In this paper, we investigate the phase information for classification between clench speed and clench force motor imagery for BCI applications. The multivariate extensions of empirical mode decomposition (MEMD) are used to decompose EEG data into intrinsic mode functions (IMFs). Then, the phase information is got by transforming IMFs into analytic signal using Hiblert transforms. Six feature t...

At present, in the feature extraction of non-stationary vibration signals, many timefrequency analytic methods have emerged to meet the further need of non-stationary signal analysis such as Wavelet Transform (WT), Short Time Fourier Transform (STFT), WignerVille Distribution (WVD), Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and so on. However, these time-f...

objective: to evaluate the current scientific evidence for the applicability, safety and effectiveness of pathways of enhanced recovery after emergency surgery (eras). methods: we undertook a search using pubmed and cochrane databases for eras protocols in emergency cases. the search generated 65 titles; after eliminating the papers not meeting search criteria, we selected 4 cohort studies and ...

Making a diagnosis involves ratifying or verifying a proposed answer. Formalizing this verification process with checklists, which highlight key variables involved in the diagnostic decision, is often advocated. However, the mechanisms by which a checklist might allow clinicians to improve their verification process have not been well studied. We hypothesize that using a checklist to verify dia...

Conventional pulse-echo ultrasonic receivers rectify the received signal. Because the signature of the reflecting interfaces is modulated by the predominant ultrasonic frequency, interpretation of this signal in terms of the structure of the reflecting interfaces is difficult. Smoothing, as by an R-C filter, ameliorates this effect, giving a less confusing display at the expense of resolution. ...

Traditional feature extraction methods for automatic speech recognition (ASR), such as MFCC (Mel-frequency cepstral coefficients) and PLP (perceptual linear prediction) [6], are extracted from short-term spectral envelopes and can be used to realize promising ASR systems. On the other hand, features extracted by TRAPs-like classifiers [2] are based on long-term envelopes of narrow-band signals....