Unobtrusive, Continuous LIDAR-Based Measurement of Gait Characteristics at Home.

This paper describes a novel approach to the unobtrusive assessment of a subset of gait characteristics using a light detection and ranging (LIDAR) device. The developed device is poised to enable unobtrusive, nearly continuous monitoring and inference of patients' gait characteristics to assess physical and cognitive states. The device provides a rapidly sampled signal representing the distance of a participant's body from the LIDAR device. The densely sampled distance estimation is processed by custom algorithms that can potentially be used to estimate various gait characteristics such as step size, cadence, double support, and even step-size symmetry.Clinical Relevance- Since gait is a complex behavior that requires seamless cooperation of multiple systems, including sensation, perception, muscular synergies, and even cognition. Subtle changes in gait may, therefore, indicate issues with physical and mental functionality. In addition to the walking speed, the gait monitoring results can provide inferences about the physical and cognitive states of the unobtrusively monitored individuals using their own data as a baseline.

Applying dynamic data collection to improve dry electrode system performance for a P300-based brain-computer interface.

OBJECTIVE: Dry electrodes have an advantage over gel-based 'wet' electrodes by providing quicker set-up time for electroencephalography recording; however, the potentially poorer contact can result in noisier recordings. We examine the impact that this may have on brain-computer interface communication and potential approaches for mitigation. APPROACH: We present a performance comparison of wet and dry electrodes for use with the P300 speller system in both healthy participants and participants with communication disabilities (ALS and PLS), and investigate the potential for a data-driven dynamic data collection algorithm to compensate for the lower signal-to-noise ratio (SNR) in dry systems. MAIN RESULTS: Performance results from sixteen healthy participants obtained in the standard static data collection environment demonstrate a substantial loss in accuracy with the dry system. Using a dynamic stopping algorithm, performance may have been improved by collecting more data in the dry system for ten healthy participants and eight participants with communication disabilities; however, the algorithm did not fully compensate for the lower SNR of the dry system. An analysis of the wet and dry system recordings revealed that delta and theta frequency band power (0.1-4 Hz and 4-8 Hz, respectively) are consistently higher in dry system recordings across participants, indicating that transient and drift artifacts may be an issue for dry systems. SIGNIFICANCE: Using dry electrodes is desirable for reduced set-up time; however, this study demonstrates that online performance is significantly poorer than for wet electrodes for users with and without disabilities. We test a new application of dynamic stopping algorithms to compensate for poorer SNR. Dynamic stopping improved dry system performance; however, further signal processing efforts are likely necessary for full mitigation.

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 user's EEG data. We further enhanced the algorithm by incorporating information about the user's 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.