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Tracking Keystroke Sequences at the Cortical Level Reveals the Dynamics of Serial Order Production
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In: ISSN: 0898-929X ; EISSN: 1530-8898 ; Journal of Cognitive Neuroscience ; https://hal.archives-ouvertes.fr/hal-02093896 ; Journal of Cognitive Neuroscience, Massachusetts Institute of Technology Press (MIT Press), 2019, 31 (7), pp.1030-1043. ⟨10.1162/jocn_a_01401⟩ (2019)
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42 |
Motor resonance during linguistic processing as shown by EEG in a naturalistic VR environment
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In: ISSN: 0278-2626 ; EISSN: 1090-2147 ; Brain and Cognition ; Society for the Neurobiology of Language ; https://hal.archives-ouvertes.fr/hal-02309994 ; Brain and Cognition, Elsevier, 2019, 134, pp.44-57. ⟨10.1016/j.bandc.2019.05.003⟩ (2019)
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44 |
Neural Encoding of Continuous Attended Speech in Monolingual and Bilingual Listeners ...
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45 |
Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment
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In: Entropy ; Volume 21 ; Issue 7 (2019)
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46 |
Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers
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In: Sensors ; Volume 20 ; Issue 1 (2019)
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Abstract:
Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson&rsquo ; s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.
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Keyword:
ANOVA; electroencephalography; emotion; entropy; Hilbert transform; Pearson’s correlation; spectral power
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URL: https://doi.org/10.3390/s20010059
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47 |
An Ocean of Brain Waves: Analyzing Methods to Find ERP Signals in the Noise
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In: Student Research Symposium (2019)
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52 |
Unitization modulates recognition of within-domain and cross-domain associations: Evidence from event-related potentials. ...
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53 |
Neural Signal to Violations of Abstract Rules Using Speech-Like Stimuli. ...
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54 |
Balancing Prediction and Sensory Input in Speech Comprehension: The Spatiotemporal Dynamics of Word Recognition in Context. ...
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55 |
Bilingualism and language similarity modify the neural mechanisms of selective attention. ...
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56 |
The neural representation of simultaneous speech and music ...
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58 |
Decoding hidden language: towards a reliable neural assessment of language comprehension in minimally-verbal autistic children
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Petit, Selene. - : Sydney, Australia : Macquarie University, 2019
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Attention Modulates Electrophysiological Responses to Simultaneous Music and Language Syntax Processing
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In: Brain Sciences ; Volume 9 ; Issue 11 (2019)
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Investigation of Objective Neurophysiological Measures of Auditory Temporal Processing Abilities and Their Applicability in Cochlear Implant Rehabilitation
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WAECHTER, SASKIA MARLEEN. - : Trinity College Dublin. School of Engineering. Discipline of Electronic & Elect. Engineering, 2019
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