 Here, we compared "super-Nyquist" density EEG ("SND") with Nyquist density ("ND") arrays for asses

68 vasive measurements of intracortical (depth) EEG (dEEG), partial pressure of oxygen in interstitial b

69 resonance imaging (EEG-fMRI) data to derive EEG-fMRI and electrical source imaging (ESI) maps.

70 Electroencephalogram (EEG) showed generalized spike and polyspike wave dischar

71 vioral assessments and electroencephalogram (EEG) recordings on freely-moving mice.

72 ogical signals such as Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), Elec

73 les are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep.

74 However, electroencephalogram (EEG) changes in the theta-frequency band correlated with

75 Fast beta (20-28 Hz) electroencephalogram (EEG) oscillatory activity may be a useful endophenotype

76 uring, and after ictal electroencephalogram (EEG) discharges.

77 eristics of individual electroencephalogram (EEG) slow waves in young and elderly humans.

78 sification analyses of electroencephalogram (EEG) data.

79 ptiform abnormality on electroencephalogram (EEG) before withdrawal.

80 o assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations.

81 a) using the optimized electroencephalogram (EEG) paradigm of mismatch negativity (MMN).

82 We used portable electroencephalogram (EEG) to simultaneously record brain activity from a clas

83 al recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matte

84 In controls, the electroencephalogram (EEG) exhibited oscillatory activity in the low-theta ran

85 usness extracted from electroencephalograms (EEG), we computed autonomic cardiac markers, such as hea

86 tly, through study of electroencephalograms (EEGs) in humans and local field potentials (LFPs) in non

87 ly model experimental electroencephalograph (EEG) signals.

88 icular, why certain electroencephalographic (EEG) rhythms are linked to memory consolidation is poorl

89 o investigate depth electroencephalographic (EEG) recordings in a large cohort of patients with drug-

90 characterization of electroencephalographic (EEG) activity.

91 -event emergence of electroencephalographic (EEG) findings over 72 hours.

92 m, are pervasive on electroencephalographic (EEG) recordings after acute brain injury.

93 arked by particular electroencephalographic (EEG) signatures, have been linked to memory consolidatio

94 y analysis to scalp electroencephalographic (EEG) recordings during a sequential learning task [2, 3]

95 dence suggests that electroencephalographic (EEG) activity extends far beyond the traditional frequen

96 s," in which unique electroencephalographic (EEG) signals corresponding to the processing of 2 contin

97 Using electroencephalographic (EEG) recordings over the iS1 and electrical stimulation

98 discrimination with electroencephalographic (EEG) frequency tagging following adaptation.

99 Electroencephalography (EEG) was used to simultaneously resolve the temporal and

100 r imaging (DTI), and electroencephalography (EEG).

101 timulation (TMS) and electroencephalography (EEG).

102 halography (MEG) and electroencephalography (EEG).

103 f a smartphone-based electroencephalography (EEG) application, the Smartphone Brain Scanner-2 (SBS2),

104 in humans with both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) in

105 Combining electroencephalography (EEG), structural MRI, and sleep-dependent memory assessm

106 Continuous electroencephalography (EEG) use in critically ill patients is expanding.

107 linical examination, electroencephalography (EEG), somatosensory evoked potentials (SSEP), and serum

108 DEP risk embedded in electroencephalography (EEG) and electrocardiography (ECG) recordings.

109 rain data, including electroencephalography (EEG), has been increasing.

110 ivariate decoding of electroencephalography (EEG) data to investigate perception of stimuli that eith

111 fined four groups of electroencephalography (EEG) event occurrence: pre+post- (+/-), pre+post+ (+/+),

112 aneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured

113 Reliable electroencephalography (EEG) signatures of transitions between consciousness and

114 Simultaneous electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterizati

115 In this study, the electroencephalography (EEG) data recorded from 15 subjects was analyzed to stud

116 sign language, using electroencephalography (EEG) in fluent speakers of American Sign Language (ASL)

117 his hypothesis using electroencephalography (EEG) to measure stimulus-evoked visual responses from hu

118 ctivity measured via electroencephalography (EEG) during pre- and post-training action observation we

119 ith electroencephalography/electromyography (EEG/EMG) to discriminate wake-sleep states.

120 tistate survival model with 3 states (entry, EEG risk, and seizure).

121 decays to <5% by 24 hours if no epileptiform EEG abnormalities emerge, independent of initial clinica

122 In the absence of epileptiform EEG abnormalities, the duration of monitoring needed for

123 re of epileptiform abnormalities, and extend EEG access to new, especially resource-limited, populati

124 neous electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterization of brain-net

125 For EEG assessment, along with linear frequency-based featur

126 patients had significant maps: 47 of 53 for EEG-fMRI, 44 of 53 for ESI, and 34 of 53 for both.

