TY - GEN
T1 - Evaluation of the feasibility of a novel distance adaptable steady-state visual evoked potential based brain-computer interface
AU - Wu, Chi-Hsu
AU - Lakany, Heba
N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has attracted great attention in BCI research due to its advantages over the other electroencephalography (EEG) based BCI paradigms, such as high speed, high signal to noise ratio, high accuracy, commands scalability and minimal user training time. Several studies have demonstrated that SSVEP BCI can provide a reliable channel to the users to communicate and control an external device. While most SSVEP based BCI studies focus on encoding the visual stimuli, enhancing the signal detection and improving the classification accuracy, there is a need to bridge the gap between BCI "bench" research and real world application. This study proposes a novel distance adaptable SSVEP based BCI paradigm which allows its users to operate the system in a range of viewing distances between the user and the visual stimulator. Unlike conventional SSVEP BCI where users can only operate the system at a fixed distance in front of the visual stimulator, users can operate the proposed BCI at a range of viewing distances. 10 healthy subjects participated in the experiment to evaluate the feasibility of the proposed SSVEP BCI. The visual stimulator was presented to the subjects at 4 viewing distances, 60cm, 150cm, 250cm and 350cm. The mean classification accuracy across the subjects and the viewing distances is over 75 The results demonstrate the feasibility of a distance adaptable SSVEP based BCI.
AB - Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has attracted great attention in BCI research due to its advantages over the other electroencephalography (EEG) based BCI paradigms, such as high speed, high signal to noise ratio, high accuracy, commands scalability and minimal user training time. Several studies have demonstrated that SSVEP BCI can provide a reliable channel to the users to communicate and control an external device. While most SSVEP based BCI studies focus on encoding the visual stimuli, enhancing the signal detection and improving the classification accuracy, there is a need to bridge the gap between BCI "bench" research and real world application. This study proposes a novel distance adaptable SSVEP based BCI paradigm which allows its users to operate the system in a range of viewing distances between the user and the visual stimulator. Unlike conventional SSVEP BCI where users can only operate the system at a fixed distance in front of the visual stimulator, users can operate the proposed BCI at a range of viewing distances. 10 healthy subjects participated in the experiment to evaluate the feasibility of the proposed SSVEP BCI. The visual stimulator was presented to the subjects at 4 viewing distances, 60cm, 150cm, 250cm and 350cm. The mean classification accuracy across the subjects and the viewing distances is over 75 The results demonstrate the feasibility of a distance adaptable SSVEP based BCI.
KW - electroencephalography
KW - visual stimulator
KW - visualization
KW - visual evoked potentials
KW - signal detection enhancement
KW - signal classification
KW - novel distance adaptable steady-state visual evoked potential
KW - medical signal processing
KW - light emitting diodes
KW - handicapped aids
UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7131936
U2 - 10.1109/NER.2015.7146559
DO - 10.1109/NER.2015.7146559
M3 - Conference contribution book
SN - 9781467363891
SP - 57
EP - 60
BT - 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
PB - IEEE
CY - Piscataway, NJ
ER -