Karim G. Oweiss

Using brain–computer interfaces to induce neural plasticity and restore function

Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.

A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in High-Density Intra-Cortical Implants

This paper describes an area and power-efficient VLSI approach for implementing the discrete wavelet transform on streaming multielectrode neurophysiological data in real time. The VLSI implementation is based on the lifting scheme for wavelet computation using the symmlet4 basis with quantized coefficients and integer fixed-point data precision to minimize hardware demands. The proposed design is driven by the need to compress neural signals recorded with high-density microelectrode arrays implanted in the cortex prior to data telemetry. Our results indicate that signal integrity is not compromised by quantization down to 5-bit filter coefficient and 10-bit data precision at intermediate stages. Furthermore, results from analog simulation and modeling show that a hardware-minimized computational core executing filter steps sequentially is advantageous over the pipeline approach commonly used in DWT implementations. The design is compared to that of a B-spline approach that minimizes the number of multipliers at the expense of increasing the number of adders. The performance demonstrates that in vivo real-time DWT computation is feasible prior to data telemetry, permitting large savings in bandwidth requirements and communication costs given the severe limitations on size, energy consumption and power dissipation of an implantable device.

Identifying functional connectivity in large-scale neural ensemble recordings: A multiscale data mining approach

Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces

This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

Area-Power Efficient VLSI Implementation of Multichannel DWT for Data Compression in Implantable Neuroprosthetics

Time-frequency domain signal processing of neural recordings, from high-density microelectrode arrays implanted in the cortex, is highly desired to ease the bandwidth bottleneck associated with data transfer to extra-cranial processing units. Because of its energy compactness features, discrete wavelet transform (DWT) has been shown to provide efficient data compression for neural records without compromising the information content. This paper describes an area-power minimized hardware implementation of the lifting scheme for multilevel, multichannel DWT with quantized filter coefficients and integer computation. Performance tradeoffs and key design decisions for implantable neuroprosthetics are presented. A 32-channel 4-level version of the circuit has been custom designed in 0.18-mum CMOS and occupies only 0.22 mm2 area and consumes 76 muW of power, making it highly suitable for implantable neural interface applications requiring wireless data transfer.

On the use of dynamic bayesian networks in reconstructing functional neuronal networks from spike train ensembles

Coordination among cortical neurons is believed to be a key element in mediating many high-level cortical processes such as perception, attention, learning, and memory formation. Inferring the structure of the neural circuitry underlying this coordination is important to characterize the highly nonlinear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of dynamic Bayesian networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying nonlinear and time-varying causal interactions between these neurons and can discriminate between mono- and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally Poisson spike train data mimicking spiking activity of cortical networks of small and moderately large size. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks.

Noise reduction in multichannel neural recordings using a new array wavelet denoising algorithm

We investigate a new technique for noise reduction in multichannel neural recordings based on the discrete wavelet transform. Starting with the denoising technique proposed by Donoho et al. (IEEE Trans. Inform. Theory 41 (1995) 613–627), we suggest a new thresholding method for the multiresolution decomposition of the multichannel data. The potential of this technique lies in the fact that thresholds at different resolution levels of the wavelet transform are estimated spatially to account for significant correlation of the wavelet coefficients across channels. The method is applied to a simulated multichannel data as well as real silicon microprobe recordings obtained in our laboratory. Preliminary results show the ability of the technique to reduce both spatially correlated and uncorrelated noise components in the neural recordings. Results are compared to existing techniques and the overall performance is evaluated.

A new technique for blind source separation using subband subspace analysis in correlated multichannel signal environments

We investigated a new framework for the problem of blind source identification in multichannel signal processing. Inspired by a neurophysiological data environment, where an array of closely spaced recording electrodes is surrounded by multiple neural cell sources, significant spatial correlation of source signals motivated the need for an efficient technique for reliable multichannel blind source identification. In a previous work Oweiss and Anderson (see Proceedings of the 34th. Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 2000) adopted a new approach for noise suppression based on thresholding an array discrete wavelet transform (ADWT) representation of the multichannel data. We extend the work of Oweiss and Anderson to identify sources from the observation mixtures. The technique relies on separating sources with highest spatial energy distribution in each frequency subband spanned by the corresponding wavelet basis. Accordingly, the best basis selection criterion we propose benefits from the additional degree of freedom offered by the space domain. The amplitude and shift invariance properties revealed by this technique make it very efficient to track spatial source variations sometimes encountered in multichannel neural recordings. Results from multichannel multiunit neural data are presented and the overall performance is evaluated.

Model-Based Analysis and Control of a Network of Basal Ganglia Spiking Neurons in the Normal and Parkinsonian States

Controlling the spatiotemporal firing pattern of an intricately connected network of neurons through microstimulation is highly desirable in many applications. We investigated in this paper the feasibility of using a model-based approach to the analysis and control of a basal ganglia (BG) network model of Hodgkin-Huxley (HH) spiking neurons through microstimulation. Detailed analysis of this network model suggests that it can reproduce the experimentally observed characteristics of BG neurons under a normal and a pathological Parkinsonian state. A simplified neuronal firing rate model, identified from the detailed HH network model, is shown to capture the essential network dynamics. Mathematical analysis of the simplified model reveals the presence of a systematic relationship between the networks structure and its dynamic response to spatiotemporally patterned microstimulation. We show that both the network synaptic organization and the local mechanism of microstimulation can impose tight constraints on the possible spatiotemporal firing patterns that can be generated by the microstimulated network, which may hinder the effectiveness of microstimulation to achieve a desired objective under certain conditions. Finally, we demonstrate that the feedback control design aided by the mathematical analysis of the simplified model is indeed effective in driving the BG network in the normal and Parskinsonian states to follow a prescribed spatiotemporal firing pattern. We further show that the rhythmic/oscillatory patterns that characterize a dopamine-depleted BG network can be suppressed as a direct consequence of controlling the spatiotemporal pattern of a subpopulation of the output Globus Pallidus internalis (GPi) neurons in the network. This work may provide plausible explanations for the mechanisms underlying the therapeutic effects of deep brain stimulation (DBS) in Parkinsons disease and pave the way towards a model-based, network level analysis and closed-loop control and optimization of DBS parameters, among many other applications.

MASSIT - Multiresolution Analysis of Signal Subspace Invariance Technique: a novel algorithm for blind source separation

We developed (2001) a novel approach for blind source separation in multichannel signal processing environments. The technique, which relies on an inherent invariance property of the signal subspace across multiresolution levels obtained in the wavelet transform domain, showed robustness to amplitude and shift variations encountered in multi-unit neural recording environments. In this work, we extend the previous work to describe in detail a fast implementation of the algorithm and outline the criterion based on which the characterization of each source should be formulated. Results and performance evaluation that were not reported in the previous paper are illustrated in this paper.