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openeeg - http://openeeg.sourceforge.net/
biosig - an open source library for BCI research
brainbay - an open source biosignal project
http://www.shifz.org/brainbay/
Christoph Veigl
Jeremy Wilkerson
OpenEEG
ModularEEG
MonolithEEG
Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis
2 taps/second
15 bits/minute (6..10 bits/minute)
2% false positives
Single-trial EEG source reconstruction for Brain-Computer Interface
effective bit rate
artifact removal
feature extraction
classification
increase the number of choices [8]
For example, a change in frequency band power can be tracked using autoregressive modelling [9], wavelet transform [10]-[12], or pseudotaper method [13]. For example, common spatial pattern (CSP) [14] or common subspace decomposition (CSSD) [15] techniques can extract new time series from the signal that contain more discriminative information.
[8] G. Dornhege, B. Blankertz, G. Curio, and K.-R. Muller, "Boosting bit rates in noninvasive EEG single-trial classifications by features combination and multiclass paradigms" (2004)
EEG "motor imagery" classification
Hand movement direction decoded from MEG and EEG
Brain activity can be used as a control signal for brain–machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for ≤7 Hz (low-frequency band) and 62–87 Hz (high-{gamma} band) and a decrease for 10–30 Hz (β band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the β and high-{gamma} bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.
EEG-based two-dimensional cursor control
A high-performance Brain Computer Interface based on visual attention
120 bits/min
Brain-computer interface in paralysis
in healthy individuals and patients with amyotrophic lateral sclerosis or stroke can transmit up to 80 bits/min of
bits/min
bits/decision
EEG "bits/min"
EEG "touch typing"
interkeypress intervals (IKI)
"skilled typists"
"average typing speed"
"finger tapping speed"
bits/min commands
2 4
4 16
8 256
16 65536
32 4294967296
64 18446744073709551616
Altered Finger Representations in Sensorimotor Cortex of Musicians with Focal Dystonia: Precentral Cortex
Abstract Using functional magnetic resonance imaging (fMRI), finger representations were characterized in the precentral cortex of 11 normal musicians and 14 musicians with focal task-specific dystonia. Finger representations were identified from differential activation during repetitive movements of each finger relative to others. Despite group similarities in topography, abnormalities in representations of affected fingers were identified. For the finger showing chronic flexion (primary dystonic finger or PDF), the cortical “disparity†from its normal location and the distance to the adjacent finger were increased. By contrast, representational characteristics of the finger showing chronic extension (primary compensatory finger or PCF) did not differ significantly from the control group, but did differ from those of the PDF. Regardless of whether either finger's representation differed substantively from normal, the PCF consistently showed greater volume of activation than the PDF or other fingers. These findings reflect dysfunctional interactions between at least two fingers and their cortical representations.
What's new in neuroimaging methods? (2009)
http://www.ncbi.nlm.nih.gov.ezproxy.lib.utexas.edu/pmc/articles/PMC2716071/
The rapid advancement of neuroimaging methodology and availability has transformed neuroscience research. The answers to many questions that we ask about how the brain is organized depend on the quality of data that we are able to obtain about the locations, dynamics, fluctuations, magnitudes, and types of brain activity and structural changes. In this review, an attempt is made to take a snapshot of the cutting edge of a small component of the very rapidly evolving field of neuroimaging. For each area covered, a brief context is provided along with a summary of a few of the current developments and issues. Then, several outstanding papers, published in the past year or so, are described, providing an example of the directions in which each area is progressing. The areas covered include functional MRI (fMRI), voxel based morphometry (VBM), diffusion tensor imaging (DTI), electroencephalography (EEG), magnetoencephalography (MEG), optical imaging, and positron emission tomography (PET). More detail is included on fMRI, as subsections include: functional MRI interpretation, new functional MRI contrasts, MRI technology, MRI paradigms and processing, and endogenous oscillations in functional MRI.
Probabilistic methods to predict muscle activity
functional electrical stimulation (FES)
http://www.iop.org.ezproxy.lib.utexas.edu/EJ/abstract/1741-2552/6/5/055008
Evaluation of probabilistic methods to predict muscle activity: implications for neuroprosthetics
Abstract. Functional electrical stimulation (FES) involves artificial activation of muscles with surface or implanted electrodes to restore motor function in paralyzed individuals. Currently, FES-based prostheses produce only a limited range of movements due to the difficulty associated with identifying patterns of muscle activity needed to evoke more complex behaviour. Here we test three probability-based models (Bayesian density estimation, polynomial curve fitting and dynamic neural network) that use the trajectory of the hand to predict the electromyographic (EMG) activities of 12 arm muscles during complex two- and three-dimensional movements. Across most conditions, the neural network model yielded the best predictions of muscle activity. For three-dimensional movements, the predicted patterns of muscle activity using the neural network accounted for 40% of the variance in the actual EMG signals and were associated with an average root-mean-squared error of 6%. These results suggest that such probabilistic models could be used effectively to predict patterns of muscle stimulation needed to produce complex movements with an FES-based neuroprosthetic.
Decoding flexion of individual fingers using electrocorticographic signals in humans
Abstract. Brain signals can provide the basis for a non-muscular communication and control system, a brain–computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.
A high-performance brain computer interface (2006)
EEG BCI is still slower than eye movements (for typing)
invasive electrodes also still slow
monkey dorsal premotor cortex
96 electrodes (cyberkinetics system)
6.5 bits/sec or 15 wpm
wpm cpm cps bits per minute
10 wpm 50 cpm 0.83 cps 350
20 wpm 100 cpm 1.66 cps 700
40 wpm 200 cpm 3.33 cps 1400
80 wpm 400 cpm 6.66 cps 2800
100 wpm 500 cpm 8.33 cps 3500
110 wpm 550 cpm 9.16 cps 3850
120 wpm 600 cpm 10.00 cps 4200
150 wpm 750 cpm 12.50 cps 5250
200 wpm 1000 cpm 16.66 cps 7000
500 wpm
1k wpm 5000 cpm 83 cps 35000
10k wpm 50k cpm 833 cps 350000
key selection system
neuroenhancement
cosmetic neurology
the working principles of neuronal networks and their function. For example, with a
technique they call “linear decoding,†researchers at the University California,
Berkeley, were able to reconstruct actual moving images from electrophysiological
recordings by means of electrodes placed in a cat's lateral geniculate ganglion, a
neural structure connected to the cat's optic system [12]. They have also shown that it
is possible to map non-linear neuronal responses to visual stimuli in the visual
cortex.
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2010-01-10
16:02 <thesnark> I don't think typing with an EEG is realistic right now
16:03 <thesnark> the physical extension of the VR stuff would be replacement or
extra limbs
16:03 <thesnark> controlling things by thought
16:03 <thesnark> which _is_ doable
16:07 <thesnark> it would make more sense to me to take a ML approach - certain
arbitrary patterns start as "words" and the user corrects the
computer if it makes an error
16:09 <kanzure> also it would be fucking awesome to say "hey look this gives me
a 50 wpm increase"
16:09 <kanzure> i type at 120wpm on a good day, and 150wpm on a great day
16:09 <kanzure> but hitting upper limits of human performance is not fun
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motor imagery as a way to get information out of the brain
http://wiki.seedea.org/Content/Concepttreefmri
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