SAFE: An EEG dataset for stable affective feature selection

An affective brain-computer interface (aBCI) is a direct communication pathway between human brain and computer, via which the computer tries to recognize the affective states of its user and respond accordingly. As aBCI introduces personal affective factors into human-computer interaction, it could potentially enrich the user’s experience during the interaction. Successful emotion recognition plays a key role in such a system. The state-of-the-art aBCIs leverage machine learning techniques which consist in acquiring affective electroencephalogram (EEG) signals from the user and calibrating the classifier to the affective patterns of the user. Many studies have reported satisfactory recognition accuracy using this paradigm. However, affective neural patterns are volatile over time even for the same subject. The recognition accuracy cannot be maintained if the usage of aBCI prolongs without recalibration. Existing studies have overlooked the performance evaluation of aBCI during long-term use. In this paper, we propose SAFE—an EEG dataset for stable affective feature selection. The dataset includes multiple recording sessions spanning across several days for each subject. Multiple sessions across different days were recorded so that the long-term recognition performance of aBCI can be evaluated. Based on this dataset, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during long-term usage. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We invite other researchers to test the performance of their aBCI algorithms on this dataset, and especially to evaluate the long-term performance of their methods

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Geometric data access for scientific and engineering databases

This dissertation studies how to enrich the existing database technology by introducing geometry at the conceptual level of data queries, proposes a geometrie model for accessing and manipulating multidimensional data in scientifie and engineering databases, implements the proposed model in a geometrie query system prototype, and applies this system for solving the problem of materials selection in product design.Doctor of Philosoph

Geometric Querying for Dynamic Exploration of Multidimensional Data

Abstract. This paper describes a geometric query model for dynamic exploring multidimensional data. An application of the model for solving the problem of materials selection in product design is discussed. Data from database are interpreted geometrically as multidimensional points. A query window is a query solid of any shape specified by its location. The queries are formulated with geometric objects and operations over them. The geometric objects and operations are described with implicit functions. The process of query specification is visualized. The user poses the queries through graphics interface accessing dynamically multidimensional points, geometric primitives and applying geometric operations over them.

Stable dynamic algorithm based on virtual coupling for 6-DOF haptic rendering

In this paper, a new stable dynamic algorithm based on virtual coupling was proposed for 6-Degrees-of-Freedom (DOF) haptic rendering. It allows stable haptic manipulation of virtual objects when a virtual tool has physical property such as mass. In the haptic rendering process, we consider the dynamic property such as rotation inertia in each haptic frame. The main contribution of the stable dynamic algorithm is that it could overcome the "buzzing" problem appeared in the haptic rendering process. A nonlinear force/torque algorithm is proposed to calculate the haptic interaction when the collision happens between the virtual tool and virtual objects. The force/torque magnitude could saturate to the maximum force/torque value of the haptic device. The implemented algorithm was tested with peg-in-hole and Stanford bunny benchmarks. The experimental results showed that our algorithm was capable to provide stable 6-DOF haptic rendering for dynamic rigid virtual objects with physical property such as mass

EEG databases for emotion recognition

Emotion recognition from Electroencephalogram (EEG) rapidly gains interest from research community. Two affective EEG databases are presented in this paper. Two experiments are conducted to set up the databases. Audio and visual stimuli are used to evoke emotions during the experiments. The stimuli are selected from IADS and IAPS databases. 14 subjects participated in each experiment. Emotiv EEG device is used for the data recording. The EEG data are rated by the participants with arousal, valence, and dominance levels. The correlation between powers of different EEG bands and the affective ratings is studied. The results agree with the literature findings and analyses of benchmark DEAP database that proves the reliability of the two databases. Similar brain patterns of emotions are obtained between the established databases and the benchmark database. A SVM-based emotion recognition algorithm is proposed and applied to both databases and the benchmark database. Use of a Fractal Dimension feature in combination with statistical and Higher Order Crossings (HOC) features gives us results with the best accuracy. Up to 8 emotions can be recognized. The accuracy is consistent between the established databases and the benchmark database

c ○ World Scientific Publishing Company ORTHOPEDIC SURGERY TRAINING SIMULATION

Surgical training is one of the most promising areas in medicine where 3-D computer graphics and virtual reality techniques are emerging. Orthopedic surgery is a discipline requiring appreciation and understanding of complex 3-dimensional bony structures and their relationships to nerves, blood vessels and other vital structures. Learning these spatial skills requires a lengthy period and much practice. In this paper, we present a software simulator which was developed to aid in the understanding of the complex 3-dimensional relationships between bones and implants. The developed software cuts down the learning curve and allows for better and more precise surgery by letting the surgeon practice the surgery in a virtual environment before undertaking the actual procedure

EEG-based dominance level recognition for emotion-enabled interaction

Emotions recognized from Electroencephalogram (EEG) could reflect the real "inner" feelings of the human. Recently, research on real-time emotion recognition received more attention since it could be applied in games, e-learning systems or even in marketing. EEG signal can be divided into the delta, theta, alpha, beta, and gamma waves based on their frequency bands. Based on the Valence-Arousal-Dominance emotion model, we proposed a subject-dependent algorithm using the beta/alpha ratio to recognize high and low dominance levels of emotions from EEG. Three experiments were designed and carried out to collect the EEG data labeled with emotions. Sound clips from International Affective Digitized Sounds (IADS) database and music pieces were used to evoke emotions in the experiments. Our approach would allow real-time recognition of the emotions defined with different dominance levels in Valence-Arousal-Dominance model