Rm Ronald Aarts

A survey of stimulation methods used in SSVEP-based BCIs

Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

Single-accelerometer-based daily physical activity classification

In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers’ intervention. For the purpose of assessing customers’ daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of ~80%, which was comparable with that obtained by a Decision Tree classifier.

Time-Frequency Analysis of Accelerometry Data for Detection of Myoclonic Seizures

Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in “normal” movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.

Sleep and Wake Classification With Actigraphy and Respiratory Effort Using Dynamic Warping

This paper proposes the use of dynamic warping (DW) methods for improving automatic sleep and wake classification using actigraphy and respiratory effort. DW is an algorithm that finds an optimal nonlinear alignment between two series allowing scaling and shifting. It is widely used to quantify (dis)similarity between two series. To compare the respiratory effort between sleep and wake states by means of (dis)similarity, we constructed two novel features based on DW. For a given epoch of a respiratory effort recording, the features search for the optimally aligned epoch within the same recording in time and frequency domain. This is expected to yield a high (or low) similarity score when this epoch is sleep (or wake). Since the comparison occurs throughout the entire-night recording of a subject, it may reduce the effects of within- and between-subject variations of the respiratory effort, and thus help discriminate between sleep and wake states. The DW-based features were evaluated using a linear discriminant classifier on a dataset of 15 healthy subjects. Results show that the DW-based features can provide a Cohens Kappa coefficient of agreement κ = 0.59 which is significantly higher than the existing respiratory-based features and is comparable to actigraphy. After combining the actigraphy and the DW-based features, the classifier achieved a κ of 0.66 and an overall accuracy of 95.7%, outperforming an earlier actigraphy- and respiratory-based feature set ( κ = 0.62). The results are also comparable with those obtained using an actigraphy- and cardiorespiratory-based feature set but have the important advantage that they do not require an ECG signal to be recorded.

Performance evaluation of a tri-axial accelerometry-based respiration monitoring for ambient assisted living

Ambient Assisted Living (AAL) technology is often proposed as a way to tackle the increasing cost of healthcare caused by population aging. However, the sensing technology for continuous respiratory monitoring at home is lacking. Known approaches of respiratory monitoring are based on measuring either respiratory effect, e.g. tracheal sound recording by a bio-acoustic sensor, or respiratory effort, e.g. abdomen movement measurement by a tri-axial accelerometer. This paper proposes a home respiration monitoring system using a tri-axial accelerometer. Three different methods to extract a single respiratory signal from the tri-axial data are proposed and analyzed. The performance of the methods is evaluated for various possible respiration conditions, defined by the sensor orientation and respiration-induced abdomen movement. The method based on Principal Component Analysis (PCA) performs better than selecting the best axis. The analytical approach called Full Angle shows worse results than the best axis when the gravity vector is close to one of the sensor’s axes (<15 degrees). Hybrid-PCA, which is a combination of both methods, performs comparable to PCA. The system is evaluated using simulated data from the most common postures, such as lying and sitting, as well as real data collected from five subjects. The results show that the system can successfully reconstruct the respiration-induced movement, which is necessary to determine the respiratory rate accurately.

3-D indoor positioning method using a single compact base station

Context awareness will become increasingly important in future domestic consumer electronics. In many domestic context aware applications, there is a need for location and orientation information about persons, devices or objects in the home. This information can be provided by a dedicated indoor location system. A consumer location system should be robust, safe, easy to set up, low cost, and should have a minimal infrastructure. In this paper, a step towards consumer location systems is taken by proposing an ultrasonic positioning method that needs just a single compact base station to measure 3D positions of mobile devices in a room. The method employs ultrasound time-of-flight tri-lateration to estimate device positions, using a base station containing an array of three ultrasound transducers. Five potential problems of the proposed method are identified; the main problem being line-of-sight path occlusion. The method has been prototyped, and initial results show an accuracy of 1.41 m or better for 95% of the position estimates, in case of a good line-of-sight path. It is concluded that the method is promising for providing 3D position information at around 1 m accuracy for context aware applications, but that the problem of line-of-sight occlusion requires further investigation.

Analyzing respiratory effort amplitude for automated sleep stage classification

Abstract Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohens Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance.

Approximation of the Struve function H1 occurring in impedance calculations

The problem of the rigid-piston radiator mounted in an infinite baffle has been studied widely for tutorial as well as for practical reasons. The resulting theory is commonly applied to model a loudspeaker in the audio-frequency range. A special function, the Struve function H1 (z), occurs in the expressions for the rigid-piston radiator. This Struve function is not readily available in programs such as Matlab or Mathcad, nor in computer languages such as FORTRAN and C. Therefore a simple and effective approximation of H1 (z) which is valid for all z is developed. Some examples of the application of the Struve function in acoustics are presented.

Sound radiation quantities arising from a resilient circular radiator

Power series expansions in ka are derived for the pressure at the edge of a radiator, the reaction force on the radiator, and the total radiated power arising from a harmonically excited, resilient, flat, circular radiator of radius a in an infinite baffle. The velocity profiles on the radiator are either Stenzel functions (1-(sigma/a)2)n, with sigma the radial coordinate on the radiator, or linear combinations of Zernike functions Pn(2(sigma/a)2-1), with Pn the Legendre polynomial of degree n. Both sets of functions give rise, via Kings integral for the pressure, to integrals for the quantities of interest involving the product of two Bessel functions. These integrals have a power series expansion and allow an expression in terms of Bessel functions of the first kind and Struve functions. Consequently, many of the results in [M. Greenspan, J. Acoust. Soc. Am. 65, 608-621 (1979)] are generalized and treated in a unified manner. A foreseen application is for loudspeakers. The relation between the radiated power in the near-field on one hand and in the far field on the other is highlighted.

On-axis and far-field sound radiation from resilient flat and dome-shaped radiators

On-axis and far-field series expansions are developed for the sound pressure due to an arbitrary, circular symmetric velocity distribution on a flat radiator in an infinite baffle. These expansions are obtained by expanding the velocity distributions in terms of orthogonal polynomials R(2n) (0)(sigma/a)=P(n)(2(sigma/a)(2)-1) with P(n) the Legendre polynomials. The terms R(2n) (0) give rise to a closed-form expression for the pressure on-axis as well as for the far-field pressure. Furthermore, for a large number of velocity profiles, including those associated with the rigid piston, the simply supported radiator, and the clamped radiators as well as Gaussian radiators, there are closed-form expressions for the required expansion coefficients. In particular, for the rigid, simply supported, and clamped radiators, this results in explicit finite-series expressions for both the on-axis and far-field pressures. In the reverse direction, a method of estimating velocity distributions from (measured) on-axis pressures by matching in terms of expansion coefficients is proposed. Together with the forward far-field computation scheme, this yields a method for far-field loudspeaker assessment from on-axis data (generalized Keele scheme). The forward computation scheme is extended to dome-shaped radiators with arbitrary velocity distributions.