127 , for time spent in slow-wave sleep, and for EEG spectral power in the delta, theta, and sigma ranges

128 new individual frequency-based approach for EEG assessment.

129 , also suppressing theta and gamma frequency EEG activity.

130 nce of two, newly identified, high frequency EEG bands.

131 t of the detrended fluctuation analysis from EEG amplitude fluctuations.

132 To use seizure risk factors from EEG and clinical history to create a simple scoring syst

133 Our hBCI extracts neural information from EEG signals and combines it with response times to build

134 classifier to determine target position from EEG, we were able to decode target positions on the vert

135 ) has been extensively documented in frontal EEG recordings in human participants.

136 1% of the variation in postictal generalized EEG suppression duration (P < 0.02).

137 Conversely, postictal generalized EEG suppression duration explained 34% of the variation

138 findings suggest that postictal generalized EEG suppression is a separate brain state and that seizu

139 duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma pr

140 re termination and the postictal generalized EEG suppression state.

141 al association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden dea

142 ermine the duration of postictal generalized EEG suppression.

143 activity suppression, postictal generalized EEG suppression.

144 We recorded 24 h EEG at the bedside of 18 patients diagnosed to be vigila

145 ossibility that human and rodent hippocampal EEG activity are not as different as previously reported

146 However, intra-hippocampus EEG recordings during virtual navigation in humans have

147 7-9 Hz rhythmicity in raw intra-hippocampus EEG traces during real as well as virtual movement.

148 generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative

149 re, we show that sustained voltages in human EEG recordings contain fine-grained information about th

150 Standard human EEG systems based on spatial Nyquist estimates suggest t

151 Here we assess LRTCs in resting state human EEG data during a 40-hour sleep deprivation experiment b

152 Using hyperscanning EEG recordings, we measured brain-to-brain synchrony in

153 Much of the prior study of >120 Hz EEG is in epileptic brains.

154 fMRI and computational modelling to identify EEG signals reflecting an accumulation process and demon

155 raphy-functional magnetic resonance imaging (EEG-fMRI) data to derive EEG-fMRI and electrical source

156 Here, we investigated the changes in EEG patterns during a continuously administered neurofee

157 ht in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in

158 that responders showed a stronger decline in EEG-vigilance levels from baseline to T1 than non-respon

159 Here, we demonstrate such an interaction in EEG and MEG recordings of task-free human brain activity

160 together revealed a significant reduction in EEG-ECG association in Kcna1-/- mice compared with wild

161 increases in slow-wave sleep, and shifts in EEG spectral power, several of which persisted at 12 wee

162 ded three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subject

163 eeks to months, thus representing individual EEG alpha phenotypes.

164 anatomical factors and forms an individual "EEG fingerprint".

165 ed milder behavioral impairments, infrequent EEG polyspikes, and fewer spectral power alterations.

166 ence epilepsy have shown that the interictal EEG displays augmented beta/gamma power in homozygous st

167 t phase-amplitude coupling in the interictal EEG of both stg/stg and tg/tg mice, compared to +/+ and

168 To further evaluate the interictal EEG, we quantified phase-amplitude coupling (PAC) in stg

169 INTERPRETATION: EEG-fMRI combined with ESI provides a simple unbiased lo

170 Using tools combining MEG and intracranial EEG with brain connectivity analyses, we provide evidenc

171 activity recorded using MEG and intracranial EEG.

172 dal analysis of functional MRI, intracranial EEG recordings, and large-scale neural population simula

173 Using intracranial EEG recordings from rare patients with medically resista

174 Using intracranial EEG, we recorded ventral striatum activity while 7 patie

175 the 21 patients, 19 (90%) underwent Invasive EEG study and 11 (52%) achieved freedom from disabling s

176 sulting in deteriorated performance and less EEG mu suppression over sensorimotor cortex.

177 al status epilepticus during slow sleep-like EEG pattern (six patients); and (iii) West syndrome cons

178 , we assessed resting-state source-localized EEG before and after one to three months of VNS-tone pai

179 a hierarchical sparse inverse solution for M/EEG.

180 d subcortical sources can be resolved with M/EEG.

181 ant changes in the corresponding macroscopic EEG recordings.

182 imum) and from the combination of both maps (EEG-fMRI/ESI spatial intersection).

183 In the current work, we measured EEG activity in the range of 200 to 2000 Hz, in the brai

184 We measured EEG of 20 human subjects while they performed two consec

185 We measured EEG of human subjects during rest and free-choice paradi

186 specifically, PA modulated preparatory motor EEG activity over central electrodes in the right hemisp

187 we found the PA effect on preparatory motor EEG activity to dominate in the beta frequency band.

