Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

Brain–machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technological limitations, there is a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this paper, we introduce and evaluate a Bayesian maximum-likelihood estimation strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control, utilizing LFPs as well as neurons in cingulate cortex, two non-traditional neural inputs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90824/1/1741-2552_8_4_046009.pd

Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes

We investigated using poly(3,4-ethylenedioxythiophene) (PEDOT) to lower the impedance of small, gold recording electrodes with initial impedances outside of the effective recording range. Smaller electrode sites enable more densely packed arrays, increasing the number of input and output channels to and from the brain. Moreover, smaller electrode sizes promote smaller probe designs; decreasing the dimensions of the implanted probe has been demonstrated to decrease the inherent immune response, a known contributor to the failure of long-term implants. As expected, chronically implanted control electrodes were unable to record well-isolated unit activity, primarily as a result of a dramatically increased noise floor. Conversely, electrodes coated with PEDOT consistently recorded high-quality neural activity, and exhibited a much lower noise floor than controls. These results demonstrate that PEDOT coatings enable electrode designs 15 µm in diameter.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90823/1/1741-2552_8_1_014001.pd

Neuralprosthetic Devices: Inputs and Outputs.

Prior studies have demonstrated that the firing rate of cortical neurons can be volitionally modulated by a subject to generate a controllable output signal; this neural output signal can then be manipulated to direct a robotic arm, a cursor on a computer screen, or other interface device. The burgeoning field of neural control has led to a number of innovative applications, known more commonly as neuroprosthetic devices. Neuroprosthetic devices have the potential to return some degree of functionality to the over 250,000 Americans with incapacitating spinal cord injuries, or allow healthy subjects to control electronic devices in their everyday lives. The research presented here consists of three studies focused on improving the current generation of neuroprosthetic devices. In the first study, we introduced and evaluated a Bayesian maximum-likelihood estimation (bMLE) strategy to identify optimized training data for neuroprosthetic devices. By limiting initial decoding assumptions and training only on relevant neural data, accurate neural-control was possible with as few as two neurons, using minimal training data and no a-priori¬ movement measurements for calibration. Moreover, implanted subjects obtained useful prosthetic control using local field potentials and neurons from cingulate cortex as input. In the second study, we refined a method to electrochemically deposit surfactant-templated ordered poly(3,4-ethylenedioxythiophene) (PEDOT) films on the recording sites of standard “Michigan” probes, and evaluated the in vivo efficacy of these modified sites in recording chronic neural activity. PEDOT sites were found to outperform control sites in terms of signal-to-noise ratio and number of viable unit potentials - thereby improving the quality of neural input sources to the neuroprosthetic device. In the third study, we evaluated a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR was found to drastically outperform standard types of electrical referencing, reducing noise by more than 30 percent. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60 percent more discernible neural units than traditional methods of electrical referencing – again improving the quality of neural input sources to a neuroprosthetic device.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/62216/1/ludwigk_1.pd

Chronic neural recordings using silicon microelectrode arrays electrochemically deposited with a poly(3,4-ethylenedioxythiophene) (PEDOT) filmThis work was supported by the Center for Wireless Integrated Microsystems NSF EEC-9986866 and the Whitaker Foundation.

Conductive polymer coatings can be used to modify traditional electrode recording sites with the intent of improving the long-term performance of cortical microelectrodes. Conductive polymers can drastically decrease recording site impedance, which in turn is hypothesized to reduce thermal noise and signal loss through shunt pathways. Moreover, conductive polymers can be seeded with agents aimed at promoting neural growth toward the recording sites or minimizing the inherent immune response. The end goal of these efforts is to generate an ideal long-term interface between the recording electrode and surrounding tissue. The goal of this study was to refine a method to electrochemically deposit surfactant-templated ordered poly(3,4-ethylenedioxythiophene) (PEDOT) films on the recording sites of standard ‘Michigan’ probes and to evaluate the efficacy of these modified sites in recording chronic neural activity. PEDOT-coated site performance was compared to control sites over a six-week evaluation period in terms of impedance spectroscopy, signal-to-noise ratio, number of viable unit potentials recorded and local field potential recordings. PEDOT sites were found to outperform control sites with respect to signal-to-noise ratio and number of viable unit potentials. The benefit of reduced initial impedance, however, was mitigated by the impedance contribution of typical silicon electrode encapsulation. Coating sites with PEDOT also reduced the amount of low-frequency drift evident in local field potential recordings. These findings indicate that electrode sites electrochemically deposited with PEDOT films are suitable for recording neural activity in vivo for extended periods. This study also provided a unique opportunity to monitor how neural recording characteristics develop over the six weeks following implantation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49188/2/jne6_1_007.pd

Neural Electrodes: Interfacing Conducting Polymer Nanotubes with the Central Nervous System: Chronic Neural Recording using Poly(3,4-ethylenedioxythiophene) Nanotubes (Adv. Mater. 37/2009)

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64335/1/3717_ftp.pd

Naïve coadaptive cortical control

The ability to control a prosthetic device directly from the neocortex has been demonstrated in rats, monkeys and humans. Here we investigate whether neural control can be accomplished in situations where (1) subjects have not received prior motor training to control the device (naïve user) and (2) the neural encoding of movement parameters in the cortex is unknown to the prosthetic device (naïve controller). By adopting a decoding strategy that identifies and focuses on units whose firing rate properties are best suited for control, we show that naïve subjects mutually adapt to learn control of a neural prosthetic system. Six untrained Long-Evans rats, implanted with silicon micro-electrodes in the motor cortex, learned cortical control of an auditory device without prior motor characterization of the recorded neural ensemble. Single- and multi-unit activities were decoded using a Kalman filter to represent an audio ‘cursor’ (90 ms tone pips ranging from 250 Hz to 16 kHz) which subjects controlled to match a given target frequency. After each trial, a novel adaptive algorithm trained the decoding filter based on correlations of the firing patterns with expected cursor movement. Each behavioral session consisted of 100 trials and began with randomized decoding weights. Within 7 ± 1.4 (mean ± SD) sessions, all subjects were able to significantly score above chance (P < 0.05, randomization method) in a fixed target paradigm. Training lasted 24 sessions in which both the behavioral performance and signal to noise ratio of the peri-event histograms increased significantly (P < 0.01, ANOVA). Two rats continued training on a more complex task using a bilateral, two-target control paradigm. Both subjects were able to significantly discriminate the target tones (P < 0.05, Z-test), while one subject demonstrated control above chance (P < 0.05, Z-test) after 12 sessions and continued improvement with many sessions achieving over 90% correct targets. Dynamic analysis of binary trial responses indicated that early learning for this subject occurred during session 6. This study demonstrates that subjects can learn to generate neural control signals that are well suited for use with external devices without prior experience or training.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49184/2/jne5_2_006.pd