Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations.

This manuscript describes tests and results of a study to evaluate classification algorithms derived from accelerometer data collected on healthy adults and older adults to better classify posture movements. Specifically, tests were conducted to 1) compare performance of 1 sensor vs. 2 sensors; 2) examine custom trained algorithms to classify for a given task 3) determine overall classifier accuracy for healthy adults under 55 and older adults (55 or older). Despite the current variety of commercially available platforms, sensors, and analysis software, many do not provide the data granularity needed to characterize all stages of movement. Additionally, some clinicians have expressed concerns regarding validity of analysis on specialized populations, such as hospitalized older adults. Accurate classification of movement data is important in a clinical setting as more hospital systems are using sensors to help with clinical decision making. We developed custom software and classification algorithms to identify laying, reclining, sitting, standing, and walking. Our algorithm accuracy is 93.2% for healthy adults under 55 and 95% for healthy older adults over 55 for the tasks in our setting. The high accuracy of this approach will aid future investigation into classifying movement in hospitalized older adults. Results from these tests also indicate that researchers and clinicians need to be aware of sensor body position in relation to where the algorithm used was trained. Additionally, results suggest more research is needed to determine if algorithms trained on one population can accurately be used to classify data from another population.

USING MACHINE LEARNING TO MITIGATE THE EFFECTS OF REVERBERATION AND NOISE IN COCHLEAR IMPLANTS.

In listening environments with room reverberation and background noise, cochlear implant (CI) users experience substantial difficulties in understanding speech. Because everyday environments have different combinations of reverberation and noise, there is a need to develop algorithms that can mitigate both effects to improve speech intelligibility. Desmond et al. (2014) developed a machine learning approach to mitigate the adverse effects of late reverberant reflections of speech signals by using a classifier to detect and remove affected segments in CI pulse trains. This study aimed to investigate the robustness of the reverberation mitigation algorithm in environments with both reverberation and noise. Sentence recognition tests were conducted in normal hearing listeners using vocoded speech with unmitigated and mitigated reverberant-only or noisy reverberant speech signals, across different reverberation times and noise types. Improvements in speech intelligibility were observed in mitigated reverberant-only conditions. However, mixed results were obtained in the mitigated noisy reverberant conditions as a reduction in speech intelligibility was observed for noise types whose spectra were similar to that of anechoic speech. Based on these results, the focus of future work is to develop a context-dependent approach that activates different mitigation strategies for different acoustic environments.

Evaluating Brain-Computer Interface Performance in an ALS Population: Checkerboard and Color Paradigms.

The objective of this study was to investigate the performance of 3 brain-computer interface (BCI) paradigms in an amyotrophic lateral sclerosis (ALS) population (n = 11). Using a repeated-measures design, participants completed 3 BCI conditions: row/column (RCW), checkerboard (CBW), and gray-to-color (CBC). Based on previous studies, it is hypothesized that the CBC and CBW conditions will result in higher accuracy, information transfer rate, waveform amplitude, and user preference over the RCW condition. An offline dynamic stopping simulation will also increase information transfer rate. Higher mean accuracy was observed in the CBC condition (89.7%), followed by the CBW (84.3%) condition, and lowest in the RCW condition (78.7%); however, these differences did not reach statistical significance ( P = .062). Eight of the eleven participants preferred the CBC and the remaining three preferred the CBW conditions. The offline dynamic stopping simulation significantly increased information transfer rate ( P = .005) and decreased accuracy ( P < .000). The findings of this study suggest that color stimuli provide a modest improvement in performance and that participants prefer color stimuli over monochromatic stimuli. Given these findings, BCI paradigms that use color stimuli should be considered for individuals who have ALS.

Fusion of P300 and eye-tracker data for spelling using BCI2000.

OBJECTIVE: Various augmentative and alternative communication (AAC) devices have been developed in order to aid communication for individuals with communication disorders. Recently, there has been interest in combining EEG data and eye-gaze data with the goal of developing a hybrid (or 'fused') BCI (hBCI) AAC system. This work explores the effectiveness of a speller that fuses data from an eye-tracker and the P300 speller in order to create a hybrid P300 speller. APPROACH: This hybrid speller collects both eye-tracking and EEG data in parallel, and the user spells characters on the screen in the same way that they would if they were only using the P300 speller. Online and offline experiments were performed. The online experiments measured the performance of the speller for sixteen non-disabled participants, while the offline simulations were used to assess the robustness of the hybrid system. MAIN RESULTS: Online results showed that for fifteen non-disabled participants, using eye-gaze in a Bayesian framework with EEG data from the P300 speller improved accuracy ([Formula: see text], [Formula: see text], [Formula: see text] for estimated, medium and high variance configurations) and reduced the average number of flashes required to spell a character compared to the standard P300 speller that relies solely on EEG data ([Formula: see text], [Formula: see text], [Formula: see text] for estimated, medium and high variance configurations). Offline simulations indicate that the system provides more robust performance than a standalone eye gaze system. SIGNIFICANCE: The results of this work on non-disabled participants shows the potential efficacy of hybrid P300 and eye-tracker speller. Further validation on the amyotrophic lateral sceloris population is needed to assess the benefit of this hybrid system.

