about
Errare machinale est: the use of error-related potentials in brain-machine interfacesWord pair classification during imagined speech using direct brain recordings.Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.Detection of self-paced reaching movement intention from EEG signals.Corticospinal neuroprostheses to restore locomotion after spinal cord injury.Personalized neuroprosthetics.The use of intracranial recordings to decode human language: Challenges and opportunities.Harnessing Prefrontal Cognitive Signals for Brain-Machine Interfaces.Action prediction based on anticipatory brain potentials during simulated driving.Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance.Anticipation- and error-related EEG signals during realistic human-machine interaction: a study on visual and tactile feedback.Self-paced movement intention detection from human brain signals: Invasive and non-invasive EEG.Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation.Behavioral and Cortical Effects during Attention Driven Brain-Computer Interface Operations in Spatial Neglect: A Feasibility Case Study.Modular organization of reaching and grasping movements investigated using EEG microstates.EEG topographies provide subject-specific correlates of motor control.Action Monitoring Cortical Activity Coupled to Submovements.Single trial prediction of self-paced reaching directions from EEG signals.Learning from EEG error-related potentials in noninvasive brain-computer interfaces.Offline decoding of upper limb muscle synergies from EEG slow cortical potentials.Brain-machine interface: closer to therapeutic reality?Transferring brain-computer interfaces beyond the laboratory: successful application control for motor-disabled users.Inferring driver's turning direction through detection of error related brain activity.EEG-based decoding of error-related brain activity in a real-world driving task.Subject-oriented training for motor imagery brain-computer interfaces.Quantifying electrode reliability during brain-computer interface operation.Neural Encoding of Auditory Features during Music Perception and Imagery.Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study.Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors.Electrically Assisted Movement Therapy in Chronic Stroke Patients With Severe Upper Limb Paresis: A Pilot, Single-Blind, Randomized Crossover Study.Invasive or noninvasive: understanding brain-machine interface technology.The effect of multimodal and enriched feedback on SMR-BCI performance.Learning reaching strategies through reinforcement for a sensor-based manipulator.Improved recognition of error related potentials through the use of brain connectivity features.EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb.Detection of anticipatory brain potentials during car driving.Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project.Rewards-driven control of robot arm by decoding EEG signals.Very high frequency oscillations (VHFO) as a predictor of movement intentions.Relevant EEG features for the classification of spontaneous motor-related tasks.
P50
Q26866159-0EDC56A0-BD2F-4326-AFD0-6DE8DB6F6886Q27316102-2796EDB7-E3B7-4A84-A721-5B6350EDB95BQ30664099-FF0A43A6-FD08-4FA2-A02E-3853C810E7BEQ36280226-28D491B1-5A80-4AB9-95C6-42410E89F74CQ38153514-76C0CF59-060C-4B6D-B2F0-F35BBBDFA88FQ38160373-9534D047-963F-4518-A873-24A3AC923EFDQ38390558-D8275B40-8C44-4589-B4FB-9A1B2B0BF6D0Q38733437-3F067549-7803-4206-A800-67192AAE7A44Q38960628-B0E2BD92-9B4D-4FF5-A941-EC12A19B7769Q39068407-EBC7ED1B-FC93-4549-A236-0CA055AD1BE5Q39475359-3FEA7CC9-2FCB-454D-94BC-3FD930375B25Q39475686-9FD0E128-DB69-4FC2-94B4-3BEA83F6A3D2Q40242513-7D428642-CE44-4376-A870-C07669C62E4AQ41024522-E31E3AA9-10AE-40A3-906D-C619098EEFFEQ41607040-E4553FB7-A0C3-449D-B7C9-7BE6191ABB87Q42379013-41888D97-600A-4BE9-8617-2CA2B6360223Q42642579-C09B5E4A-1BA1-4609-A3CB-6ED7CA118F73Q42761503-3E649F0B-EDBD-4832-8838-DB2E81812768Q44289892-5E584017-2464-428C-8E21-A9F1E37D9A62Q44937931-21067048-F36D-4DF0-A0BF-93D4A8E156F9Q45083254-6EE2B056-CC7F-46AB-BD6F-7703A93AB01BQ45118615-27DCC7EF-5B60-4AC8-9B3E-767AA152D83AQ45348382-D6C08B7C-6D05-443A-8F57-058EE96D4FEEQ45952894-29B3ED38-C648-4D8A-B0FB-4AEB7CDCD530Q45955389-4C85AA21-BDA6-4335-9F5E-8519DE67F64BQ46846087-42923ADF-FE9E-4871-A556-C6958B338A15Q46886143-4A642E0F-62C5-4B68-A9A9-29EE3104653CQ47093928-0783FE93-873F-4BDC-A21B-81156191051AQ47163316-59A4DF6E-AF2E-4887-A98F-03656952A37EQ47373651-A1FC9566-3A51-4913-BB6E-BDC845406C23Q47442235-147AB4BC-D71D-411E-8E0E-6E5D740E9209Q47635320-787DAF43-61E5-45F5-A7E2-59EE7BB4F7F0Q48008104-83DF2AE4-ED80-439D-ADC7-4D0F8B809850Q48183252-04DAE5A8-AAF8-4155-BAD5-CB367C1541F0Q48184499-421C9D45-B370-4361-AAC3-ED14146F8314Q48184686-D2FD0002-2650-4491-972B-CBFBB956C4E3Q48235174-96C33877-298C-427E-9E78-CDB84E6C56E1Q48385643-54040D9A-4976-4DA5-A11C-63C7EAE4DCB8Q48449971-6A9DE09C-9FFB-4578-8D6F-2C62439814AEQ48653187-F172899E-263C-4D27-A959-223BBE503145
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Jose del R Millan
@ast
Jose del R Millan
@en
Jose del R Millan
@es
Jose del R Millan
@nl
Jose del R Millan
@sl
type
label
Jose del R Millan
@ast
Jose del R Millan
@en
Jose del R Millan
@es
Jose del R Millan
@nl
Jose del R Millan
@sl
prefLabel
Jose del R Millan
@ast
Jose del R Millan
@en
Jose del R Millan
@es
Jose del R Millan
@nl
Jose del R Millan
@sl
P1053
F-1696-2011
P106
P1153
7202018324
P21
P2456
m/JosedelRMillan
P31
P3829
P496
0000-0001-5819-1522
P569
2000-01-01T00:00:00Z