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[0] Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV, A high-performance brain-computer interface.Nature 442:7099, 195-8 (2006 Jul 13)[1] Shenoy KV, Meeker D, Cao S, Kureshi SA, Pesaran B, Buneo CA, Batista AP, Mitra PP, Burdick JW, Andersen RA, Neural prosthetic control signals from plan activity.Neuroreport 14:4, 591-6 (2003 Mar 24)

[0] Is this the bionic man?Nature 442:7099, 109 (2006 Jul 13)[1] Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP, Neuronal ensemble control of prosthetic devices by a human with tetraplegia.Nature 442:7099, 164-71 (2006 Jul 13)[2] Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV, A high-performance brain-computer interface.Nature 442:7099, 195-8 (2006 Jul 13)[3] Shenoy KV, Meeker D, Cao S, Kureshi SA, Pesaran B, Buneo CA, Batista AP, Mitra PP, Burdick JW, Andersen RA, Neural prosthetic control signals from plan activity.Neuroreport 14:4, 591-6 (2003 Mar 24)

[0] Birbaumer N, Cohen LG, Brain-computer interfaces: communication and restoration of movement in paralysis.J Physiol 579:Pt 3, 621-36 (2007 Mar 15)

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ref: Schalk-2000.12 tags: error potential EEG wadsworth BCI 2000 BMI date: 01-23-2013 07:15 gmt revision:3 [2] [1] [0] [head]

PMID-11090763[0] EEG-based communication: presence of an error potential.

  • Idea: they trained a set of subjects to use mu/beta rhythm over central sulcus (sensorimotor) amplitude to move a cursor around the screen, and simultaneously monitored for error-related potentials to correct errors in decoding.
  • patients get 80-97% accuracy in a binary choice task.
  • look at the end of a trial to see if they 'approve' of the choice.
  • had to remove eyeblink artifacts! however, people tend to defer eyeblinks until the end of performance.
  • error = average EEG during error trials - EEG during correct trial. (a potential)
    • the error was over primary motor/ somatosensory cortex.
    • used adaptive noise cancellation to remove some of the eyeblink EMG.

____References____

[0] Schalk G, Wolpaw JR, McFarland DJ, Pfurtscheller G, EEG-based communication: presence of an error potential.Clin Neurophysiol 111:12, 2138-44 (2000 Dec)

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ref: Santhanam-2006.07 tags: Shenoy BMI BCI trials date: 01-08-2012 23:37 gmt revision:4 [3] [2] [1] [0] [head]

PMID-16838020[0] A high-performance brain-computer interface

  • the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements.
    • What is the eye-movement rate?
  • implanted in PMD. 96 electrodes (utah array).
  • used an instructed-delay task. figure 1
    • monkey had to move to target when center target disappeared. peripheral target appeared several seconds prior.
  • actually had the monkey reach to targets; if correct, monkey was immediately rewarded.
    • real movement trials were interspersed to keep the monkey engaged.
  • decoding model: assume that the spike counts come from a poisson or gaussian distribution. Apply ML decoding.
    • poisson better than gaussian.
  • up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes.
    • Peak of continuous control = 1.6 bits per second.
  • ITRC = information transfer rate capacity. this metric is proportional to the single trial accuracy / trial length (sorta, see ref 23 - Blahut-Arimoto algorithm)
  • most of their neurons seem to be responsive to actual movements (que supressa!)
  • maximum bandwidth with a trial length of 250ms.
    • lots of other good information-theoretic analysis.
  • PMID-12657892[1] Neural prosthetic control signals from plan activity. -- the preceding Neuroreport simulation study.
    • performance to exceed 90% with as few as 40 neurons.
    • maximum likelihood decoders controlling a FSM.

____References____

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ref: -2008 tags: OCZ NIA teardown autopsy BMI BCI date: 01-06-2012 03:09 gmt revision:19 [18] [17] [16] [15] [14] [13] [head]

Recently we bought a OCZ NIA device for our lab. Having designed similar hardware myself, I simply *had to* take the thing apart to inspect it, as others have done -- see Joe Pit's teardown (with schematic!!). Of course, I graciously let the others try it for a few hours (it doesn't work all that well) before taking the anodized, extruded, surface- ground aluminum case apart. Below is the top side of the 4-layer circuit board inside the case, as well as a key to indicate the function of the labeled devices. (some of the labels are hard to read due to the clutter of the silkscreen on the board; sorry).