188 hip between target position and multivariate EEG in an eight-position display.

189 ndings demonstrate the power of multivariate EEG analysis to track feature-based target selection wit

190 We found noninvasive EEG signatures for attentional modulation of neural even

191 Further, time-frequency analyses of EEG signals reveal that metacognitive performance is ass

192 Classical analysis of EEG and ECG recordings separately showed significantly d

193 e introduce a new approach, a combination of EEG and NIRS, to detect driver drowsiness.

194 By using a combination of EEG source separation, time-frequency, and single-trial

195 Using a combination of EEG, fMRI, and diffusion-weighted imaging, we show that

196 Spectral components of EEG were shown to be connected to mental ability both in

197 The 15-minutes Time-course of EEG-vigilance did not differ significantly between group

198 The degree of EEG-ECG association was also proportional to the surviva

199 We link the trial-by-trial dynamics of EEG oscillatory activity during movement preparation to

200 y an independent component analysis (ICA) of EEG data and measured event-related responses by means o

201 s to antidepressants show a) a high level of EEG-vigilance (an indicator of brain arousal) and b) a m

202 gated, for the first time, the parameters of EEG slow waves, including their incidence, amplitude, du

203 classification to assess the specificity of EEG mu rhythm response to action varying in terms of act

204 ), and provided equal performance to that of EEG and SSEP.

205 al seizures, and epileptiform abnormality on EEG before withdrawal.

206 fied and their properties shown to depend on EEG-defined stage of sleep/wakefulness.

207 ake-sleep states were scored based either on EEG/EMG or on WBP signals and sleep-dependent respirator

208 If there were no epileptiform findings on EEG, the risk of seizures within 72 hours was between 9%

209 No effects were found on EEG signatures of spatial attention orienting over occip

210 Statistical tests were performed on EEG and NIRS signals to find the most informative parame

211 lzheimer's disease (AD) without a history or EEG evidence of seizures.

212 To determine how sustained and oscillating EEG signals are related to attention and working memory,

213 effects through changes in known oscillatory EEG signatures of spatial attention orienting and motor

214 lly specific, attention-related, oscillatory EEG modulations.

215 sponders differed in distribution of overall EEG-vigilance stages (F2,133 = 4.780, p = 0.009), with r

216 Polygraphic EEG-electromyographic studies demonstrated a cortical or

217 Clinical factors were used for baseline (pre-EEG) risk.

218 We compared the percentage of resected pre-EEG events, time to recurrence, and the different tailor

219 In the present EEG study, we manipulated visual gaze independently of a

220 ype by investigating the behavioral profile, EEG activity, and seizure threshold.

221 dental to these findings was more pronounced EEG theta activity over frontal, temporal and parietal r

222 from the study highlights that our proposed EEG analysis and markers are more efficient at decipheri

223 making using a combination of psychophysics, EEG and modelling.

224 emonstrate that multivariate analyses of raw EEG data provide a much more fine-grained spatial profil

225 Simultaneous source-reconstructed EEG and cross-frequency directionality analyses revealed

226 wo bars to be probed over time, and recorded EEG in healthy human volunteers.

227 -trial covariations of concurrently recorded EEG and fMRI in a cued visual spatial attention task in

228 be a direct generator of the scalp-recorded EEG, these covariational patterns appear to reflect the

229 We recorded EEG during the RLT in three groups: (a) people with psyc

230 We recorded EEG from human participants (both genders) while they pe

231 We recorded EEG in healthy human participants (9 females and 7 males

232 To address these questions, we recorded EEG in healthy male and female volunteers undergoing sub

233 We recorded EEGs in human participants and found that neural activit

234 nd first-order task performance by recording EEG signals while participants were asked to make a "dia

235 modulations and other consciousness-related EEG markers were combined, single patient classification

236 Change-locked and decision-related EEG responses were found in a centro-parietal scalp loca

237 These findings reveal EEG correlates of tightly coupled sensorimotor processin

238 Cohen's kappa (kappa) for the SBS2 EEG and standard EEG for the epileptiform versus non-epi

239 n using fMRI and concurrently acquired scalp EEG.

240 ious research has suggested that human scalp EEG recordings contain signals that reflect the neural r

241 In human scalp EEG recordings, both sustained potentials and alpha-band

242 age who underwent continuous surface (scalp) EEG (sEEG) recording and multimodality monitoring, inclu

243 e function period determined from a separate EEG experiment.

244 ile spasms and a hypsarrhythmic (or similar) EEG no more than 7 days before enrolment.

