Average Window Length Research Articles

Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition.

In the conventional studies related to steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the window length (detection time) was typically predetermined through the offline analysis, which had limitations of practical applicability of a BCI system due to the inter-subject/trial variability of electroencephalography (EEG) signals. To address these limitations, this study aims to automatically optimize the window length for each trial based on training-free approaches and proposes a novel adaptive window method (ANCOVA-based filter-bank canonical correlation analysis, ABFCCA) for SSVEP-based BCIs. The proposed method is based on analysis of covariance (ANCOVA) which is applied after feature extraction by the conventional training-free SSVEP recognition approaches. To evaluate the performance of the proposed method, conventional fixed window and recent adaptive window methods were compared using two open-access datasets. In the Benchmark dataset, the average information transfer rate (ITR) was 146.81 bits/min, the average accuracy 93.55%, and the average window length 1.53 s. In the OpenBMI dataset, the average ITR was 119.01 bits/min, the average accuracy 83.50%, and the average window length 0.65 s. The proposed method significantly outperformed the conventional approaches with fixed window in terms of the accuracy and ITR, and is applicable to various SSVEP-based BCI paradigms based on the criterion of significance level without offline analysis to find optimal hyper-parameters. ABFCCA is enabled the practical use of various BCI systems by automatically optimizing the window length independently.

How Statistically Significant is the DMCA Coefficient?

In this paper, we proposed a statistical test for the Detrending Moving-Average Cross-Correlation Coefficient ([Formula: see text]). With this methodology, it is possible to evaluate the statistical significance of [Formula: see text] for different confidence levels. The test was applied to financial market and climatological data. Findings on this research show that rejection or non-rejection of the null hypothesis of [Formula: see text] depends on the size [Formula: see text] of the series and the moving average window length [Formula: see text] evaluated. Our findings also show a behavioral pattern in the critical values of [Formula: see text]. Fixing the size of the series [Formula: see text], as the size of the moving average window length [Formula: see text] increases, the critical values tend to increase.

Near-Optimal Moving Average Estimation at Characteristic Timescales: An Allan Variance Approach

A major challenge in moving average (MA) estimation is the selection of an appropriate averaging window length or timescale over which measurements remain relevant to the estimation task. Prior works typically perform timescale selection by examining multiple window lengths (or models) before selecting the `optimal' one using heuristics, domain knowledge expertise, goodness-of-fit, or information criterion (e.g., AIC, BIC etc.). In the presented work, we propose an alternative mechanism based on Allan Variance (AVAR) that obviates the need for assessing multiple models and systematically reduces reliance on heuristics or rules-of-thumb. The Allan Variance approach is used to identify the timescale that minimizes bias, thus determining the timescale over which past information remains most relevant. We also introduce an alternative method to obtain AVAR for unevenly spaced timeseries. The results from moving average estimation using an Allan Variance-determined window length are compared to the optimal moving average estimator that minimizes mean square error (MSE) for a variety of signals corrupted with Gaussian white noise. While the relevant timescales determined through AVAR tend to be longer than those associated with minimum MSE (i.e., AVAR-based MA estimation requires more measurements spread over a longer period of time), the AVAR-based moving average approach provides a valuable, systematic technique for near-optimal simple moving average estimation.

Detrended cross-correlation patterns between North Atlantic oscillation and precipitation

In this study, the long-range relationships between North Atlantic Oscillation (NAO) and precipitation data obtained from Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP) from 1979 to 2016 are investigated using Detrended Fluctuation Moving Average Cross-Correlation Analysis (DMCA). In the atmosphere, teleconnections through strong convective processes sporadically affect various climatic regimes in Europe, Mediterranean basin, North Africa, Middle East, and Caucasus. The NAO is one of the teleconnection processes and results in heavy rainfall in the Mediterranean basin during its negative phase while it gives rise to rain in Europe during its positive phase. The DMCA technique shows that the NAO fluctuation series exhibit different long-range cross-correlation coefficients, ρDMCA(s) with “s” being the moving average time window length, between the precipitation values and NAO. Large ρDMCA(s) coefficients with time window(s) larger than 12 months were obtained particularly over the Mediterranean basin, near the North Pole including northern Europe. Furthermore, the ρDMCA(s) coefficients were grouped into clusters using K-mean method to distinguish the similar patterns. The 1st cluster refers to the negative phase of the NAO indicating warm-rainy conditions and dry spells, which is especially evident in the Mediterranean basin. The 2nd cluster represents the long-range cross-correlation with respect to the positive phase of NAO and precipitation values, particularly for the Western and Northern Europe. Conversely, the 3rd cluster is evaluated as power law of long-range cross-correlations between the precipitation and NAO with respect to the different time scale processes.

Fiber optic gyroscope noise reduction with fiber ring resonator.

A method of improving the angular random walk (ARW) of a fiber optic gyroscope (FOG) using a fiber ring resonator (FRR) to reduce the relative intensity noise (RIN) is demonstrated. An FRR reduces the RIN near the proper frequency and improves the ARW. The analytical forms for the RIN transfer function and the power transmission of the FRR are derived when the free spectral range of the FRR is much narrower than the source bandwidth. Building the FRR using couplers and fiber with low excess loss enhances the RIN reduction. For the coupling ratios, there is a trade-off between the RIN reduction and the power transmission. The coupling ratios are 90%-10% tap in the experiment. When the FRR resonator length is shorter than the FOG coil length, the ARW improvement is further enhanced by adjusting the average window length in the signal processing. An experiment demonstrated a 7dB decrease in the RIN at the loop proper frequency, which reduces the standard deviation by 3.5dB. The ARW was reduced from 970 to 570 μdeg/h1/2, which amounts to a 2.3dB reduction.

