Fast Memory Response Behavior Performance Prediction based on EEG Signals

One of the most important parts of all applications trying to discriminate between a person's different mental tasks using their recorded EEG data is the process of feature construction. A common practice for this is to exploit an apriori knowledge about the nature of the mental processes of interest and their impact on the EEG signals. However, the use of features constructed in this way is restricted to applications concerning the corresponding mental processes. We present here a novel method for EEG data classification which is very general as it makes no assumptions about the nature of the EEG signals. It is based on the construction of a characteristic signal for each class which remains as invariant as possible over the trials belonging to that class. We use the proposed method in combination with a novel method for channel selection in an oddball experiment to predict a person's quick or late response.

Comparative exploration on EEG signal filtering using window control methods

This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

Working memory load-dependent spatio-temporal activity of single-trial P3 response detected with an adaptive wavelet denoiser

Reconfigurable computing (RC) is rapidly emerging as a promising technology for the future of high-performance and embedded computing, enabling systems with the computational density and power of custom-logic hardware and the versatility of software-driven hardware in an optimal mix. Novel methods for rapid virtual prototyping, performance prediction, and evaluation are of critical importance in the engineering of complex reconfigurable systems and applications. These techniques can yield insightful tradeoff analyses while saving valuable time and resources for researchers and engineers alike. The research described herein provides a methodology for mapping arbitrary applications to targeted reconfigurable platforms in a simulation environment called RCSE. By splitting the process into two domains, the application and simulation domains, characterization of each element can occur independently and in parallel, leading to fast and accurate performance prediction results for large and complex systems. This article presents the design of a novel framework for system-level simulative performance prediction of RC systems and applications. The article also presents a set of case studies analyzing two applications, Hyperspectral Imaging (HSI) and Molecular Dynamics (MD), across three disparate RC platforms within the simulation framework. The validation results using each of these applications and systems show that our framework can quickly obtain performance prediction results with reasonable accuracy on a variety of platforms. Finally, a set of simulative case studies are presented to illustrate the various capabilities of the framework to quickly obtain a wide range of performance prediction results and power consumption estimates.

Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. This study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures. Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. The study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis. The obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.

Background: Traditional physical rehabilitation involves participants performing repetitive body movements with the assistance of physiotherapists. Owing to the exercises’ monotonous nature and lack of reward, participants may become disinterested and cease their recovery. Games could be used as tools to engage participants in the rehabilitation process. Consequently, participants could perform rehabilitation exercises while playing the game, receiving rewards from the experience. Maintaining the players’ engagement requires regularly adjusting the game difficulty. The players’ engagement can be measured using questionnaires and biosignals (e.g., electroencephalography signals—EEG). This study aims to determine whether there is a significant difference in players’ engagement between two game modes with different game difficulty adjustments: non-tailored and tailored modes. Methods: We implemented two game modes which were controlled using hand movements. The features of the game rewards (position and size) were changed in the game scene; hence, the game difficulty could be modified. The non-tailored mode set the features of rewards in the game scene randomly. Conversely, the tailored mode set the features of rewards in the game scene based on the participants’ range of motion using fuzzy logic. Consequently, the game difficulty was adjusted dynamically. Additionally, engagement was computed from 53 healthy participants in both game modes using two EEG sensors: Bitalino Revolution and Unicorn. Specifically, the theta (θ) and alpha (α) bands from the frontal and parietal lobes were computed from the EEG data. A questionnaire was applied to participants after finishing playing both game modes to collect their impressions on the following: their favorite game mode, the game mode that was the easiest to play, the game mode that was the least frustrating to play, the game mode that was the least boring to play, the game mode that was the most entertaining to play, and the game mode that had the fastest game response time. Results: The non-tailored game mode reported the following means of engagement: 6.297 ± 11.274 using the Unicorn sensor, and 3.616 ± 0.771 using the Bitalino sensor. The tailored game mode reported the following means of engagement: 4.408 ± 6.243 using the Unicorn sensor, and 3.619 ± 0.551 using Bitalino. The non-tailored mode reported the highest mean engagement (6.297) when the Unicorn sensor was used to collect EEG signals. Most participants selected the non-tailored game mode as their favorite, and the most entertaining mode, irrespective of the EEG sensor. Conversely, most participants chose the tailored game mode as the easiest, and the least frustrating mode to play, irrespective of the EEG sensor. Conclusions: A Wilcoxon-Signed-Rank test revealed that there was only a significant difference in engagement between game modes when the EEG signal was collected via the Unicorn sensor (p value = 0.04054). Fisher’s exact tests showed significant associations between the game modes (non-tailored, tailored) and the following players’ variables: ease of play using the Unicorn sensor (p value = 0.009341), and frustration using Unicorn sensor (p value = 0.0466).

