Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications.

BACKGROUND: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. METHODS: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0-234 Hz), mid-frequency MWP (mf-MWP, 234 Hz-3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. RESULTS: All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. CONCLUSIONS: Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. TRIAL REGISTRATION: This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).

Changes in physical activity, sedentary time, and risk of falling: The Women's Health Initiative Observational Study.

Falling significantly affects quality of life, morbidity, and mortality among older adults. We sought to evaluate the prospective association between sedentary time, physical activity, and falling among post-menopausal women aged 50-79years recruited to the Women's Health Initiative Observational Study between 1993 and 1998 from 40 clinical centers across the United States. Baseline (B) and change in each of the following were evaluated at year 3 (Y3) and year 6 (Y6; baseline n=93,676; Y3 n=76,598; Y6 n=75,428): recreational physical activity (MET-h/wk), sitting, sleeping (min/day), and lean body mass by dual energy X-ray absorptiometry (subset N=6475). Falls per year (0, 1, 2, ≥3) were assessed annually by self-report questionnaire and then dichotomized as ≤1 and ≥2falls/year. Logistic regression models were adjusted for demographics, body mass index, fall history, tobacco and alcohol use, medical conditions, and medications. Higher baseline activity was associated with greater risk of falling at Y6 (18%; p for trend <0.0001). Increasing sedentary time minimally decreased falling (1% Y3; 2% Y6; p<0.05). Increasing activity up to ≥9MET-h/wk. (OR: 1.12, 95% CI: 1.03-1.22) or maintaining ≥9MET-h/wk. (OR: 1.20, 95% CI: 1.13-1.29) increased falling at Y3 and Y6 (p for trend <0.001). Adding lean body mass to the models attenuated these relationships. Physically active lifestyles increased falling among post-menopausal women. Additional fall prevention strategies, such as balance and resistance training, should be evaluated to assist post-menopausal women in reaching or maintaining levels of aerobic activity known to prevent and manage several chronic diseases.

Primary caregivers of persons with brain injury: life change 1 year after injury.

The impact of a traumatic brain injury on the family of the injured person is just beginning to be explored. In the current study, 61 primary caregivers were contacted at 1 year following injury. They completed the Relative and Friend Support Index, Social Support Index, Trauma Complaints List and the Life Change Question. The majority of caregivers indicated at least mild negative life change following the brain injury. Greater social support was correlated with less life change and greater injury severity was correlated with negative life change. Neither of these relationships was found to be significant at the 0.05 level. A significant positive correlation was found between caregivers' perception of deficits and the degree of negative life change. Perceived deficits accounted for the greatest amount of variance in life change followed by relative and friend support when all variables were entered into a stepwise regression. Further analyses indicated that the most significant factor of the Trauma Complaints List in predicting life change may be problems with cognition, which accounted for a significant amount of the variance in life change. Implications for counselling and further research regarding caregivers of persons with brain injury are discussed.