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Review of Brain-Machine Interfaces Used in Neural Prosthetics with New Perspective on Somatosensory Feedback through Method of Signal BreakdownToward more versatile and intuitive cortical brain-machine interfacesEncoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neuronsA Wireless 32-Channel Implantable Bidirectional Brain Machine Interface.Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward.Spiking and LFP activity in PRR during symbolically instructed reaches.Direct brain recordings fuel advances in cognitive electrophysiologyMicropower CMOS Integrated Low-Noise Amplification, Filtering, and Digitization of Multimodal Neuropotentials.Electroencephalographic (EEG) control of three-dimensional movement.Decoding flexion of individual fingers using electrocorticographic signals in humans.What limits the performance of current invasive brain machine interfaces?A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfacesIdentifying and quantifying multisensory integration: a tutorial review.Real-time decision fusion for multimodal neural prosthetic devices.Brain control of movement execution onset using local field potentials in posterior parietal cortexLong term, stable brain machine interface performance using local field potentials and multiunit spikes.Cognitive neural prostheticsHigh accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials.A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapiesTime-varying covariance of neural activities recorded in striatum and frontal cortex as monkeys perform sequential-saccade tasksSeven years of recording from monkey cortex with a chronically implanted multiple microelectrode.Topological analysis of population activity in visual cortex.Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex.Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis.Real-time estimation and biofeedback of single-neuron firing rates using local field potentials.Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices.Frequency dependence of signal power and spatial reach of the local field potential.Applying the multivariate time-rescaling theorem to neural population models.Local field potentials mitigate decline in motor decoding performance caused by loss of spiking units.Future developments in brain-machine interface researchInformative features of local field potential signals in primary visual cortex during natural image stimulation.Volitional control of single cortical neurons in a brain-machine interface.Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo.Coherent neural representation of hand speed in humans revealed by MEG imagingEvoked potentials in motor cortical local field potentials reflect task timing and behavioral performance.Optimizing the decoding of movement goals from local field potentials in macaque cortex.Relationships between spike-free local field potentials and spike timing in human temporal cortexChromatic and Achromatic Spatial Resolution of Local Field Potentials in Awake Cortex.Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change.
P2860
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P2860
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
Selecting the signals for a brain-machine interface.
@ast
Selecting the signals for a brain-machine interface.
@en
type
label
Selecting the signals for a brain-machine interface.
@ast
Selecting the signals for a brain-machine interface.
@en
prefLabel
Selecting the signals for a brain-machine interface.
@ast
Selecting the signals for a brain-machine interface.
@en
P2093
P1476
Selecting the signals for a brain-machine interface.
@en
P2093
Bijan Pesaran
Richard A Andersen
Sam Musallam
P304
P356
10.1016/J.CONB.2004.10.005
P577
2004-12-01T00:00:00Z