1,053 research outputs found
Student Teaching and Research Laboratory Focusing on Brain-computer Interface Paradigms - A Creative Environment for Computer Science Students -
This paper presents an applied concept of a brain-computer interface (BCI)
student research laboratory (BCI-LAB) at the Life Science Center of TARA,
University of Tsukuba, Japan. Several successful case studies of the student
projects are reviewed together with the BCI Research Award 2014 winner case.
The BCI-LAB design and project-based teaching philosophy is also explained.
Future teaching and research directions summarize the review.Comment: 4 pages, 4 figures, accepted for EMBC 2015, IEEE copyrigh
EEG Signal Processing and Classification for the Novel Tactile-Force Brain-Computer Interface Paradigm
The presented study explores the extent to which tactile-force stimulus
delivered to a hand holding a joystick can serve as a platform for a brain
computer interface (BCI). The four pressure directions are used to evoke
tactile brain potential responses, thus defining a tactile-force brain computer
interface (tfBCI). We present brain signal processing and classification
procedures leading to successful interfacing results. Experimental results with
seven subjects performing online BCI experiments provide a validation of the
hand location tfBCI paradigm, while the feasibility of the concept is
illuminated through remarkable information-transfer rates.Comment: 6 pages (in conference proceedings original version); 6 figures,
submitted to The 9th International Conference on Signal Image Technology &
Internet Based Systems, December 2-5, 2013, Kyoto, Japan; to be available at
IEEE Xplore; IEEE Copyright 201
Novel Virtual Moving Sound-based Spatial Auditory Brain-Computer Interface Paradigm
This paper reports on a study in which a novel virtual moving sound-based
spatial auditory brain-computer interface (BCI) paradigm is developed. Classic
auditory BCIs rely on spatially static stimuli, which are often boring and
difficult to perceive when subjects have non-uniform spatial hearing perception
characteristics. The concept of moving sound proposed and tested in the paper
allows for the creation of a P300 oddball paradigm of necessary target and
non-target auditory stimuli, which are more interesting and easier to
distinguish. We present a report of our study of seven healthy subjects, which
proves the concept of moving sound stimuli usability for a novel BCI. We
compare online BCI classification results in static and moving sound paradigms
yielding similar accuracy results. The subject preference reports suggest that
the proposed moving sound protocol is more comfortable and easier to
discriminate with the online BCI.Comment: 4 pages (in conference proceedings original version); 6 figures,
accepted at 6th International IEEE EMBS Conference on Neural Engineering,
November 6-8, 2013, Sheraton San Diego Hotel & Marina, San Diego, CA; paper
ID 465; to be available at IEEE Xplore; IEEE Copyright 201
Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review
A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance
Multi-command Tactile Brain Computer Interface: A Feasibility Study
The study presented explores the extent to which tactile stimuli delivered to
the ten digits of a BCI-naive subject can serve as a platform for a brain
computer interface (BCI) that could be used in an interactive application such
as robotic vehicle operation. The ten fingertips are used to evoke
somatosensory brain responses, thus defining a tactile brain computer interface
(tBCI). Experimental results on subjects performing online (real-time) tBCI,
using stimuli with a moderately fast inter-stimulus-interval (ISI), provide a
validation of the tBCI prototype, while the feasibility of the concept is
illuminated through information-transfer rates obtained through the case study.Comment: Haptic and Audio Interaction Design 2013, Daejeon, Korea, April
18-19, 2013, 15 pages, 4 figures, The final publication will be available at
link.springer.co
Head-related Impulse Response Cues for Spatial Auditory Brain-computer Interface
This study provides a comprehensive test of a head-related impulse response
(HRIR) cues for a spatial auditory brain-computer interface (saBCI) speller
paradigm. We present a comparison with the conventional virtual sound
headphone-based spatial auditory modality. We propose and optimize the three
types of sound spatialization settings using a variable elevation in order to
evaluate the HRIR efficacy for the saBCI. Three experienced and seven naive BCI
users participated in the three experimental setups based on ten presented
Japanese syllables. The obtained EEG auditory evoked potentials (AEP) resulted
with encouragingly good and stable P300 responses in online BCI experiments.
Our case study indicated that users could perceive elevation in the saBCI
experiments generated using the HRIR measured from a general head model. The
saBCI accuracy and information transfer rate (ITR) scores have been improved
comparing to the classical horizontal plane-based virtual spatial sound
reproduction modality, as far as the healthy users in the current pilot study
are concerned.Comment: 4 pages, 4 figures, accepted for EMBC 2015, IEEE copyrigh
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
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