Chandra S. Throckmorton

Performance of an adaptive feature-based processor for a wideband ground penetrating radar system

A two-stage algorithm for landmine detection with a ground penetrating radar (GPR) system is described. First, 3-D data sets are processed using a computationally inexpensive pre-screening algorithm which flags potential locations of interest. These flagged locations are then passed to a feature-based processor which further discriminates target-like anomalies from naturally occurring clutter. Current field trial (over 6500 square meters) and blind test results (over 39000 square meters) are presented and these show at least an order of magnitude improvement over other radar system-based detection algorithms on the same test lanes.

The effect of channel interactions on speech recognition in cochlear implant subjects: Predictions from an acoustic model

Acoustic models that produce speech signals with information content similar to that provided to cochlear implant users provide a mechanism by which to investigate the effect of various implant-specific processing or hardware parameters independent of other complicating factors. This study compares speech recognition of normal-hearing subjects listening through normal and impaired acoustic models of cochlear implant speech processors. The channel interactions that were simulated to impair the model were based on psychophysical data measured from cochlear implant subjects and include pitch reversals, indiscriminable electrodes, and forward masking effects. In general, spectral interactions degraded speech recognition more than temporal interactions. These effects were frequency dependent with spectral interactions that affect lower-frequency information causing the greatest decrease in speech recognition, and interactions that affect higher-frequency information having the least impact. The results of this study indicate that channel interactions, quantified psychophysically, affect speech recognition to different degrees. Investigation of the effects that channel interactions have on speech recognition may guide future research whose goal is compensating for psychophysically measured channel interactions in cochlear implant subjects.

Investigating perceptual features of electrode stimulation via a multidimensional scaling paradigm

To achieve the most effective speech processing for individuals with cochlear implants, it is important to understand the perceptual features associated with the stimulation parameters. In general, when electrodes are stimulated in order from apex to base, the pitch of the perceived sound changes in an orderly fashion from low to high. Some deviations from this assumed order have been documented. Also, pitch is the dominant perceptual attribute of a sound when the stimuli associated with different electrodes have been accurately loudness balanced. In this study, the results of a multidimensional scaling (MDS) paradigm were compared to the results of a pitch-ranking procedure for six subjects implanted with multichannel cochlear prostheses. Results indicate that there may be multiple percepts that change with electrode location. Not surprisingly, the dominant percept is strongly correlated with pitch. The results also indicate that the structure of the second percept is consistent across subjects, although not interpretable using the data measured in this study. Furthermore, results indicate that MDS data can be used to pinpoint indiscriminable electrodes more accurately than pitch data. The results of this study may have importance for the design of the next generation of speech processors.

Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers

P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study

OBJECTIVE The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. APPROACH We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a users EEG data. We further enhanced the algorithm by incorporating information about the users language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. MAIN RESULTS Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. SIGNIFICANCE We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

Acoustic model investigation of a multiple carrier frequency algorithm for encoding fine frequency structure: Implications for cochlear implants ☆

Current cochlear implants provide frequency resolution through the number of channels. Improving resolution by increasing channels is limited by factors such as the physiological feasibility of increasing the number of electrodes, the inability to increase the number of channels for those already implanted, and the increased possibility of channel interactions reducing channel efficacy. Recent studies have suggested an alternative method: providing a continuum of pitch percepts for each channel based on the frequency content of that channel. This study seeks to determine the frequency resolution necessary for the highest performance gain, which may give some indication of the feasibility for implementation in implants. A discrete set of carrier frequencies, instead of a continuum, are evaluated using an acoustic model to measure speech recognition. Performance increased as the number of available frequencies increased, and substantive improvement was seen with as few as two frequencies per channel. The effect of variable frequency discrimination was also assessed, and the results suggest that frequency modulation can still provide benefits with poor frequency discrimination on some channels. These results suggest that if two or more discriminable frequencies per channel can be generated for cochlear implant subjects then an improvement in speech recognition may be possible.

