21 Feb 2020
- FES = implanted electrode in muscles.
- Use intracortical recordings from M1 to predict muscle contractions in three groups of muscles.
- During experiment, block muscle groups.
- Grasping dysfunctional when muscle contractions blocked.
- Grasping restored by FES stimulation predicted from M1 recordings.
2 Feb 2020
- Use mean power of LFP between 200Hz – 400Hz band. For each channel compute mean power.
- decoded into 4 class (wheel chair task)
- uses LDA for decoding
- Adaptive
- method 1: update dataset used to train LDA
- there is a fix set of training data used to compute LDA model. The training dataset is a fixed size. Update training dataset when you have good data.
- criteria
- predict probability > 99%
- same output in previous 5 timesteps
- method 2: adaptive channel selection
- method 1: update dataset used to train LDA
19 Jan 2020
[Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control]
(https://web.stanford.edu/~mgolub/publications/GolubCONeur2016.pdf)
29th Nov 2019
Rajesh Rao: Brain-computer interfacing: an introduction
20 nov 2019
Approaches to large scale neural recording by chronic implants for mobile BCIs
15 nov 2019
several papers by Zhang Yin and Steven Chase
talk to public by Jose Carmena
6 nov 2019
rao’s paper about bidirectional neural interfaces
5 nov 2019
- Ten-dimensional anthropomorphic arm control in a human brain−machine interface: difficulties, solutions, and limitations
- Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array
- Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex
- Control of a brain–computer interface without spike sorting
- Self-recalibrating classifiers for intracortical brain–computer interfaces
- Intra-day signal instabilities affect decoding performance in an intracortical neural interface system
Intracortical recording stability in human brain–computer interface users
[Silicon Valley new focus on brain computer interface: hype or hope for new applications? (2016)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343225/)
invasive vs non invasive signals for BCI. Will one prevail (2016)
3 nov 2019
- a new frontier: the convergence of nanotechnology, brain machine interface and artificial intelligence
- Computational neuroscience and neuroinformatics: Recent progress and resources.
- Arto Nurmikko – BCI PI from brown
konrad kording podcast neuralink
physical principles for scalable neural recording
neurallink live coverage by andrew hires
Technical notes 