Journal ArticleDOI
Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components
TLDR
This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations and revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics.Abstract:
Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.read more
Citations
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Journal ArticleDOI
Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.
TL;DR: The steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation are investigated and future research directions related to basic and applied aspects of SSVEPs are outlined.
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
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
TL;DR: A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI) that were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.
Journal ArticleDOI
Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general
TL;DR: A unifying categorization of BCI-based applications, including the novel approach of passive BCI is proposed, which focuses on applications for healthy users, and the specific requirements and demands of this user group.
Journal ArticleDOI
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method
TL;DR: The positive characteristics of the proposed SSVEP-based BCI system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.
References
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Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
Brain-computer interfaces for communication and control.
Jonathan R. Wolpaw,Jonathan R. Wolpaw,Niels Birbaumer,Niels Birbaumer,Dennis J. McFarland,Gert Pfurtscheller,Theresa M. Vaughan +6 more
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
Journal ArticleDOI
Event-related EEG/MEG synchronization and desynchronization: basic principles.
TL;DR: Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously.
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
Motor imagery and direct brain-computer communication
TL;DR: At this time, a tetraplegic patient is able to operate an EEG-based control of a hand orthosis with nearly 100% classification accuracy by mental imagination of specific motor commands.