  • A - Input connector. Center channel is isolated ground; outside two channels are the signal. They had to make this custom so people couldn't plug it into other (possibly dangerous) stuff.
  • B - Input current limiting resistors, in series with signal, 4.02K
  • C - Dual capacitor from input channels to shared ground (I think; the cap has 4 contacts, 2 at the end, 2 in the middle; I assume they use this package to get very accurately matched capacitance so as not to hurt the CMRR of the instrumentation amplifier).
  • D - Gain-setting resistor, 1.00K. Sets the instrumentation amplifier gain to 50 (I think).
    • I do not know what devices were intended for the 1206 footprints above and below this resistor...
  • E - Instrumentation amplifier, Analog Devices logo, AD8220 by my guess, A-grade. Measures the difference in voltage between the two input channels (left and right electrodes on the headband).
  • F - 47 ohm resistors & capacitors to filter the power supply to the instrumentation amplifier.
  • H - Opamp, Texas Instruments OPA348A. Looks like it is used as feedback to the instrumentation amplifier reference pin to effect highpass operation (?).
  • I - Quad opamp, TI OPA4348A. Used to filter the signal; I did not go through the filter topology, but they might have copied it off the AD8220 datasheet ;)
  • J - Stereo ADC, Texas Instruments (Burr-Brown logo, TI bought BB) PCM1803A. Only one channel is used. 24 bits, 96khz max sampling rate; device in master mode (Mode1 = 0V, Mode0 = 3.3v); Fs = SCLK/512 -> sampling rate = 3.90625 KHz.
  • K - Three channel digital isolator, Analog Devices ADUM1300. Transmits the ADC's DOUT, BCK, and LRCLK signals to the USB (non-isolated) side.
  • L - Two-channel optical (?) isolator; unknown type; used to drive the ADC's SCLK and some other signal ?
    • from Joe Pits: "Yeah, optical isolator with logic gates for high speed I guess (HCPL2631S). I'm also not sure what the second signal does, it goes to U4 (JSR marking). I suspect it could be a switch which adds C14 + R17 in the feedback loop of U2C (see the schematic). But I don't know what the reason for this is."
  • M - Isolated supply daughterboard, Texas Instruments logo, very simple design: driver is 2 BJTs (which get hot!) in push-pull topology; bases are driven by windings on the toroidal transformer; transformer center tap seems to go to USB VCC. Output is +-5V.
  • N - +3.9V, +3.3, and -3.9V power supply circuitry. I cannot identify the SOT-23-5's and SC-70's here.
  • O - PIC18F2455, with USB 2.0 (obviously!) SOIC-28 package.
    • device comes up as (on my Linux box, Debian Lenny, kernel 2.6.24):
      • usb 4-1: new full speed USB device using uhci_hcd and address 8
      • hiddev96hidraw1: USB HID v1.10 Device [Brain Actuated Technologies Neural Impulse Actuator Prototype 1.0] on usb-0000:00:1a.1-1
    • I'll put up a usbmon trace later, maybe.
  • P - Transistors for driving the tricolor LEDS on the bottom of the board.
  • Q - 16.0000 MHz crystal. Needed for correct USB timing; clocks the PIC at 48Mhz.
  • R - USB type B connector. Note the ferrites to the left. (I though they were fuses, but I accidentally shorted Vdd to ground while probing the programming connector, and these let out a little smoke rather than blowing completely. Had they been fuses, they would be open circuit now. This is consistent with Joe Pit's analysis.)
  • S - 74HCT595A 8-bit shift registers, to convert the serial data into parallel data for the PIC to read in. 3 devices = 24 bits in total.
    • Note that the 74HCT595A has a output enable, which permits the PIC to read the 3 bytes of the sample sequentially. Otherwise, as Stefan Jung (via the openeeg-list) points out, the PIC would not have enough data pins (28 pins vs. 24 bits)!
  • T - 74HCT393, Texas Instruments logo, Dual 4-bit binary ripple counter. Used to drive the ADC with a 2Mhz clock, which puts the sampling rate at (as before) 3.90625 KHz.
  • U - Programming connector. That's right, a programming connector! Looks to be the same as a PIC ICSP connector (pointed out on hack a day)
    • So far as I can tell:
      • Pin 1 = +5V, PIC pin 1, (through 100 ohm resistor), Vpp (?)
      • Pin 2 = PIC pin 20 , Vdd
      • Pin 3 = PIC pin 19 , Vss
      • Pin 4 = PIC pin 28 (through 100 ohm resistor), PGD
      • Pin 5 = PIC pin 27 (through 100 ohm resistor), PGC
    • I do not know if the device can be reprogrammed, though it looks that way.
    • from here - bootloader (to address 0x07ff) can be read, but everything above that is read-protected.
Bottom of board, showing the (very bright!) tricolor LEDs

Comments?

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ref: web-1972 tags: BCI BMI silly groovy date: 01-06-2012 03:07 gmt revision:2 [1] [0] [head]

http://www.ibva.com/Gallery/Gallery.htm

  • since 1972 - groovy!
  • how does this company stay afloat?
  • looks like they have products & software for Mac & PC

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ref: bookmark-2006.07 tags: BMI BCI EEG bibliography Stephan Scott date: 09-07-2008 19:54 gmt revision:2 [1] [0] [head]

http://www.cs.colostate.edu/eeg/links.html

____References____

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ref: Birbaumer-2007.03 tags: BMI operant conditioning review BCI date: 04-09-2007 14:25 gmt revision:0 [head]

PMID-17234696[0] Brain-computer interfaces: communication and restoration of movement in paralysis

  • A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients.
  • attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed (!)
  • We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. I'm not sure if 'contingencies' (something that can happen, but is generally not anticipated); should there not be a strong causal relationship between brain activity and prosthetic control?
  • still, the focus of this article are non-invasive BMIs.

____References____

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ref: Parra-2003.06 tags: BMI BCI EEG error correction ERN date: 0-0-2007 0:0 revision:0 [head]

PMID-12899266 Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring

  • the goal of an adaptive interface is to estimate variables correlated to human performance and adapt the HCI (human computer interface) = BCI accordingly.
    • use specific observable states to judge the subject's cognitive state, and use this information to adapt the BCI & maximize performance.
  • percieved errors are associated with a negative fronto-central deflection in the EEG signal = ERN, error-related negativity.
  • they can detect the ERN using a linear classifier within 100ms on a single-trial basis.
  • also have to remove eyeblink.

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ref: Blankertz-2003.06 tags: BMI BCI EEG error classification motor commands Blankertz date: 0-0-2007 0:0 revision:0 [head]

PMID-12899253 Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

  • want to minimize subject training and maximize the major learning load on the computer.
  • task: predict the laterality of imminent left-right hand finger movements in a natural keyboard typing condition. they got ~15bits/minute (in one subject, ~50bits per minute!)
    • used non-oscilatory signals.
  • did a to detect 85% percent of error trials, and limited false-positives to ~2%