Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. LMP decoding enabled quick and ...Continue Reading
Encoding of movement direction in different frequency ranges of motor cortical local field potentials
Early visuomotor representations revealed from evoked local field potentials in motor and premotor cortical areas
Comparison of direction and object selectivity of local field potentials and single units in macaque posterior parietal cortex during prehension
Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information
Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia
Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms
Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex
A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces
Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices
Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials
Long term, stable brain machine interface performance using local field potentials and multiunit spikes
Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex
Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions
Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements
Minimax-optimal decoding of movement goals from local field potentials using complex spectral features
A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates
The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions
Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks
Using Coherence-based spectro-spatial filters for stimulus features prediction from electro-corticographic recordings.
A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.
Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation.
Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy.
Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior.
A brain-computer interface, also known as a brain-machine interface, is a bi-directional communication pathway between an external device and a wired brain. Here is the latest research on this topic.