m8ta
You are not authenticated, login.
text: sort by
tags: modified
type: chronology
[0] Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA, Learning to control a brain-machine interface for reaching and grasping by primates.PLoS Biol 1:2, E42 (2003 Nov)

{172}
hide / / print
ref: Foffani-2004.07 tags: STN motor preparation human 2003 basal_ganglia DBS SMA date: 01-26-2012 17:23 gmt revision:3 [2] [1] [0] [head]

PMID-15249649 Involvement of the human subthalamic nucleus in movement preparation

  • STN receives large afferent from SMA, so it should be involved in movement planning.
  • the STN and nearby structures are active before self-paced movements in humans.
  • normal patients show a negative EEG movement-related potential (MRP) starting 1-2 seconds before the onset of self-paced movements.
  • STN also shows premovement negative MRP.
    • REquire very sensitive methods to record this MRP -- it's on the order of 1 uv.
  • the amplitude of the scalp MRP is reduced in parkinson's patients.
    • impairment of movement preparation in PD may be related to deficits in the SMA and M1, e.g. underactivity.
    • the MRP is normalized with the administration of levodopa.
  • MPTP monkeys have increased activity in the STN
  • examined the role of the STN in movement preparation and inhibition via MRP recorded from DBS electrodes in the STN + simultaneously recorded scalp electrodes.
  • their procedure has the leads externalized during the first week after surgery.

{318}
hide / / print
ref: Carmena-2003.11 tags: Carmena nicolelis BMI learning 2003 date: 01-08-2012 18:53 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-14624244[0] Learning to control a brain-machine interface for reaching and grasping by primates.

  • strong focus on learning & reorganization.
  • Jose's first main paper.
  • focuses on two engineering / scientific questions: what signal to use, and how much of it, and from where.
    • As for where, of course we suggest that the representation is distributed.
  • Quality of predictions: gripping force > hand velocity > hand position.
  • Showed silent EMGs during BMI control.
  • Put a robot in the feedback path; this ammounted for some nonlinearities + 60-90ms delay.
  • Predictions follow anatomical expectation:
    • M1 (33-56 cells) predicts 73% variance for hand pos, 66% velocity, 83% for gripping force .
    • SMA (16-19 cells) 51% position, 51% velocity, 19% gripping force.
    • They need a table for this shiz.
  • Relatively high-quality predictions. (When I initially looked at the data, I was frustrated with the noise!)
  • Learning was associated with increased contribution of single units.
    • appeared to be more 'learning' in SMA.
    • Training on a position model seemed to increase the ctx representation of hand position.
  • changes between pole control and brain control:
    • 68% of of sampled neurons showed reduced tuning in BCWOH
    • 14% no change
    • 18% enhanced tuning.
  • Directional tuning curves clustered in a band during brain control -- neurons clustering around the first PC?
    • All cortical areas tested showed increases in correlated firing -- arousal?
    • this puts some movements into the nullspace of the Wiener matrix. Or does it? should have had the monkey make stereotyped movements to dissociate movement directions.
  • Knocks {334} in that:
    • preferred directions were derived not from actual movements, but from firing rates during target appearance time windows.
    • tuning strength could have increased simple because the movements became straighter with practice.
  • From Fetz, {329}: Interestingly, the conversion parameters obtained for one set of trials provided increasingly poor predictions of future responses, indicating a source of drift over tens of minutes in the open-loop condition. This problem was alleviated when the monkeys observed the consequences of their neural activity in ‘real time’ and could optimize cell activity to achieve the desired goal under ‘closed-loop’ conditions.

____References____

{900}
hide / / print
ref: Helms-2003.01 tags: Schwartz BMI adaptive control Taylor Tillery 2003 date: 11-26-2011 00:58 gmt revision:1 [0] [head]

PMID-12929922 Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

  • Lays out the coadaprive algorithm.
  • with supervised / adaptive training, ML estimator is able to get 80% of the targets correct.
  • Reviews in the Neurosciences (conference) Workshop on Neural and Artificial Computation.