Reading Your Mind: EEG during Reading Task

This paper demonstrates the ability to study the human reading behaviors with the use of Electroencephalography (EEG). This is a relatively new research direction because, obviously, gaze-tracking technologies are used specifically for those types of studies. We suspect, EEG, with the capability of recording brain-wave activities from the human scalp, in theory, could exhibit potential attributes to replace gaze-tracking in such research. To prove the concept, in this paper, we organized a BCI experiment and propose a model for effective classifying EEG data in comparison to the accuracy of gaze-tracking. The results show that by using EEG, we could achieve comparable results against the more established methods while demonstrating a potential live EEG applications. This paper also discusses certain points of consideration for using EEG in this work

Why don't the modules dominate - Investigating the Structure of a Well-Known Modularity-Inducing Problem Domain

Wagner's modularity inducing problem domain is a key contribution to the study of the evolution of modularity, including both evolutionary theory and evolutionary computation. We study its behavior under classical genetic algorithms. Unlike what we seem to observe in nature, the emergence of modularity is highly conditional and dependent, for example, on the eagerness of search. In nature, modular solutions generally dominate populations, whereas in this domain, modularity, when it emerges, is a relatively rare variant. Emergence of modularity depends heavily on random fluctuations in the fitness function, with a randomly varied but unchanging fitness function, modularity evolved far more rarely. Interestingly, high-fitness non-modular solutions could frequently be converted into even-higher-fitness modular solutions by manually removing all inter-module edges. Despite careful exploration, we do not yet have a full explanation of why the genetic algorithm was unable to find these better solutions

Keyboard before Head Tracking Depresses User Success in Remote Camera Control

In remote mining, operators of complex machinery have more tasks or devices to control than they have hands. For example, operating a rock breaker requires two handed joystick control to position and fire the jackhammer, leaving the camera control to either automatic control or require the operator to switch between controls. We modelled such a teleoperated setting by performing experiments using a simple physical game analogue, being a half size table soccer game with two handles. The complex camera angles of the mining application were modelled by obscuring the direct view of the play area and the use of a Pan-Tilt-Zoom (PTZ) camera. The camera control was via either a keyboard or via head tracking using two different sets of head gestures called "head motion" and "head flicking" for turning camera motion on/off. Our results show that the head motion control was able to provide a comparable performance to using a keyboard, while head flicking was significantly worse. In addition, the sequence of use of the three control methods is highly significant. It appears that use of the keyboard first depresses successful use of the head tracking methods, with significantly better results when one of the head tracking methods was used first. Analysis of the qualitative survey data collected supports that the worst (by performance) method was disliked by participants. Surprisingly, use of that worst method as the first control method significantly enhanced performance using the other two control methods

Observer’s Galvanic Skin Response for Discriminating Real from Fake Smiles

This paper demonstrates a system to discriminate real from fake smiles with high accuracy by sensing observers’ galvanic skin response (GSR). GSR signals are recorded from 10 observers, while they are watching 5 real and 5 posed or acted smile video stimuli. We investigate the effect of various feature selection methods on processed GSR signals (recorded features) and computed features (extracted features) from the processed GSR signals, by measuring classification performance using three different classifiers. A leave-one-observer-out process is implemented to reliably measure classification accuracy. It is found that simple neural network (NN) using random subset feature selection (RSFS) based on extracted features outperforms all other cases, with 96.5% classification accuracy on our two classes of smiles (real vs. fake). The high accuracy highlights the potential of this system for use in the future for discriminating observers’ reactions to authentic emotional stimuli in settings such as advertising and tutoring systems