Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

30.8K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
30.8K
Psychosis: Goals of Pharmacotherapy01:26

Psychosis: Goals of Pharmacotherapy

486
Antipsychotic drugs are a crucial treatment method for acute and chronic psychoses, bipolar illness, and behavioral disorders. The selection of these drugs depends on several factors, including the state of the disease, clinical judgment, possible drug interactions, and the patient's sensitivity to adverse effects. In immediate scenarios, such as delirium and dementia, short-term treatment with low doses of high-potency typical or atypical agents can effectively manage symptom exacerbation.
486
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

48.4K
Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
48.4K
Finding Electric Potential From Electric Field01:13

Finding Electric Potential From Electric Field

5.4K
For a system of charges, it is easy to calculate the system's potential because potential is a scalar quantity. However, in some instances where calculating the electric field is more straightforward than finding the potential, the electric field is used to calculate the system's potential. For a positive charge, the electric field is radially outward, and the potential is positive at any finite distance from the positive charge. In such an electric field, the motion away from the...
5.4K
Determining Electric Field From Electric Potential01:12

Determining Electric Field From Electric Potential

4.9K
The electric field and electric potential are related to each other. If the electric field at various points in the region of interest is known, it can be used to calculate the electric potential difference between any two points. Similarly, if the electric potential is known for various points, then it is possible to calculate the electric field.
In general, regardless of whether the electric field is uniform, it points in the direction of decreasing potential because the force on a positive...
4.9K
Electric Potential Energy in a Uniform Electric Field01:09

Electric Potential Energy in a Uniform Electric Field

6.3K
When an electric field accelerates a free positive charge, it acquires kinetic energy. This process is analogous to an object being accelerated by a gravitational field as if the charge were going down an electrical hill where its electric potential energy is converted into kinetic energy, although, of course, the sources of the forces are very different. The electrostatic or Coulomb force acting on the positive test charge is conservative, which means that the work done on a test charge is...
6.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Anti-LAG-3 with or without anti-PD-1 in recurrent glioblastoma: a phase 1 trial.

Nature medicine·2026
Same author

Utility of repeat stereotactic EEG in pediatric patients with tuberous sclerosis.

Epilepsia open·2026
Same author

A wireless subdural-contained brain-computer interface with 65,536 electrodes and 1,024 channels.

Nature electronics·2026
Same author

APOL1-risk alleles modulate T-cell receptor signaling to promote allograft rejection.

The Journal of clinical investigation·2026
Same author

Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma.

Life science alliance·2026
Same author

Microscale organization and separability of upper extremity representations in the human motor homunculus.

Research square·2026

Related Experiment Video

Updated: Jan 26, 2026

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

12.9K

Minimax-optimal decoding of movement goals from local field potentials using complex spectral features.

Marko Angjelichinoski1, Taposh Banerjee2, John Choi3

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America.

Journal of Neural Engineering
|April 17, 2019
PubMed
Summary

This study enhances neural decoding by incorporating phase information from local field potentials (LFPs) for predicting eye movement goals. The novel complex spectrum approach significantly improves prediction accuracy compared to traditional methods.

More Related Videos

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats
10:46

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

Published on: June 22, 2017

16.4K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K

Related Experiment Videos

Last Updated: Jan 26, 2026

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

12.9K
Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats
10:46

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

Published on: June 22, 2017

16.4K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Predicting eye movement goals from neural signals is crucial for understanding motor control.
  • Local field potentials (LFPs) are widely used for neural decoding, but phase information has been underutilized.

Purpose of the Study:

  • To develop a statistically optimal framework for decoding eye movement intentions using LFPs.
  • To investigate the utility of incorporating LFP phase information into neural decoding models.

Main Methods:

  • Formulated decoding as a statistically optimal problem using Gaussian sequence modeling and Pinsker's theorem.
  • Generated minimax-optimal LFP estimates incorporating phase information via complex Fourier coefficients.
  • Applied a low-pass filtering effect to attenuate higher frequency components in the LFP features.

Main Results:

  • Achieved prediction accuracy up to [Formula: see text] at superficial cortical depths.
  • Demonstrated significant performance improvement over conventional power spectrum-based decoders.
  • Showcased the effectiveness of complex spectrum-based decoding for neural signals.

Conclusions:

  • Low-pass filtered LFP signals, particularly those incorporating phase information, offer highly reliable neural decoding.
  • The proposed complex spectrum approach represents a significant advancement in decoding intended motor actions from neural activity.