Intracortical brain-machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control. We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0-150 Hz range. While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0-80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80-150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0-40, 40-80, and 80-150 Hz)...Continue Reading
Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization
Encoding of movement direction in different frequency ranges of motor cortical local field potentials
Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia
Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices
Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions
Inducing γ oscillations and precise spike synchrony by operant conditioning via brain-machine interface
Control of a biomimetic brain machine interface with local field potentials: performance and stability of a static decoder over 200 days
Long term, stable brain machine interface performance using local field potentials and multiunit spikes
Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces
Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering
Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device
A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.
A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates
Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.
Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation.
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.