Avatar navigation in Second Life using brain signals
- 1 September 2013
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-7
Abstract
This paper presents an interdisciplinary project conducted in a sophisticated approach to set up a connection between the human brain and a virtual world illustration. The main purpose of this research is to develop an interaction technique for entertainment and to investigate the virtual reality aspects for physical rehabilitation. Users could freely explore Second Life's virtual world and steer their avatar to the target point using their brain signals. The experiment was carried out on three subjects. The control can be accomplished by online processing of single-trial EEG signals recorded from EPOC headset, Emotiv, and passing the parameters to the main interface. An adaptive filter-based approach was used to reduce the eye blink distortions. Time-frequency features with combination of an ensemble LDA classifier led to a maximum 87.2% of accuracy.Keywords
This publication has 12 references indexed in Scilit:
- Adapting the P300-Based Brain–Computer Interface for Gaming: A ReviewIEEE Transactions on Computational Intelligence and AI in Games, 2013
- How about taking a low‐cost, small, and wireless EEG for a walk?Psychophysiology, 2012
- Interactive computer play in rehabilitation of children with sensorimotor disorders: a systematic reviewDevelopmental Medicine & Child Neurology, 2009
- EEG Signal Processing2007
- Decoding two-dimensional movement trajectories using electrocorticographic signals in humansJournal of Neural Engineering, 2007
- What's Real About Virtual Reality?2005
- Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancyIEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
- Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humansProceedings of the National Academy of Sciences, 2004
- Stochastic time-frequency dictionaries for matching pursuitIEEE Transactions on Signal Processing, 2001
- Matching pursuits with time-frequency dictionariesIEEE Transactions on Signal Processing, 1993