17,678 research outputs found
EMBEDDED LEARNING ROBOT WITH FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR
Fuzzy Q-learning is extending of Q-learning algorithm that uses fuzzy inference system to enable Q-learning holding continuous action and state. This learning has been implemented in various robot learning application like obstacle avoidance and target searching. However, most of them have not been realized in embedded robot. This paper presents implementation of fuzzy Q-learning for obstacle avoidance navigation in embedded mobile robot. The experimental result demonstrates that fuzzy Q-learning enables robot to be able to learn the right policy i.e. to avoid obstacle
In-ear EEG biometrics for feasible and readily collectable real-world person authentication
The use of EEG as a biometrics modality has been investigated for about a
decade, however its feasibility in real-world applications is not yet
conclusively established, mainly due to the issues with collectability and
reproducibility. To this end, we propose a readily deployable EEG biometrics
system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor
(collectability), which does not require skilled assistance or cumbersome
preparation. Unlike most existing studies, we consider data recorded over
multiple recording days and for multiple subjects (reproducibility) while, for
rigour, the training and test segments are not taken from the same recording
days. A robust approach is considered based on the resting state with eyes
closed paradigm, the use of both parametric (autoregressive model) and
non-parametric (spectral) features, and supported by simple and fast cosine
distance, linear discriminant analysis and support vector machine classifiers.
Both the verification and identification forensics scenarios are considered and
the achieved results are on par with the studies based on impractical on-scalp
recordings. Comprehensive analysis over a number of subjects, setups, and
analysis features demonstrates the feasibility of the proposed ear-EEG
biometrics, and its potential in resolving the critical collectability,
robustness, and reproducibility issues associated with current EEG biometrics
Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
Independent component analysis (ICA) has proven useful for modeling brain and
electroencephalographic (EEG) data. Here, we present a new, generalized method
to better capture the dynamics of brain signals than previous ICA algorithms.
We regard EEG sources as eliciting spatio-temporal activity patterns,
corresponding to, e.g., trajectories of activation propagating across cortex.
This leads to a model of convolutive signal superposition, in contrast with the
commonly used instantaneous mixing model. In the frequency-domain, convolutive
mixing is equivalent to multiplicative mixing of complex signal sources within
distinct spectral bands. We decompose the recorded spectral-domain signals into
independent components by a complex infomax ICA algorithm. First results from a
visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics
in the data, (2) links to subject behavior, (3) sources with a limited spectral
extent, and (4) a higher degree of independence compared to sources derived by
standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor
typo
The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents
No description supplie
Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
We propose a fusion approach that combines features from simultaneously
recorded electroencephalographic (EEG) and magnetoencephalographic (MEG)
signals to improve classification performances in motor imagery-based
brain-computer interfaces (BCIs). We applied our approach to a group of 15
healthy subjects and found a significant classification performance enhancement
as compared to standard single-modality approaches in the alpha and beta bands.
Taken together, our findings demonstrate the advantage of considering
multimodal approaches as complementary tools for improving the impact of
non-invasive BCIs
Prefrontal cortex activation upon a demanding virtual hand-controlled task: A new frontier for neuroergonomics
open9noFunctional near-infrared spectroscopy (fNIRS) is a non-invasive vascular-based functional neuroimaging technology that can assess, simultaneously from multiple cortical areas, concentration changes in oxygenated-deoxygenated hemoglobin at the level of the cortical microcirculation blood vessels. fNIRS, with its high degree of ecological validity and its very limited requirement of physical constraints to subjects, could represent a valid tool for monitoring cortical responses in the research field of neuroergonomics. In virtual reality (VR) real situations can be replicated with greater control than those obtainable in the real world. Therefore, VR is the ideal setting where studies about neuroergonomics applications can be performed. The aim of the present study was to investigate, by a 20-channel fNIRS system, the dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) in subjects while performing a demanding VR hand-controlled task (HCT). Considering the complexity of the HCT, its execution should require the attentional resources allocation and the integration of different executive functions. The HCT simulates the interaction with a real, remotely-driven, system operating in a critical environment. The hand movements were captured by a high spatial and temporal resolution 3-dimensional (3D) hand-sensing device, the LEAP motion controller, a gesture-based control interface that could be used in VR for tele-operated applications. Fifteen University students were asked to guide, with their right hand/forearm, a virtual ball (VB) over a virtual route (VROU) reproducing a 42 m narrow road including some critical points. The subjects tried to travel as long as possible without making VB fall. The distance traveled by the guided VB was 70.2 ± 37.2 m. The less skilled subjects failed several times in guiding the VB over the VROU. Nevertheless, a bilateral VLPFC activation, in response to the HCT execution, was observed in all the subjects. No correlation was found between the distance traveled by the guided VB and the corresponding cortical activation. These results confirm the suitability of fNIRS technology to objectively evaluate cortical hemodynamic changes occurring in VR environments. Future studies could give a contribution to a better understanding of the cognitive mechanisms underlying human performance either in expert or non-expert operators during the simulation of different demanding/fatiguing activities.openCarrieri, Marika; Petracca, Andrea; Lancia, Stefania; Basso Moro, Sara; Brigadoi, Sabrina; Spezialetti, Matteo; Ferrari, Marco; Placidi, Giuseppe; Quaresima, ValentinaCarrieri, Marika; Petracca, Andrea; Lancia, Stefania; BASSO MORO, Sara; Brigadoi, Sabrina; Spezialetti, Matteo; Ferrari, Marco; Placidi, Giuseppe; Quaresima, Valentin
Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot
In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio
Exploiting short-term memory in soft body dynamics as a computational resource
Soft materials are not only highly deformable but they also possess rich and
diverse body dynamics. Soft body dynamics exhibit a variety of properties,
including nonlinearity, elasticity, and potentially infinitely many degrees of
freedom. Here we demonstrate that such soft body dynamics can be employed to
conduct certain types of computation. Using body dynamics generated from a soft
silicone arm, we show that they can be exploited to emulate functions that
require memory and to embed robust closed-loop control into the arm. Our
results suggest that soft body dynamics have a short-term memory and can serve
as a computational resource. This finding paves the way toward exploiting
passive body dynamics for control of a large class of underactuated systems.Comment: 22 pages, 11 figures; email address correcte
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