The impact of time on seizure prediction performance in the FSPEEG database.

We demonstrate evidence that high discriminability between preictal and interictal intracranial electroencephalogram (iEEG) recordings [1,2] of the Freiburg database (FSPEEG) may be due to the amount of time that occurred between recordings, as opposed to the underlying seizure state, i.e., preictal or interictal. After replicating published classification methods and results, we performed two experiments. In the first experiment, almost perfect discriminability between discontinuous interictal recordings and almost perfect discriminability between discontinuous preictal recordings were observed as the amount of time between recordings increased. Further, a second experiment demonstrated that the classification performance for patients with large time gaps between preictal and interictal recordings was noticeably higher than the classification performance for patients with contiguous preictal and interictal files. These results provide evidence that time likely plays a major role in the discriminability of the iEEG features considered in this study, regardless of the underlying seizure state. Feature nonstationarity is present and may, under certain conditions, lead to overestimation or underestimation of the probability of seizure occurrence.

Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers.

P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies ( > 70%) to account for error revisions. Error-related potentials (ErrP), which are changes in EEG potentials when a person is aware of or perceives erroneous behavior or feedback, have been proposed as inputs to drive corrective mechanisms that veto erroneous actions by BCI systems. The goal of this study is to demonstrate that training an additional ErrP classifier for a P300 speller is not necessary, as we hypothesize that error information is encoded in the P300 classifier responses used for character selection. We perform offline simulations of P300 spelling to compare ErrP and non-ErrP based corrective algorithms. A simple dictionary correction based on string matching and word frequency significantly improved accuracy (35-185%), in contrast to an ErrP-based method that flagged, deleted and replaced erroneous characters (-47-0%) . Providing additional information about the likelihood of characters to a dictionary-based correction further improves accuracy. Our Bayesian dictionary-based correction algorithm that utilizes P300 classifier confidences performed comparably (44-416%) to an oracle ErrP dictionary-based method that assumed perfect ErrP classification (43-433%).

Projected accuracy metric for the P300 Speller.

The P300 Speller brain-computer interface (BCI) is a virtual keyboard that allows users to type without requiring neuromuscular control. P300 Speller research commonly aims to improve the system accuracy, which is typically estimated by spelling a small number of characters and calculating the percent spelled correctly. In this paper we introduce a new method for estimating the long-term ("projected") accuracy, which utilizes all available flash data and a probabilistic model of the Speller system to produce an estimate with lower variance and lower granularity than the standard measure. We apply the new method to 110 previously-collected P300 Speller runs to confirm its consistency, and simulate spelling runs from real subject data to demonstrate lower variance on the accuracy estimate for any given amount of data.

The effects of reverberant self- and overlap-masking on speech recognition in cochlear implant listeners.

Many cochlear implant (CI) listeners experience decreased speech recognition in reverberant environments [Kokkinakis et al., J. Acoust. Soc. Am. 129(5), 3221-3232 (2011)], which may be caused by a combination of self- and overlap-masking [Bolt and MacDonald, J. Acoust. Soc. Am. 21(6), 577-580 (1949)]. Determining the extent to which these effects decrease speech recognition for CI listeners may influence reverberation mitigation algorithms. This study compared speech recognition with ideal self-masking mitigation, with ideal overlap-masking mitigation, and with no mitigation. Under these conditions, mitigating either self- or overlap-masking resulted in significant improvements in speech recognition for both normal hearing subjects utilizing an acoustic model and for CI listeners using their own devices.

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 ( ) at 90.36% accuracy.

Using channel-specific statistical models to detect reverberation in cochlear implant stimuli.

Reverberation is especially detrimental for cochlear implant listeners; thus, mitigating its effects has the potential to provide significant improvements to cochlear implant communication. Efforts to model and correct for reverberation in acoustic listening scenarios can be quite complex, requiring estimation of the room transfer function and localization of the source and receiver. However, due to the limited resolution associated with cochlear implant stimulation, simpler processing for reverberation detection and mitigation may be possible for cochlear implants. This study models speech stimuli in a cochlear implant on a per-channel basis both in quiet and in reverberation, and assesses the efficacy of these models for detecting the presence of reverberation. This study was able to successfully detect reverberation in cochlear implant pulse trains, and the results appear to be robust to varying room conditions and cochlear implant stimulation parameters. Reverberant signals were detected 100% of the time for a long reverberation time of 1.2 s and 86% of the time for a shorter reverberation time of 0.5 s.

