What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Neuroscience
Lucas C. Parra mostly deals with Artificial intelligence, Neuroscience, Electroencephalography, Algorithm and Pattern recognition.
Lucas C. Parra has researched Artificial intelligence in several fields, including Machine learning, Brain–computer interface and Computer vision.
His Transcranial direct-current stimulation, Transcranial alternating current stimulation and Brain activity and meditation study in the realm of Neuroscience interacts with subjects such as Chemistry.
His Electroencephalography study incorporates themes from Rapid serial visual presentation, Neurophysiology, Correlation, Functional magnetic resonance imaging and Ground truth.
His research integrates issues of Speech recognition, Iterative reconstruction, Blind signal separation and Generalization in his study of Algorithm.
Lucas C. Parra has included themes like Linear model and Scalp in his Pattern recognition study.
His most cited work include:
- Convolutive blind separation of non-stationary sources (778 citations)
- Recipes for the linear analysis of EEG. (419 citations)
- Optimized multi-electrode stimulation increases focality and intensity at target. (353 citations)
What are the main themes of his work throughout his whole career to date?
Lucas C. Parra mainly focuses on Artificial intelligence, Electroencephalography, Neuroscience, Stimulation and Pattern recognition.
His biological study spans a wide range of topics, including Machine learning, Brain–computer interface, Computer vision and Expectation–maximization algorithm.
His Electroencephalography study combines topics from a wide range of disciplines, such as Stimulus, Speech recognition, Correlation and Audiology.
His work carried out in the field of Speech recognition brings together such families of science as Algorithm and Blind signal separation.
In the subject of general Neuroscience, his work in Transcranial direct-current stimulation and Neuron is often linked to Chemistry, thereby combining diverse domains of study.
His studies in Pattern recognition integrate themes in fields like Probability distribution and Pyramid.
He most often published in these fields:
- Artificial intelligence (24.56%)
- Electroencephalography (23.86%)
- Neuroscience (20.70%)
What were the highlights of his more recent work (between 2018-2021)?
- Stimulation (15.09%)
- Neuroscience (20.70%)
- Transcranial direct-current stimulation (13.68%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Stimulation, Neuroscience, Transcranial direct-current stimulation, Electroencephalography and Artificial intelligence.
Lucas C. Parra combines subjects such as Waveform, Intensity, Cortical surface and Direct current with his study of Stimulation.
His Neuroscience research is multidisciplinary, incorporating perspectives in Hebbian theory and Modulation.
His Transcranial direct-current stimulation research incorporates themes from Magnetic resonance imaging, Nuclear magnetic resonance and Craving.
The Electroencephalography study combines topics in areas such as Speech recognition, Disorders of consciousness, Correlation and Voice activity detection.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Sample size determination and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipeline (75 citations)
- Can transcranial electric stimulation with multiple electrodes reach deep targets (39 citations)
- Direct current stimulation boosts hebbian plasticity in vitro. (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Stimulation, Electroencephalography, Neuroscience, Transcranial direct-current stimulation and Brain activity and meditation.
His Stimulation research incorporates elements of Time constant, Cortical surface, Nuclear magnetic resonance and Modulation.
His Electroencephalography research is multidisciplinary, relying on both Correlation, Audiology, Speech comprehension, Artificial intelligence and Pattern recognition.
His Artificial intelligence research integrates issues from Signal processing, Event-related potential and Magnetoencephalography.
Lucas C. Parra has researched Pattern recognition in several fields, including Noise reduction and Scalp.
His study looks at the intersection of Neuroscience and topics like Hebbian theory with Associative learning, Membrane potential and Depolarization.
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