Independent component analysis and source analysis of auditory evoked potentials for assessment of cochlear implant users

Source analysis of the Auditory Evoked Potential (AEP) has been used before to evaluate the maturation of the auditory system in both adult and children; in the same way, this technique could be applied to ongoing EEG recordings, in response to acoustic specific frequency stimuli, from children with cochlear implants (CI). This is done in oder to objectively assess the performance of this electronic device and the maturation of the child?s hearing. However, these recordings are contaminated by an artifact produced by the normal operation of the CI; this artifact in particular makes the detection and analysis of AEPs much harder and generates errors in the source analysis process. The artifact can be spatially filtered using Independent Component Analysis (ICA); in this research, three different ICA algorithms were compared in order to establish the more suited algorithm to remove the CI artifact. Additionally, we show that pre-processing the EEG recording, using a temporal ICA algorithm, facilitates not only the identification of the AEP peaks but also the source analysis procedure. From results obtained in this research and limited dataset of CI vs normal recordings, it is possible to conclude that the AEPs source locations change from the inferior temporal areas in the first 2 years after implantation to the superior temporal area after three years using the CIs, close to the locations obtained in normal hearing children. It is intended that the results of this research are used as an objective technique for a general evaluation of the performance of children with CIs

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Using novel stimuli and alternative signal processing techniques to enhance BCI paradigms

A Brain-Computer Interface (BCI) is a device that uses the brain activity of a person as an input to select desired outputs on a computer. BCIs that use surface electroencephalogram (EEG) recordings as their input are the least invasive but also suffer from a very low signal-to-noise ratio (SNR) due to the very low amplitude of the person’s brain activity and the presence of many signal artefacts and background noise. This can be compensated for by subjecting the signals to extensive signal processing, and by using stimuli to trigger a large but consistent change in the signal – these changes are called evoked potentials. The method used to stimulate the evoked potential, and introduce an element of conscious selection in order to allow the user’s intent to modify the evoked potential produced, is called the BCI paradigm. However, even with these additions the performance of BCIs used for assistive communication and control is still significantly below that of other assistive solutions, such as keypads or eye-tracking devices. This thesis examines the paradigm and signal processing components of BCIs and puts forward several methods meant to enhance BCIs’ performance and efficiency. Firstly, two novel signal processing methods based on Empirical Mode Decomposition (EMD) were developed and evaluated. EMD is a technique that divides any oscillating signal into groups of frequency harmonics, called Intrinsic Mode Functions (IMFs). Furthermore, by using Takens’ theorem, a single channel of EEG can be converted into a multi-temporal channel signal by transforming the channel into multiple snapshots of its signal content in time using a series of delay vectors. This signal can then be decomposed into IMFs using a multi-channel variation of EMD, called Multi-variate EMD (MEMD), which uses the spatial information from the signal’s neighbouring channels to inform its decomposition. In the case of a multi-temporal channel signal, this allows the temporal dynamics of the signal to be incorporated into the IMFs. This is called Temporal MEMD (T-MEMD). The second signal processing method based on EMD decomposed both the spatial and temporal channels simultaneously, allowing both spatial and temporal dynamics to be incorporated into the resulting IMFs. This is called Spatio-temporal MEMD (ST-MEMD). Both methods were applied to a large pre-recorded Motor Imagery BCI dataset along with EMD and MEMD for comparison. These results were also compared to those from other studies in the literature that had used the same dataset. T-MEMD performed with an average classification accuracy of 70.2%, performing on a par with EMD that had an average classification accuracy of 68.9%. Both ST-MEMD and MEMD outperformed them with ST-MEMD having an average classification accuracy of 73.6%, and MEMD having an average classification accuracy of 75.3%. The methods containing spatial dynamics, i.e. MEMD and ST-MEMD, outperformed those with only temporal dynamics, i.e. EMD and T-MEMD. The two methods with temporal dynamics each performed on a par with the non-temporal method that had the same level of spatial dynamics. This shows that only the presence of spatial dynamics resulted in a performance increase. This was concluded to be because the differences between the classes of motor-imagery are inherently spatial in nature, not temporal. Next a novel BCI paradigm was developed based on the standard Steady-state Somatosensory Evoked Potential (SSSEP) BCI paradigm. This paradigm uses a tactile stimulus applied to the skin at a certain frequency, generating a resonance signal in the brain’s activity. If two stimuli of different frequency are applied, two resonance signals will be present. However, if the user attends one stimulus over the other, its corresponding SSSEP will increase in amplitude. Unfortunately these changes in amplitude can be very minute. To counter this, a stimulus amplitude and frequency of the vibrotactile stimuli. It was hypothesised that if the stimuli generator was constructed that could alter the were of the same frequency, but one’s amplitude was just below the user’s conscious level of perception and the other was above it, the changes in the SSSEP between classes would be the same as those between an SSSEP being generated and neutral EEG, with differences in α activity between the low-amplitude SSSEP and neutral activity due to the differences in the user’s level of concentration from attending the low-amplitude stimulus. The novel SSSEP BCI paradigm performed on a par with the standard paradigm with an average 61.8% classification accuracy over 16 participants, compared to an average 63.3% classification accuracy respectively, indicating that the hypothesis was false. However, the large presence of electro-magnetic interference (EMI) in the EEG recordings may have compromised the data. Many different noise suppression methods were applied to the stimulus device and the data, and whilst the EMI artefacts were reduced in magnitude they were not eliminated completely. Even with the noise the standard SSSEP stimulus paradigm performed on a par with studies that used the same paradigm, indicating that the results may not have been invalidated by the EMI. Overall the thesis shows that motor-imagery signals are inherently spatial in difference, and that the novel methods of T-MEMD and ST-MEMD may yet out-perform the existing methods of EMD and MEMD if applied to signals that are temporal in nature, such as functional Magnetic Resonance Imaging (fMRI). Whilst the novel SSSEP paradigm did not result in an increase in performance, it highlighted the impact of EMI from stimulus equipment on EEG recordings and potentially confirmed that the amplitude of SSEP stimuli is a minor factor in a BCI paradigm