EEG variability: Task-driven or subject-driven signal of interest?

International audience

Fast and accurate flow calculation and performance prediction of multistage axial flow turbines at design and off-design conditions were performed using a compressible steady state inviscid through-flow code with high fidelity loss and mixing models. The code is based on a stream function model and a finite element solution procedure. A new design system has been developed which optimizes hub and shroud geometry and inlet and exit flow field parameters for each blade row of a multistage axial flow turbine. Optimization was performed using a hybrid constrained optimization code that switches among the modules automatically in order to avoid local minima and to accelerate design convergence rate. By automatically varying a relatively small number of geometric variables per turbine stage it is possible to find an optimal radial distribution of flow parameters at the inlet and outlet of every blade row. Thus, an optimized meridional flow path can be found that is defined by the optimized shape of the hub and shroud while keeping blade shapes intact. The multistage design optimization system has been demonstrated using an actual two-stage axial gas turbine as an example. The comparison of computed performance of an already very high efficiency initial design and its optimized design demonstrates more than 1 per cent improvement in the turbine efficiency at design and significant off-design conditions. The entire design optimization process is feasible on a typical single-processor computer workstation or a personal computer.

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

Compared with conventional high-specific-speed axial flow pump, better cavitation performance and compact size have been achieved in contra-rotating axial flow pump, where the rear rotor is employed additionally to the front rotor to convert the swirling flow to the pressure rise. Meanwhile, significantly deteriorated performance has also been observed at well off-design flow rates with design rotational speed. The rotational speed control (RSC) of front and rear rotors has been experimentally proved to be effective to enhance the performance. However, thorough investigations are necessary to find the optimum rotational speeds of rotors. It may be done by computational fluid dynamics (CFD) simulations, whereas it is time-consuming to cover the wide ranges of rotational speeds. Therefore, in the present paper, a fast and effective performance prediction model is established by considering radial equilibrium condition, conservation of rothalpy and mass, empirical deviation angle, bladerows interaction and empirical losses. Experimental and CFD results are employed to validate the proposed prediction model. It is found that the proposed model shows good enough accuracy in predicting performances of contra-rotating axial flow pump under RSC in broad flow rate range. Furthermore, an energy saving application of the proposed model is also illustrated for two typical system resistances. Compared with the traditional valve control under the design rotational speed operation, the RSC method can well modify the pump head to satisfy the system resistance at wide flow rate range with the significantly improved energy performance.

Detailed reservoir models routinely contain hundred of thousands to several million grid blocks. These models often cannot be used directly in a reservoir simulation because of the time and memory required for solving the pressure grid on the fine grid. We propose a nested gridding technique that efficiently obtains an approximate solution for the pressure field. The domain is divided into a series of coarse blocks, each containing many fine cells, for which effective mobilities are computed. The pressure is then found on the coarse scale. The pressure field within each coarse block is computed using constant flux boundary conditions obtained from the coarse pressure solution. In this way, a continuous but approximate velocity field is computed on the fine grid. The method is similar to the first step in a multigrid pressure solution. Streamline-based simulation is used to move saturations forward in time. The pressure field is periodically recomputed, taking account of the fine scale permeability and saturation distributions. We test the method for a series of example waterflood problems and demonstrate that the method can give accurate estimates of oil production for large 3D models up to 8.5 times faster than direct simulation using streamlines on the fine grid, making the method overall approximately up to 1,000 times faster than direct conventional simulation. The method is thus able to handle multi-million grid blocks problems easily, enabling simulation to be performed directly on detailed geological models. It is a robust alternative to traditional upscaling, since the effects of changing boundary conditions are automatically accommodated.

Distinguishing cognitive effort and working memory load using scale-invariance and alpha suppression in EEG