245 Our simultaneous EEG and fMRI analysis on 21 human subjects (12 males, 9

246 Here we use simultaneous EEG-fMRI and computational modelling to identify EEG sig

247 Here, we use simultaneous EEG-fMRI to localise the source of delta brush events in

248 In this paper, we use simultaneous EEG/fMRI and multivariate dynamical systems (MDS) analys

249 ed by increased vigilant attention and sleep EEG hallmarks.

250 gree with in-depth phenotyping of both sleep EEG and metabolic traits in 48 family members.

251 d to spectral components of full-night sleep EEG, while controlling for the effects of age.

252 , in contrast to humans, absolute NREM sleep EEG slow-wave activity (SWA, spectral power density betw

253 pathway is specifically linked to these slow EEG spectrum changes.

254 Overall, SND EEG captured more neural information from visual cortex,

255 anced compared to classification with solely EEG markers.

256 sleep is characterized not only by specific EEG waveforms, but also by its circadian organization.

257 d robust behavioral impairments, spontaneous EEG polyspikes, and increased cortical and hippocampal p

258 cator of brain arousal) and b) a more stable EEG-vigilance regulation than non-responders.

259 kappa (kappa) for the SBS2 EEG and standard EEG for the epileptiform versus non-epileptiform outcome

260 SBS2 and standard EEG were performed in people with suspected epilepsy in

261 detected on 14% of SBS2 and 25% of standard EEGs.

262 were abnormally organized with resting state EEG.

263 ated depressed patients 15-min resting-state EEGs were recorded off medication (baseline).

264 al epilepsy who underwent presurgical stereo-EEG (SEEG) were included in the study.

265 We found that sustained EEG activity could be used to decode the remembered orie

266 eprivation (SD) with a slow-wave sleep (SWS) EEG delta (1.0 to 4.0 Hz) power rebound like WT litterma

267 ked for groups of students with synchronized EEG acquisition.

268 lity (sympathetic adrenal medullary system), EEG event-related potentials (nociceptive cortical activ

269 sequently, mice were subjected to telemetric EEG recording and video monitoring.

270 ation was derived from each individual test (EEG-fMRI global maxima [GM]/ESI maximum) and from the co

271 The EEG variables were interpreted using standardized termin

272 Our goal was to compare the EEG/EMG-based and the WBP-based scoring of wake-sleep st

273 hat modulations of brain oscillations in the EEG alpha frequency band in posterior cortex can dissoci

274 involve spike-wave discharges (SWDs) in the EEG and interruption of consciousness and ongoing behavi

275 e-like "REM beta tufts" are described in the EEG of healthy subjects, which may reflect the functioni

276 ndex mediated by cardiac interference in the EEG.

277 Four weeks into the EEG recording period, at a time when spontaneous seizure

278 Novel analysis of the EEG and ECG together revealed a significant reduction in

279 ponse at 3 Hz in the frequency domain of the EEG over right occipito-temporal channels, replicating o

280 The shape of the EEG spectrum in sleep relies on genetic and anatomical f

281 n was accomplished via quantification of the EEG's spectral power and event-related brain potentials

282 ts many different spectral properties on the EEG, including alpha oscillations (8-12 Hz), Slow Wave O

283 en be relayed to cortex and expressed on the EEG.

284 of posterior AR components contribute to the EEG is essential for clinical neuroscience as an objecti

285 gained and guidelines established within the EEG working group of the Canadian Biomarker Integration

286 Functional Source Separation (FSS) to their EEG recordings.

287 A detailed analysis of these EEG changes showed that the alpha power rose because of

288 and genes influencing traits such as timing, EEG characteristics, sleep duration, and response to sle

289 howed that pattern separation was related to EEG oscillatory parameters of non-REM sleep serving as m

290 oscillations based on three experiments (two EEG and one psychophysics) by demonstrating that alpha-b

291 We tested these models in two EEG experiments in humans where we analyzed the effects

292 ldren with drug-resistant epilepsy underwent EEG-fMRI.

293 ients had more than 6 hours of uninterrupted EEG recordings.

294 We used EEG and EMG to investigate the development of corticomus

295 ited spontaneous seizures during a 7 d video-EEG recording period.

296 Finally, video-EEG of Emx-Cre; Clock(flox/flox) mice reveals epileptifo

297 on cortical hyperexcitability during in vivo EEG recordings in awake mice where the effects of the pr

298 While EEG analysis exhibited significant discrepancies between

299 Here, we recorded neural activity with EEG as subjects performed a two-interval forced-choice c

300 variation in the strength of the peaks with EEG-defined sleep/wakefulness.