Refined electrophysiological recording and processing of neural signals from the retina and ascending visual pathways

The purpose of this thesis was the development of refined methods for recording and processing of neural signals of the retina and ascending visual pathways. The first chapter describes briefly the fundamentals of the human visual system and the basics of the functional testing of the retina and the visual pathways. The second and third chapters are dedicated to the processing of visual electrophysiological data using the newly developed software ERG Explorer, and present a proposal for an open and standardized data format, ElVisML, for future proof storage of visual electrophysiological data. The fourth chapter describes the development and application of two novel electrodes: First a contact lens electrode for the recording of electrical potentials of the ciliary muscle during accommodation, and second, the marble electrode, which is made of a super-absorbant polymer and allows for a preparation-free recording of visual evoked potentials. Results obtained in studies using the both electrodes are presented. The fifths and last chapter of the thesis presents the results from four studies within the field of visual electrophysiology. The first study examines the ophthalmological assessment of cannabis-induced perception disorder using electrophysiological methods. The second study presents a refined method for the objective assessment of the visual acuity using visual evoked potentials and introduces therefore, a refined stimulus paradigm and a novel method for the analysis of the sweep VEP. The third study presents the results of a newly developed stimulus design for full-field electrophysiology, which allows to assess previously non-recordable electroretinograms. The last study describes a relation of the spatial frequency of a visual stimulus to the amplitudes of visual evoked potentials in comparison to the BOLD response obtained using functional near-infrared spectroscopy and functional magnetic resonance imaging

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Collective analog bioelectronic computation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 677-710).In this thesis, I present two examples of fast-and-highly-parallel analog computation inspired by architectures in biology. The first example, an RF cochlea, maps the partial differential equations that describe fluid-membrane-hair-cell wave propagation in the biological cochlea to an equivalent inductor-capacitor-transistor integrated circuit. It allows ultra-broadband spectrum analysis of RF signals to be performed in a rapid low-power fashion, thus enabling applications for universal or software radio. The second example exploits detailed similarities between the equations that describe chemical-reaction dynamics and the equations that describe subthreshold current flow in transistors to create fast-and-highly-parallel integrated-circuit models of protein-protein and gene-protein networks inside a cell. Due to a natural mapping between the Poisson statistics of molecular flows in a chemical reaction and Poisson statistics of electronic current flow in a transistor, stochastic effects are automatically incorporated into the circuit architecture, allowing highly computationally intensive stochastic simulations of large-scale biochemical reaction networks to be performed rapidly. I show that the exponentially tapered transmission-line architecture of the mammalian cochlea performs constant-fractional-bandwidth spectrum analysis with O(N) expenditure of both analysis time and hardware, where N is the number of analyzed frequency bins. This is the best known performance of any spectrum-analysis architecture, including the constant-resolution Fast Fourier Transform (FFT), which scales as O(N logN), or a constant-fractional-bandwidth filterbank, which scales as O (N2).(cont.) The RF cochlea uses this bio-inspired architecture to perform real-time, on-chip spectrum analysis at radio frequencies. I demonstrate two cochlea chips, implemented in standard 0.13m CMOS technology, that decompose the RF spectrum from 600MHz to 8GHz into 50 log-spaced channels, consume < 300mW of power, and possess 70dB of dynamic range. The real-time spectrum analysis capabilities of my chips make them uniquely suitable for ultra-broadband universal or software radio receivers of the future. I show that the protein-protein and gene-protein chips that I have built are particularly suitable for simulation, parameter discovery and sensitivity analysis of interaction networks in cell biology, such as signaling, metabolic, and gene regulation pathways. Importantly, the chips carry out massively parallel computations, resulting in simulation times that are independent of model complexity, i.e., O(1). They also automatically model stochastic effects, which are of importance in many biological systems, but are numerically stiff and simulate slowly on digital computers. Currently, non-fundamental data-acquisition limitations show that my proof-of-concept chips simulate small-scale biochemical reaction networks at least 100 times faster than modern desktop machines. It should be possible to get 103 to 106 simulation speedups of genome-scale and organ-scale intracellular and extracellular biochemical reaction networks with improved versions of my chips. Such chips could be important both as analysis tools in systems biology and design tools in synthetic biology.by Soumyajit Mandal.Ph.D