06-07-2012, 12:57 PM
Design and Implementation of a Brain-Computer Interface With High Transfer Rates
[attachment=26966]
INTRODUCTION
Abrain-computer interface (BCI) is a communication
channel connecting the brain to a computer or another
electronic device. The intrinsic feature of a BCI is that it does
not depend on the brain’s normal output pathways of peripheral
nerves and muscles [1]. Two basic requirements are met for
a communication channel between the brain and computer:
1) features that are useful to distinguish several kinds of
brain state; 2) methods for the detection and classification of
such features implemented in real time. Various techniques
are now available to monitor brain function, e.g., electroencephalography
(EEG), magnetoencephalography, functional
magnetic resonance imaging, and positron emission tomography.
The latter three techniques are technically demanding
and expensive. At present, EEG is the optimal choice for BCI
implementation.
METHODS
A. Scientific Background
VEPs reflect, at least to some extent, the electrophysiological
mechanisms underlying the processing of visual information in
the brain. The signals are always in response to changes in the
stimulus. A static stimulus in the visual field does not appear
to effect any significant alterations in EEG activity. The signals
evoked by changes in the visual input have been shown to reflect
certain properties of the stimulus [10].
A distinction is made between transient VEP and SSVEP
based on the stimulation frequency. The former arises when the
stimulation frequency is less than 2 Hz. If the repetition rate of
the stimulus is higher than 6 Hz, however, a periodic response
called the SSVEP will result. It is composed of a series of components
whose frequencies are exact integer multiples of the
repetition frequency. The amplitude and phase of the SSVEP are
highly sensitive to stimulus parameters such as repetition rate,
contrast or modulation depth, and spatial frequency [19]. Fig. 1
shows the amplitude spectrum of 7 Hz-induced SSVEP. Three
peaks at 7, 14, and 21 Hz can be found clearly.
RESULTS
In the first task, eight of the thirteen subjects succeeded in
ringing the mobile phone, the others failed. No false positives
occurred for any subjects. In the second task, the average
transfer rate over all subjects was 27.15 bits/min. The results
could be classified into three grades.
[attachment=26966]
INTRODUCTION
Abrain-computer interface (BCI) is a communication
channel connecting the brain to a computer or another
electronic device. The intrinsic feature of a BCI is that it does
not depend on the brain’s normal output pathways of peripheral
nerves and muscles [1]. Two basic requirements are met for
a communication channel between the brain and computer:
1) features that are useful to distinguish several kinds of
brain state; 2) methods for the detection and classification of
such features implemented in real time. Various techniques
are now available to monitor brain function, e.g., electroencephalography
(EEG), magnetoencephalography, functional
magnetic resonance imaging, and positron emission tomography.
The latter three techniques are technically demanding
and expensive. At present, EEG is the optimal choice for BCI
implementation.
METHODS
A. Scientific Background
VEPs reflect, at least to some extent, the electrophysiological
mechanisms underlying the processing of visual information in
the brain. The signals are always in response to changes in the
stimulus. A static stimulus in the visual field does not appear
to effect any significant alterations in EEG activity. The signals
evoked by changes in the visual input have been shown to reflect
certain properties of the stimulus [10].
A distinction is made between transient VEP and SSVEP
based on the stimulation frequency. The former arises when the
stimulation frequency is less than 2 Hz. If the repetition rate of
the stimulus is higher than 6 Hz, however, a periodic response
called the SSVEP will result. It is composed of a series of components
whose frequencies are exact integer multiples of the
repetition frequency. The amplitude and phase of the SSVEP are
highly sensitive to stimulus parameters such as repetition rate,
contrast or modulation depth, and spatial frequency [19]. Fig. 1
shows the amplitude spectrum of 7 Hz-induced SSVEP. Three
peaks at 7, 14, and 21 Hz can be found clearly.
RESULTS
In the first task, eight of the thirteen subjects succeeded in
ringing the mobile phone, the others failed. No false positives
occurred for any subjects. In the second task, the average
transfer rate over all subjects was 27.15 bits/min. The results
could be classified into three grades.