488 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
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 correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm
Dementia in the elderly has recently become the most usual cause of cognitive
decline. The proliferation of dementia cases in aging societies creates a
remarkable economic as well as medical problems in many communities worldwide.
A recently published report by The World Health Organization (WHO) estimates
that about 47 million people are suffering from dementia-related neurocognitive
declines worldwide. The number of dementia cases is predicted by 2050 to
triple, which requires the creation of an AI-based technology application to
support interventions with early screening for subsequent mental wellbeing
checking as well as preservation with digital-pharma (the so-called beyond a
pill) therapeutical approaches. We present an attempt and exploratory results
of brain signal (EEG) classification to establish digital biomarkers for
dementia stage elucidation. We discuss a comparison of various machine learning
approaches for automatic event-related potentials (ERPs) classification of a
high and low task-load sound stimulus recognition. These ERPs are similar to
those in dementia. The proposed winning method using tensor-based machine
learning in a deep fully connected neural network setting is a step forward to
develop AI-based approaches for a subsequent application for subjective- and
mild-cognitive impairment (SCI and MCI) diagnostics.Comment: In ICASSP 2019 - 2019 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), pp. 8578-8582, May 201
Utilizing Visual Attention and Inclination to Facilitate Brain-Computer Interface Design in an Amyotrophic Lateral Sclerosis Sample
Individuals who suffer from amyotrophic lateral sclerosis (ALS) have a loss of motor control and possibly the loss of speech. A brain-computer interface (BCI) provides a means for communication through nonmuscular control. Visual BCIs have shown the highest potential when compared to other modalities; nonetheless, visual attention concepts are largely ignored during the development of BCI paradigms. Additionally, individual performance differences and personal preference are not considered in paradigm development. The traditional method to discover the best paradigm for the individual user is trial and error. Visual attention research and personal preference provide the building blocks and guidelines to develop a successful paradigm. This study is an examination of a BCI-based visual attention assessment in an ALS sample. This assessment takes into account the individual’s visual attention characteristics, performance, and personal preference to select a paradigm. The resulting paradigm is optimized to the individual and then tested online against the traditional row-column paradigm. The optimal paradigm had superior performance and preference scores over row-column. These results show that the BCI needs to be calibrated to individual differences in order to obtain the best paradigm for an end user
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