IEEE Computational Intelligence Magazine - February 2018 - 27

by B. Kybartas and R. Bidarra, IEEE
Transactions on Computational Intelligence and AI in Games, Vol. 9, No. 3,
September 2017, pp. 239-253.

Case, by C. Hu, F. Arvin, C. Xiong,
and S.Yue, IEEE Transactions on Cognitive and Developmental Systems, Vol. 9,
No. 3, September 2017, pp. 241-254.

Wang, and C. Guan, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 1, No. 4, August
2017, pp. 270-279.

Digital Object Identifier: 10.1109/
TCIAIG.2016.2546063
"Computers are often used as tools
to design, implement, and even visualize a variety of narrative forms. Many
researchers and artists are now further
attempting to engage the computer
actively throughout the development
of the narrative itself. Any form of
computational narrative authoring is
at some level always mixed-initiative,
meaning that the processing capabilities of the computer are utilized with
a varying degree to automate certain
features of the authoring process. We
structure this survey by focusing on
two key components of stories, plot
and space, and more specifically the
degree to which these are either automated by the computer or authored
manually. By examining the successes
of existing research, we identify potential new research directions in the
field of computational narrative. We
also identify the advantages of developing a standard model of narrative to
allow for collaboration between plot
and space automation techniques.
This would likely benefit the field of
automated space generation with the
strengths in the field of automated
plot generation."

Digital Object Identifier: 10.1109/
TCDS.2016.2574624
"In this paper, the authors present a
new bio-inspired vision system embedded for micro-robots. The vision system
takes inspiration from locusts in detecting fast approaching objects. Neurophysiological research suggested that
locusts use a wide field visual neuron
called lobula giant movement detector
(LGMD) to respond to imminent collisions. In this paper, the authors present
the implementation of the selected
neuron model by a low-cost ARM processor as part of a composite vision
module. As the first embedded LGMD
vision module fits to a micro-robot, the
developed system performs all image
acquisition and processing independently. The vision module is placed on
top of a micro-robot to initiate obstacle
avoidance behavior autonomously. Both
simulation and real-world experiments
were carried out to test the reliability
and robustness of the vision system. The
results of the experiments with different scenarios demonstrated the potential of the bio-inspired vision system
as a low-cost embedded module for
autonomous robots."

Digital Object Identifier: 10.1109/
TETCI.2017.2690913
"Electroencephalography (EEG)
data are used to design useful indicators
that act as proxies for detecting humans'
mental activities. However, these electrical signals are susceptible to different
forms of interferences-known as artifacts-from voluntarily and involuntarily
muscle movements that greatly obscure
the information in the signal. It is pertinent to design effective artifact re moval techniques (ARTs) capable of
removing or reducing the impact of
these artifacts. However, most ARTs
have been focusing on handling a few
specific types, or a single type, of EEG
artifacts. EEG processing that generalizes to multiple types of artifacts remains
a major challenge. In this paper, we
investigate a variety of eight different
and typical artifacts that occur in practice. We characterize the spatiotemporal-frequency influence of these EEG
artifacts and offer two heuristics. The
proposed heuristics extend influential
independent component analysis to
clean the contaminated EEG signal.
These proposed heuristics are compared against four state-of-the-art EEG
ARTs using both real and synthesized
EEG, collected in the presence of multiple artifacts. The results show that
both heuristics offer superior spatiotemporal-frequency performance in
automatic artifacts removal and are able
to reconstruct clean EEG signals."

IEEE Transactions on Cognitive
and Developmental Systems

Bio-Inspired Embedded Vision System for
Autonomous Micro-Robots: The LGMD

IEEE Transactions on Emerging
Topics in Computational Intelligence

Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components, by S. K. Goh, H. A.
Abbass, K. C. Tan, A. Al-Mamun, C.

February 2018 | Ieee ComputatIonal IntellIgenCe magazIne

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - February 2018