The full description of this project, with its approach, findings and figures, is on the Suhail Lab website.
Key papers: (Suhail & Oweiss, 2004; Oweiss et al., 2007; Thomson et al., 2005; Thomson et al., 2006; Suhail & Oweiss, 2006; Jin et al., 2006; Oweiss et al., 2007; Suhail & Oweiss, 2006).
References
2007
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A scalable wavelet transform VLSI architecture for real-time signal processing in high-density intra-cortical implants
Karim G. Oweiss, Andrew Mason, Yasir Suhail, Awais M. Kamboh, and Kyle E. Thomson
IEEE Transactions on Circuits and Systems I: Regular Papers, 2007
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. © 2007 IEEE.
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Identifying neuronal assemblies with local and global connectivity with scale space spectral clustering
Karim Oweiss, Rong Jin, and Yasir Suhail
Neurocomputing, 2007
A nonparametric approach is proposed to identify clusters of functionally interdependent neurons, independent of the time scale at which they are maximally correlated. The neural point processes are represented in a N-dimensional scale space using the Haar wavelet transform. A similarity measure between any given pair of neurons is defined in the scale space. Clusters of "similar" neurons are identified by first reducing the N-dimensional scale space representation using principal components to obtain a Q-dimensional space. The weighted principal components are subsequently used to connect each neuron to the others in a graph representation. A probabilistic spectral clustering algorithm is used to perform graph partitioning by maximizing cluster compactness. Performance is compared to that of the k-means and the expectation-maximization algorithms for 120 neurons with time-varying intensity functions consisting of spontaneous background activity and phased response elicited at distinct time scales. © 2006 Elsevier B.V. All rights reserved.
2006
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B-Spline vs. Lifting architecture for neuroprosthetic devices
Kyle E. Thomson, Theo Shlien, Yasir Suhail, Andrew Mason, and Karim G. Oweiss
In 2006 IEEE International Conference on Electro Information Technology, 2006
Advances in high-density microelectrode arrays has created a need for high level signal processing to cope with the immense data throughput. The Discrete Wavelet Transform (DWT) has been shown to optimally reduce the amount of data throughput, while concisely preserving the information in the data. Two factorizations, Lifting and B-spline, have been proposed for implementing the DWT in hardware. A comparison based on critical path, memory requirements, and computational hardware is drawn. The context of this comparison focuses on multichannel and multiresolution neuroprosthetic devices.
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Multiresolution Bayesian detection of multiunit extracellular spike waveforms in multichannel neuronal recordings
Yasir Suhail, and Karim Oweiss
In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2006
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A mixture model for spike train ensemble analysis using spectral clustering
Rong Jin, Yasir Suhail, and Karim Oweiss
In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2006
Identifying clusters of neurons with correlated spiking activity in large-size neuronal ensembles recorded with highdensity multielectrode array is an emerging problem in computational neuroscience. We propose a nonparametric approach that represents multiple neural spike trains by a mixed point process model. A spectral clustering algorithm is applied to identify the clusters of neurons through their correlated firing activities. The advantage of the proposed technique is its ability to efficiently identify large populations of neurons with correlated spiking activity independent of the temporal scale. We report the clustering performance of the algorithm applied to a complex synthesized data set and compare it to multiple clustering techniques. © 2006 IEEE.
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Augmenting information channels in hearing aids and cochlear implants under adverse conditions
Yasir Suhail, and Karim G. Oweiss
In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2006
We conceptualize a new signal processing strategy to better represent the temporal and spectral cues in speech signals for Hearing Aid (HA) and Cochlear Implant (CI) applications under severe adverse conditions. The proposed approach rests on two well studied methods for signal separation and noise suppression, namely, the denoising and function approximation capabilities of the wavelet transform, blended with signal subspace decomposition through low rank approximation. The technique targets suppression of "competing voice" type noises. A cost function is defined to obtain a "best basis" representation of the desired speech signal for which an inherent invariance property of the signal subspace is observed. This allows better separation of the speech-like noise in contrast to classical bandpass filtering currently employed in CI and HA devices. We demonstrate the efficiency of the proposed method in capturing the rapid dynamics of speech signals, while minimizing the masking effects of noise, in addition to improved recognition rates in normal hearing listeners. The technique remains to be tested on actual patients. © 2006 IEEE.
2005
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A scalable architecture for streaming neural information from implantable multichannel neuroprosthetic devices
Kyle E. Thomson, Yasir Suhail, and Karim G. Oweiss
In Proceedings - IEEE International Symposium on Circuits and Systems, 2005
Two hardware architectures for implementing lifting-based discrete wavelet transform (DWT) suitable for implantable, real-time operation of high-density sensor array neuroprosthetic devices. A core computational node (CN) is designed for use in both architectures to yield maximum processor usage. The first uses multiple pipelined replicas of the CN, requiring fewer clock cycles. The second architecture reuses a single CN, thus requires less chip area but longer time delay. By utilizing the difference between the data sampling rate and available computation bandwidth, the novelty of both designs lies in the scalability to an arbitrary number of channels by interleaving the DWT computation without affecting the real-time operability. Performance comparison and overall considerations of both designs are presented in details. © 2005 IEEE.
2004
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A reduced complexity integer lifting wavelet-based module for real-time processing in implantable neural interface devices
Yasir Suhail, and Karim Oweiss
In 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004
