Brain-machine interface using electrocorticography in humans

Paralysis has a severe impact on a patient’s quality of life and entails a high emotional burden and life-long social and financial costs. More than 5 million people in the USA suffer from some form of paralysis and about 50% of the people older than 65 experience difficulties or inabilities with movement. Restoring movement and communication for patients with neurological and motor disorders, stroke and spinal cord injuries remains a challenging clinical problem without an adequate solution. A brain-machine interface (BMI) allows subjects to control a device, such as a computer cursor or an artificial hand, exclusively by their brain activity. BMIs can be used to control communication and prosthetic devices, thereby restoring the communication and movement capabilities of the paralyzed patients. So far, most powerful BMIs have been realized by extracting movement parameters from the activity of single neurons. To record such activity, electrodes have to penetrate the brain tissue, thereby generating risk of brain injury. In addition, recording instability, due to small movements of the electrodes within the brain and the neuronal tissue response to the electrode implant, is also an issue. In this thesis, I investigate whether electrocorticography (ECoG), an alternative recording technique, can be used to achieve BMIs with similar accuracy. First, I demonstrate a BMI based on the approach of extracting movement parameters from ECoG signals. Such ECoG based BMI can further be improved using supervised adaptive algorithms. To implement such algorithms, it is necessary to continuously receive feedback from the subject whether the BMI-decoded trajectory was correct or incorrect. I show that, by using the same ECoG recordings, neuronal responses to trajectory errors can be recorded, detected and differentiated from other types of errors. Finally, I devise a method that could be used to improve the detection of error related neuronal responses

Synthesis and magnetic properties of NiFe_{2-x}Al_{x}O_{4} nanoparticles

Nanocrystalline Al-doped nickel ferrite powders have been synthesized by sol-gel auto-ignition method and the effect of non-magnetic aluminum content on the structural and magnetic properties has been studied. The X-ray diffraction (XRD) revealed that the powders obtained are single phase with inverse spinel structure. The calculated grain sizes from XRD data have been verified using transmission electron microscopy (TEM). TEM photographs show that the powders consist of nanometer-sized grains. It was observed that the characteristic grain size decreases from 29 to 6 nm as the non-magnetic Al content increases, which was attributed to the influence of non-magnetic Al concentration on the grain size. Magnetic hysteresis loops were measured at room temperature with a maximum applied magnetic field of 1T. As aluminum content increases, the measured magnetic hysteresis curves become more and more narrow and the saturation magnetization and remanent magnetization both decreased. The reduction of agnetization compared to bulk is a consequence of spin non-collinearity. Further reduction of magnetization with increase of aluminum content is caused by non-magnetic Al^{3+} ions and weakened interaction between sublattices. This, as well as the decrease in hysteresis was understood in terms of the decrease in particle size.Comment: 24 pages, 6 figure

Simulated mono-phasic (left) and bi-phasic (right) neuronal responses.

<p>Simulated mono-phasic (left) and bi-phasic (right) neuronal responses.</p

Jitter reduction obtained using <i>dTAV</i>-optimized MaxCorr.

<p>Jitter reduction is shown for monophasic (left panels) and biphasic (right panels) simulated neural responses and for jitter distributed according to Gaussian (top panels) and uniform distributions (bottom panels). Different lines show jitter reduction for simulated experiments containing different numbers of trials. MaxCorr algorithm options and parameters were optimized over the following values: maximum allowed correlation time lag Δ<i>λ</i>_<i>MAX</i>: 50ms, 100ms, 200ms, 400ms and 800ms; processing of the cross-correlation coefficients: “linear” and “logarithmic”; normalization of cross-correlation coefficients: “none”, “coefficient” and “unbiased”; number of consecutive iterations of the MaxCorr algorithm: 1 and 3; and the width of the filter window: 100ms, 250ms, 500ms and 1000ms. <i>σ’</i>_<i>J</i> and <i>σ”</i>_<i>J</i>−jitter standard deviation before and after realignment, respectively. All results are averaged over 100 simulation repetitions; error bars depict the standard errors of the mean. For SNR of 1.3 and 2, standard errors are too small to be noticed on the plots.</p

Reliability of <i>dTAV</i> as a measure of jitter reduction for bi-phasic neuronal response.

<p>See caption of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153773#pone.0153773.g003" target="_blank">Fig 3</a> for details.</p

Effect of single trial jitter on the estimation of the underlying neuronal response.

<p>A: Neuronal responses are related to the external event (in this example stimulus; S), but are triggered (R) by an internal process, which is not precisely time-locked to the onset of the event. B: A certain amount of noise is recorded together with the relevant neuronal responses. C: During the experiment, the external event occurs multiple times, while the neuronal activity is recorded. D: When neuronal responses are aligned on the response start, the trial average response (F: blue line) is a good approximation of the real neuronal response. However, the response onset is unknown. The trial-averaged response aligned on the event onset triggers (F: red line) does not correctly reproduce the real neuronal response. In addition, the standard deviation across trials calculated using the event onset triggers (F: blue and red shaded tubes) is an incorrect estimate of the variability of neuronal responses. This example was generated using Gaussian white noise with a standard deviation equal to 5% of the maximum response amplitude (SNR = 20). Differences between the response starts and stimulus onsets were modelled using a Gaussian distribution with a standard deviation equal to 1/7 of the response standard deviation (<i>σ</i>_<i>R</i> = 700ms, <i>σ</i>_<i>J</i> = 100ms).</p

Reliability of <i>dTAV</i> as a measure of jitter reduction for mono-phasic neuronal responses.

<p>Data obtained by simulating 2000 100-trial experiments. A: Expectation of <i>dTAV</i> as a function of the reduction of jitter standard deviation. Lines drawn only for values of SNR of 0.2 and higher. For lower SNR, 2000 repetitions were insufficient to provide a reliable estimate of the expected value of <i>dTAV</i> due to the high noise level. For the shown SNR range, the expected value of <i>dTAV</i> is independent of the SNR. B: The standard deviation (std) of <i>dTAV</i> as a function of the amount of jitter reduction for different SNRs. Standard deviations of <i>dTAV</i> for SNR of 0.2 and lower are above 10⁻⁶ and are, therefore, not shown. C: Probability of jitter reduction as a function of <i>ndTAV</i> for different SNRs. Panels A, B and C are shown for integration time <i>T</i>_<i>I</i> of 300ms. D: Values of jitter reduction and integration times for which the probability of correct <i>dTAV</i> prediction reaches 90%. For jitter reductions and integration times above the line, the probability for correct <i>dTAV</i> prediction, , is above 90%. For SNRs of 0.13 and lower, the probability of correct <i>dTAV</i> prediction never reached 90%.</p

Maximum, median and <i>dTAV</i> optimized jitter reduction obtained by MaxCorr algorithm.

<p>Main panels show maximum (black lines) and median (red lines) of jitter reduction obtained by using all permutations of options and parameter values used for <i>dTAV</i> optimization (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153773#pone.0153773.g006" target="_blank">Fig 6</a> for the list of options and parameter values). Blue lines show reduction of jitter standard deviation obtained using <i>dTAV</i> optimized parameter values. Insets show the percentage of maximum jitter reduction recovered when using <i>dTAV</i> optimization (grey line with black dots); and the percentage of jitter reduction values, obtained when using all option selections and parameter values, that are lower than the jitter reduction obtained using <i>dTAV</i> optimization (purple line with black dots). Both are shown only for SNRs for which the jitter reduction obtained using <i>dTAV</i> optimization was positive. All results are averaged over 100 simulation repetitions; error bars depict the standard errors of the mean. For some SNRs standard errors may be too small to be noticed on the plots.</p