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.

Stream segregation on a single electrode as a function of pulse rate in cochlear implant listeners.

While cochlear implants (CIs) usually provide high levels of speech recognition in quiet, speech recognition in noise remains challenging. To overcome these difficulties, it is important to understand how implanted listeners separate a target signal from interferers. Stream segregation has been studied extensively in both normal and electric hearing, as a function of place of stimulation. However, the effects of pulse rate, independent of place, on the perceptual grouping of sequential sounds in electric hearing have not yet been investigated. A rhythm detection task was used to measure stream segregation. The results of this study suggest that while CI listeners can segregate streams based on differences in pulse rate alone, the amount of stream segregation observed decreases as the base pulse rate increases. Further investigation of the perceptual dimensions encoded by the pulse rate and the effect of sequential presentation of different stimulation rates on perception could be beneficial for the future development of speech processing strategies for CIs.

Investigating the effects of stimulus duration and context on pitch perception by cochlear implant users.

Cochlear implant sound processing strategies that use time-varying pulse rates to transmit fine structure information are one proposed method for improving the spectral representation of a sound with the eventual goal of improving speech recognition in noisy conditions, speech recognition in tonal languages, and music identification and appreciation. However, many of the perceptual phenomena associated with time-varying rates are not well understood. In this study, the effects of stimulus duration on both the place and rate-pitch percepts were investigated via psychophysical experiments. Four Nucleus CI24 cochlear implant users participated in these experiments, which included a short-duration pitch ranking task and three adaptive pulse rate discrimination tasks. When duration was fixed from trial-to-trial and rate was varied adaptively, results suggested that both the place-pitch and rate-pitch percepts may be independent of duration for durations above 10 and 20 ms, respectively. When duration was varied and pulse rates were fixed, performance was highly variable within and across subjects. Implications for multi-rate sound processing strategies are discussed.

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.

Assessing the pitch structure associated with multiple rates and places for cochlear implant users.

Cochlear implant subjects continue to experience difficulty understanding speech in noise and performing pitch-based musical tasks. Acoustic model studies have suggested that transmitting additional fine structure via multiple stimulation rates is a potential mechanism for addressing these issues [Nie et al., IEEE Trans. Biomed. Eng. 52, 64-73 (2005); Throckmorton et al., Hear. Res. 218, 30-42 (2006)]; however, results from preliminary cochlear implant studies have been less compelling. Multirate speech processing algorithms previously assumed a place-dependent pitch structure in that a basal electrode would always elicit a higher pitch percept than an apical electrode, independent of stimulation rate. Some subjective evidence contradicts this assumption [H. J. McDermott and C. M. McKay, J. Acoust. Soc. Am. 101, 1622-1630 (1997); R. V. Shannon, Hear. Res. 11, 157-189 (1983)]. The purpose of this study is to test the hypothesis that the introduction of multiple rates may invalidate the tonotopic pitch structure resulting from place-pitch alone. The SPEAR3 developmental speech processor was used to collect psychophysical data from five cochlear implant users to assess the tonotopic structure for stimuli presented at two rates on all active electrodes. Pitch ranking data indicated many cases where pitch percepts overlapped across electrodes and rates. Thus, the results from this study suggest that pitch-based tuning across rate and electrode may be necessary to optimize performance of a multirate sound processing strategy in cochlear implant subjects.

Expediting the identification of impaired channels in cochlear implants via analysis of speech-based confusion matrices.

There is significant variability in the benefit provided by cochlear implants to severely deafened individuals. The reasons why some subjects exhibit low speech recognition scores are unknown; however, underlying physiological or psychophysical factors may be involved. Certain phenomena, such as indiscriminable electrodes and nonmonotonic pitch rankings, might hint at limitations in the ability of individual channels in the cochlear implant and/or sensorineural pathway to convey speech information. In this paper, four approaches for analyzing the results of a simple listening test using speech stimuli are investigated for the purpose of targeting channels of concern in order for follow-on psychophysical experiments to correctly identify channels performing in an "impaired" or anomalous manner. Listening tests were first conducted with normal-hearing subjects and acoustic models simulating channel-specific anomalies. Results indicate that these proposed analyses perform significantly better than chance in providing information about the location of anomalous channels. Vowel and consonant confusion matrices from six cochlear implant subjects were also analyzed to test the robustness of the proposed analyses to variability intrinsic to cochlear implant data. The current study suggests that confusion matrix analyses have the potential to expedite the identification of impaired channels by providing preliminary information prior to exhaustive psychophysical testing.

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 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.