The efficacy of human observation for discrimination and feature identification of targets measured by the NIITEK ground-penetrating radar

Recently, blind tests of several automated detection algorithms operating on the NIITEK ground penetrating radar data (GPR) have resulted in quite promising performance results. Anecdotally, human observers have also shown notable skill in detecting landmines and rejecting false alarms in this same data; however, the basis of human performance has not been studied in depth. In this study, human observers are recruited from the undergraduate and graduate student population at Duke University and are trained to visually detect landmines in the NIITEK GPR data. Subjects are then presented with GPR responses associated with blanks, clutter items (including emplaced clutter), and landmines in a blind test scenario. Subjects are asked to make the decision as to whether they are viewing a landmine response or a false alarm, and their performance is scored. A variety of landmines, measured at several test sites, are presented to determine the relative difficulty in detecting each mine type. Subject performance is compared to the performance of two automated algorithms already under development for the NIITEK radar system: LMS and FROSAW. In addition, subjects are given a subset of features for each alarm from which they may indicate the reason behind their decision. These last data may provide a basis for the design of an automated algorithm that takes advantage of the most useful of the observed features.

Utilizing a Language Model to Improve Online Dynamic Data Collection in P300 Spellers

P300 spellers provide a means of communication for individuals with severe physical limitations, especially those with locked-in syndrome, such as amyotrophic lateral sclerosis. However, P300 speller use is still limited by relatively low communication rates due to the multiple data measurements that are required to improve the signal-to-noise ratio of event-related potentials for increased accuracy. Therefore, the amount of data collection has competing effects on accuracy and spelling speed. Adaptively varying the amount of data collection prior to character selection has been shown to improve spelling accuracy and speed. The goal of this study was to optimize a previously developed dynamic stopping algorithm that uses a Bayesian approach to control data collection by incorporating a priori knowledge via a language model. Participants (n = 17) completed online spelling tasks using the dynamic stopping algorithm, with and without a language model. The addition of the language model resulted in improved participant performance from a mean theoretical bit rate of 46.12 bits/min at 88.89% accuracy to 54.42 bits/min (p <; 0.0065) at 90.36% accuracy.

Mandarin Chinese tone identification in cochlear implants: Predictions from acoustic models

It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulation rate and current steering may provide the means of delivering more spectral information and thus may provide the auditory fine-structure required for tone recognition. Several cochlear implant signal processing strategies are examined in this study, the continuous interleaved sampling (CIS) algorithm, the frequency amplitude modulation encoding (FAME) algorithm, and the multiple carrier frequency algorithm (MCFA). These strategies provide different types and amounts of spectral information. Pattern recognition techniques can be applied to data from Mandarin Chinese tone recognition tasks using acoustic models as a means of testing the abilities of these algorithms to transmit the changes in fundamental frequency indicative of the four lexical tones. The ability of processed Mandarin Chinese tones to be correctly classified may predict trends in the effectiveness of different signal processing algorithms in cochlear implants. The proposed techniques can predict trends in performance of the signal processing techniques in quiet conditions but fail to do so in noise.

Algorithms for land mine detection using the NIITEK ground penetrating radar

Ground penetrating radar has been proposed as an alternative sensor to classical electromagnetic induction techniques for the landmine detection problem. The NIITEK-Wichmann antenna provides a high frequency radar signal with very low noise levels following the ground reflection. As a result, the signal from a buried object is not masked by the inherent noise in the system. It has been demonstrated that an operator can learn to interpret the NIITEK-Wichmann radar signal to detect and identify buried targets. The goal of this work is to develop signal processing algorithms to automatically process the radar signals and differentiate between targets and clutter. The algorithms that we are investigating have been tested on data collected at the JUXOCO test grid as well as on data collected in calibration lanes that are used for evaluating the performance of handheld and vehicular landmine detection systems. We have developed algorithms based on principle component analysis, independent component analysis, matched filters, and Bayesian processing of wavelet features. We have also considered several approaches to ground-bounce removal prior to processing. In this paper we discuss the relative performance of each of the techniques as well as the impact of ground bounce removal on processing of the data.