On the stimulus duty cycle in steady state visual evoked potential

Brain-computer interfaces (BCI) are useful devices that allow direct control of external devices using thoughts, i.e. brain's electrical activity. There are several BCI paradigms, of which steady state visual evoked potential (SSVEP) is the most commonly used due to its quick response and accuracy. SSVEP stimuli are typically generated by varying the luminance of a target for a set number of frames or display events. Conventionally, SSVEP based BCI paradigms use magnitude (amplitude) information from frequency domain but recently, SSVEP based BCI paradigms have begun to utilize phase information to discriminate between similar frequency targets. This paper will demonstrate that using a single frame to modulate a stimulus may lead to a bi-modal distribution of SSVEP as a consequence of a user attending both transition edges. This incoherence, while of less importance in traditional magnitude domain SSVEP BCIs becomes critical when phase is taken into account. An alternative modulation technique incorporating a 50% duty cycle is also a popular method for generating SSVEP stimuli but has a unimodal distribution due to user's forced attention to a single transition edge. This paper demonstrates that utilizing the second method results in significantly enhanced performance in information transfer rate in a phase discrimination SSVEP based BCI

A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) create a new approach to human computer communication, allowing the user to control a system simply by performing mental tasks such as motor imagery. This paper proposes and analyses different strategies for time segmentation in extracting common spatial patterns of the brain signals associated to these tasks leading to an improvement of BCI performance

Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface

The steady state visual evoked protocol has recently become a popular paradigm in brain–computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control

Boussinesq Modelling of Waves and Currents in the Presence of Submerged Detached/Discontinuous Breakwaters

The effect of beach configurations with the main focus on the detached submerged breakwater on shoreline currents is investigated numerically. The Boussinesq equations are used to model the beach with a constant slope, continuous submerged breakwater, and discontinuous/detached submerged breakwater. Our numerical simulation results show that the transient rip currents are generated near the shoreline at the beach with constant slope while the continuous submerged breakwater structure creates a calm beach area along the shoreline. The presence of the gap in submerged breakwater changes the currents along the shoreline by generating rip currents with two pairs of vortices. One pair of vorticities, located around the gap, damage the breakwater by transmitting sediments along the breakwater foundation and eroding its surface. The second pair, created near the shoreline, erodes the shoreline due to sediment transportation and leads to a dangerous and unsafe situation for swimmers. The rip current creates five main critical areas with the maximum velocity towards the shoreline and offshore. The first set of three areas (numbered 1, 2, 3) has an approximately average velocity of 1-1.25 m/s towards the shoreline. One of these areas (numbered 2) is located close to the shoreline and the other two (numbered 1 and 3) are found to occur near the edge of the detached part of the breakwater. The second set of the two areas (numbered by 4 and 5) has the average velocity that is higher than 2.1 m/s towards the offshore and is located at the beginning part of the rip neck. An approximately linear relationship between the returning velocity and the gap length is observed. As the gap length decreases the location of the areas (numbered 4 and 5) gets closer to the center of the gap. Our simulations indicate that the return velocity towards the offshore increases at the gap center while the gap length decreases. Furthermore, the velocity profiles have a sharp jump for gap length that is approximately smaller than 80 m. Also, the return velocity at the gap center is related to the height of the breakwater. The breakwater that is higher (the breakwater height d = 4.2 m) damps wave energy more than shorter breakwater and the return velocity decreases for this structure. For smaller heights (d = 3.7 and 3.2) damping is nearly the same and the returning flow varies depending on the available space through the gap. Specifically, the return velocity for d = 3.7 is higher than that for d = 3.2. The numerical results presented herein suggest that aggressive rip currents are generated in the case of detached submerged breakwater beach configurations

An unusual rash heralding pleomorphic transformation of Mantle Cell Lymphoma

Open Access via Wiley publishing agreement.Peer reviewedPublisher PD

Bridging sd1 molecular knowledge with recent breeding strategies for the improvement of traditional rice varieties - a japonica case-study

The rice semidwarfing gene, sd1, also known as the “green revolution gene”, has been studied intensively due to its contribution to the increase of crop production. Although sd1 breeding was extensively applied since the 1960s, the recent advances in the molecular basis of this gene alloweddesigning a more precise breeding strategy - marker assisted backcrossing (MAB) - to track sd1 introgression in two traditional rice varieties. For selection of sd1 plants we first confirmed the efficiency of specific markers based on Os200 x 2 gene sequence. Background selection was alsoperformed with the help of microsatellites markers (SSR) and a total of 7 breeding lines were recovered containing a higher percentage of recurrent parent genome (RPG). Analysis of Covariance (ANCOVA) using mean progenitor plant height as covariate was performed to compare several agronomic and quality-related parameters in two different environments. The results suggest that plant height differs significantly between the two environments F(1, 220) = 155.336; p < 0.001. From the total variability ofplant height we could conclude that 73% is due to the genotype, while 10.4% depends on the environment. In addition, the percentage of RPG seems negatively correlated with plant height (p < 0.005). MAB and background selection thus revealed as useful tools to assist breeding forsemidwarfism in traditional rice varieties

Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics

Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a pilot study that reveal the combined use of brain and heart modalities provide improved classification performance and further-more, an improvement in the stability of the features over time through the use of binaural brain entrainment. The classification rate was increased, for the case of the neural network classifier from 92.4% to 95.1% and for the case of LDA, from 98.6% to 99.8%. The average standard deviation with binaural brain entrainment using all the inter-session features (from all the subjects) was 1.09, as compared to 1.26 without entrainment. This result suggests the improved stability of both the EEG and ECG features over time and hence resulting in higher classification performance. Overall, the results indicate that combining ECG and EEG gives improved classification performance and that through the use of binaural brain entrainment, both the ECG and EEG features are more stable over time

A probabilistic fusion framework for 3-D reconstruction using heterogeneous sensors

This letter proposes a framework to perform 3-D reconstruction using a heterogeneous sensor network, with potential use in augmented reality, human behavior understanding, smart-room implementations, robotics, and many other applications. We fuse orientation measurements from inertial sensors, images from cameras and depth data from Time of Flight sensors within a probabilistic framework in a synergistic manner to obtain robust reconstructions. A fully probabilistic method is proposed to efficiently fuse the multi-modal data of the system

Effects of hidden unit sizes and autoregressive features in mental task classification

Classification of electroencephalogram (EEG) signals extracted during mental tasks is a technique that is actively pursued for Brain Computer Interfaces (BCI) designs. In this paper, we compared the classification performances of univariateautoregressive (AR) and multivariate autoregressive (MAR) models for representing EEG signals that were extracted during different mental tasks. Multilayer Perceptron (MLP) neural network (NN) trained by the backpropagation (BP) algorithm was used to classify these features into the different categories representing the mental tasks. Classification performances were also compared across different mental task combinations and 2 sets of hidden units (HU): 2 to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks were studied for each subject. Three different feature extraction methods with 6th order were used to extract features from these EEG signals: AR coefficients computed with Burg-s algorithm (ARBG), AR coefficients computed with stepwise least square algorithm (ARLS) and MAR coefficients computed with stepwise least square algorithm. The best results were obtained with 20 to 100 HU using ARBG. It is concluded that i) it is important to choose the suitable mental tasks for different individuals for a successful BCI design, ii) higher HU are more suitable and iii) ARBG is the most suitable feature extraction method