about
Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders.Statistical modeling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humansDiagnosis of Alzheimer's disease from EEG signals: where are we standing?Improving the specificity of EEG for diagnosing Alzheimer's disease.Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands.On the synchrony of steady state visual evoked potentials and oscillatory burst eventsSlowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics.EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts.Techniques for early detection of Alzheimer's disease using spontaneous EEG recordings.Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease.Quantifying the similarity of multiple point processes with application to early diagnosis of Alzheimer's disease from EEG.Quantifying statistical interdependence, part III: N > 2 point processes.Quantifying statistical interdependence by message passing on graphs-part I: one-dimensional point processes.A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG.Quantifying statistical interdependence by message passing on graphs-part II: multidimensional point processes.Removal of ocular artifacts for high resolution EEG studies: a simulation study.EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique.Inferring Brain Networks through Graphical Models with Hidden VariablesAnalyzing brain signals by combinatorial optimizationOn the synchrony of empirical mode decompositions with application to electroencephalographyA Novel Measure for Synchrony and its Application to Neural SignalsBlind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer’s Disease
P50
Q30426810-2F5F9ED2-2E25-46A9-876F-56396AAA0826Q33325879-9B9B1A31-EC26-4EE2-8C28-31327C0049F7Q34114412-19D3ACEC-A24A-4AE3-A4CC-DEB8D322F27DQ35029378-5E1C0479-A3AA-4194-8A77-C18625763362Q37644997-15CECE9E-D6BE-47A9-95AF-3D740FFA314CQ40089246-8BC95124-BDA7-4BCC-AF55-C7B3ABD01AA8Q41811356-D175AF29-DB6A-40DE-B8D8-0FB7350D5F9BQ41960933-681034EF-E5C6-4FFB-BAB1-DBD6374B9011Q45965636-2236266F-B7AC-4260-9501-53ABA0DC0D8BQ46797295-291E992F-2E5A-479E-A59D-628886F691A3Q48223566-0A8CDF29-2320-4E53-BA1F-A6687092909BQ48706290-0E75A386-AD33-4FF0-9BB8-6A3FD46E5AF4Q48794791-CF9182CA-F181-4203-9C82-1D0B81B9DE6BQ51496291-950DFDE9-EFD9-40C7-8A66-A8A74535B859Q51802697-4F27D340-9B1C-44EF-8520-99E8BFEA224EQ51815343-BD33EF59-259F-493C-B688-899D89323D34Q51834615-F01F8CF3-22E0-4C94-98AD-02F492F294E9Q51903050-3FE2BBF0-4371-4688-A100-E283CE7BBD99Q53156906-F7D76F7B-AD16-4632-BCE6-366B5D797FE7Q60486613-32C29D84-5EA5-4968-AC46-B6A6DEA65521Q60486804-A2DA5892-03B9-4310-9412-13BACB1F01BDQ60486843-66BACBB8-4513-42F2-B00A-BFF673EBD09BQ60486859-1D528E62-2999-4921-8ACC-5725B4514C5BQ60486925-2A090715-4FA6-4EF6-858A-F298FE6215FA
P50
description
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Francois Benoit Vialatte
@ast
Francois Benoit Vialatte
@en
Francois Benoit Vialatte
@es
Francois Benoit Vialatte
@nl
Francois Benoit Vialatte
@sl
type
label
Francois Benoit Vialatte
@ast
Francois Benoit Vialatte
@en
Francois Benoit Vialatte
@es
Francois Benoit Vialatte
@nl
Francois Benoit Vialatte
@sl
prefLabel
Francois Benoit Vialatte
@ast
Francois Benoit Vialatte
@en
Francois Benoit Vialatte
@es
Francois Benoit Vialatte
@nl
Francois Benoit Vialatte
@sl
P106
P1153
13106117900
P2456
P31
P496
0000-0002-9162-4278