A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System.

In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m.

등척성 일정 자의 수축 시에 기록한 표면근전도 신호에 대한 근전도 진폭 추정기의 정량적 분석

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[㎳] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively.

Age-related differences in SSVEP-based BCI performance

Brain–Computer Interface (BCI) systems analyze brain signals to generate control commands for computer applications or external devices. Utilized as alternative communication channel, BCIs have the potential to assist people with severe motor disabilities to interact with their environment and to participate in daily life activities. Handicapped people from all age groups could benefit from such BCI technologies. Although some papers have previously reported slightly worse BCI performance by older subjects, in many studies BCI systems were tested with young subjects only.In the presented paper age-associated differences in BCI performance were investigated. We compared accuracy and speed of a steady-state visual evoked potential (SSVEP)-based BCI spelling application controlled by participants of two different equally sized age groups. Twenty subjects (eleven female and nine male) participated in this study; each age group consisted of ten subjects, ranging from 19 to 27 years and from 64 to 76 years. Our results confirm that elderly people may have a deteriorated information transfer rate (ITR). The mean (SD) ITR of the young age group was 27.36 (6.50) bit/min while the elderly people achieved a significantly lower ITR of 16.10 (5.90) bit/min. The average time window length associated with the signal classification was usually larger for the participants of advanced age. These findings show that the subject age must be taken into account during the development of SSVEP-based applications.

The Impact of Averaging Window Length on the"Desaturation Indexes during Overnight Pulse Oximetry at High-Altitude".

To determine the impact of averaging window-length on the "desaturation" indexes (DIs) obtained via overnight pulse oximetry (SpO2) at high altitude. Overnight SpO2 data were collected during a 10-day sojourn at high altitude. SpO2 was obtained using a commercial wrist-worn finger oximeter whose firmware was modified to store unaveraged beat-to-beat data. Simple moving averages of window lengths spanning 2 to 20 cardiac beats were retrospectively applied to beat-to-beat SpO2 datasets. After SpO2 artifacts were removed, the following DIs were then calculated for each of the averaged datasets: oxygen desaturation index (ODI); total sleep time with SpO2 < 80% (TST < 80), and the lowest SpO2 observed during sleep (SpO2 low). South Base Camp, Mt. Everest (5,364 m elevation). Five healthy, adult males (35 ± 5 y; 180 ± 1 cm; 85 ± 4 kg). N/A. 49 datasets were obtained from the 5 participants, totalling 239 hours of data. For all window lengths ≥ 2 beats, ODI and TST < 80 were lower, and SpO2 low was higher than those values obtained from the beat-to-beat SpO2 time series data (P < 0.05). Our findings indicate that increasing oximeter averaging window length progressively underestimates the frequency and magnitude of sleep disordered breathing events at high altitude, as indirectly assessed via the desaturation indexes.

Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series

In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient ρDMCA(λ) with a moving average window length λ. We analytically show that the coefficient ranges between −1 and 1 as a standard correlation does. In the simulation study, we show that the values of ρDMCA(λ) very well correspond to the true correlation between the analyzed series regardless the (non-)stationarity level. Dependence of the newly proposed measure on other parameters–correlation level, moving average window length and time series length–is discussed as well.

Numerical demodulation of a Gaussian white noise modulated in amplitude by a deterministic volatility

The Gaussian white noise modulated in amplitude is defined as the product of a Gaussian white noise and a slowly varying signal with strictly positive values, called volatility. It is a special case of the superstatistical systems with the amplitude as the single parameter associated to the environment variations. If the volatility is deterministic, then the demodulation, i.e., the separation of the two components from a measured time series, can be achieved by a moving average with the averaging window length optimized by the condition that the absolute values of the estimated white noise are uncorrelated. Using Monte Carlo experiments we show that the large scale deterministic volatility can be accurately numerically determined. The artificial deterministic volatilities have a variety of shapes comparable with those occurring in real financial time series. Applied to the daily returns of the S&P500 index, the demodulation algorithm indicates that the most part of the financial volatility is deterministic.

Time and frequency domain methods for quantifying common modulation of motor unit firing patterns

BackgroundIn investigations of the human motor system, two approaches are generally employed toward the identification of common modulating drives from motor unit recordings. One is a frequency domain method and uses the coherence function to determine the degree of linear correlation between each frequency component of the signals. The other is a time domain method that has been developed to determine the strength of low frequency common modulations between motor unit spike trains, often referred to in the literature as 'common drive'.MethodsThe relationships between these methods are systematically explored using both mathematical and experimental procedures. A mathematical derivation is presented that shows the theoretical relationship between both time and frequency domain techniques. Multiple recordings from concurrent activities of pairs of motor units are studied and linear regressions are performed between time and frequency domain estimates (for different time domain window sizes) to assess their equivalence.ResultsAnalytically, it may be demonstrated that under the theoretical condition of a narrowband point frequency, the two relations are equivalent. However practical situations deviate from this ideal condition. The correlation between the two techniques varies with time domain moving average window length and for window lengths of 200 ms, 400 ms and 800 ms, the r2 regression statistics (p < 0.05) are 0.56, 0.81 and 0.80 respectively.ConclusionsAlthough theoretically equivalent and experimentally well correlated there are a number of minor discrepancies between the two techniques that are explored. The time domain technique is preferred for short data segments and is better able to quantify the strength of a broad band drive into a single index. The frequency domain measures are more encompassing, providing a complete description of all oscillatory inputs and are better suited to quantifying narrow ranges of descending input into a single index. In general the physiological question at hand should dictate which technique is best suited.