Proceedings ArticleDOI
Toward Inexpensive and Practical Brain Computer Interface
Hamzah S. AlZu'bi,Nayel Al-Zubi,Waleed Al-Nuaimy +2 more
- pp 98-101
TLDR
This paper studies the feasibility of using inexpensive Electroencephalogram(EEG) device for BCI with asynchronous BCI mode which leads the control mechanism to become highly available for variety of users and a more natural way to communicate.Abstract:
Brain Computer Interface (BCI) introduces a new communication system that does not depend on brain's normal output pathways. This paper studies the feasibility of using inexpensive Electroencephalogram(EEG) device for BCI with asynchronous BCI mode which leads the control mechanism to become highly available for variety of users and a more natural way to communicate than the current BCI versions which depend on expensive EEG devices, which are less practical where they need special environment to run the BCI system, and use synchronous BCI mode in most cases. Benchmarking the results using Emotiv to another complex EEG device (BrainAmp) shows the reliability of such inexpensive devices.read more
Citations
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Journal ArticleDOI
A Survey of Interactive Systems based on Brain- Computer Interfaces
Alessandro Luiz Stamatto Ferreira,Leonardo Cunha de Miranda,Erica Esteves Cunha de Miranda,Sarah Gomes Sakamoto +3 more
TL;DR: Only noninvasive BCIs are addressed, since this kind of capture is the only one to not present risk to human health, and a discussion on challenges and future of this subject matter is discussed.
Journal ArticleDOI
Are low cost Brain Computer Interface headsets ready for motor imagery applications
TL;DR: It is shown that the performance of this headset is comparable to that found in professional devices when using the same number of sensors and sensor positions for a three status motor imagery cognitive process, implying an increase on the number of EEG headsets the researchers and manufacturers of BCI systems applied to motor imagery problems can integrate and a reduction of their cost.
Proceedings ArticleDOI
EEG based brain activity monitoring using Artificial Neural Networks
TL;DR: The presented thought recognition methodology is a three step process which utilizes SOM for unsupervised clustering of pre-processed EEG data and feed-forward Artificial Neural Networks (ANN) for classification.
Proceedings ArticleDOI
Human machine interaction via brain activity monitoring
Dumidu Wijayasekara,Milos Manic +1 more
TL;DR: The results show that while BCI control of a mobile robot is possible, precise movement required to guide a robot along a set path is difficult with the current setup.
Proceedings ArticleDOI
EEG feature selection for thought driven robots using evolutionary Algorithms
TL;DR: An initial step in the framework is presented, which is a methodology for optimal feature selection for abstract thought EEG data classification, and experimental results showed that the presented method outperformed the method without feature selection with a 10% or higher improvement in classification accuracy.
References
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Journal ArticleDOI
Brain-computer interface technology: a review of the first international meeting
Jonathan R. Wolpaw,Niels Birbaumer,W.J. Heetderks,Dennis J. McFarland,Paul Hunter Peckham,Gerwin Schalk,Emanuel Donchin,L.A. Quatrano,C.J. Robinson,C.J. Robinson,Theresa M. Vaughan +10 more
TL;DR: The first international meeting devoted to brain-computer interface research and development is summarized, which focuses on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users.
Journal ArticleDOI
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.
TL;DR: It is proposed that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.
Journal ArticleDOI
A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.
TL;DR: This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006, and asks what are the key signal processing components of a BCI, and what signal processing algorithms have been used in BCIs.
Journal ArticleDOI
EEG-based discrimination between imagination of right and left hand movement
TL;DR: By averaging over all training and over all feedback sessions, the EEG data revealed a significant desynchronisation (ERD) over the contralateral central area and synchronisation (ERS) overThe ipsilateral side over all sessions displayed a relatively small intra-subject variability with slight differences between sessions with and without feedback.
Journal ArticleDOI
Designing optimal spatial filters for single-trial EEG classification in a movement task
TL;DR: The effectiveness of the devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions is demonstrated, and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.