scholarly journals A BCI Gaze Sensing Method Using Low Jitter Code Modulated VEP

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3797
Author(s):  
Ibrahim Kaya ◽  
Jorge Bohórquez ◽  
Özcan Özdamar
Keyword(s):  
Steady State ◽  
Template Matching ◽  
Information Transfer ◽  
Roc Curves ◽  
Simultaneous Generation ◽  
Transfer Rates ◽  
Low Jitter ◽  
Roc Area ◽  
Generation Mechanisms ◽  
Pattern Display

Visual evoked potentials (VEPs) are used in clinical applications in ophthalmology, neurology, and extensively in brain–computer interface (BCI) research. Many BCI implementations utilize steady-state VEP (SSVEP) and/or code modulated VEP (c-VEP) as inputs, in tandem with sophisticated methods to improve information transfer rates (ITR). There is a gap in knowledge regarding the adaptation dynamics and physiological generation mechanisms of the VEP response, and the relation of these factors with BCI performance. A simple, dual pattern display setup was used to evoke VEPs and to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50, and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits quasi-steady-state VEPs (QSS-VEPs) that are utilized for the simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs, and it is assumed that it shares similar generators of the SSVEPs. Eight subjects were recorded, and the performance of the overall system was analyzed using receiver operating characteristic (ROC) curves, accuracy plots, and ITRs. In summary, QSS-VEPs performed better than transient VEPs (TR-VEP). It was found that in general, 32 rps stimulation had the highest ROC area, accuracy, and ITRs. Moreover, QSS-VEPs were found to lead to higher accuracy by template matching compared to SSVEPs at 32 rps. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated.

scholarly journals BCI Gaze Sensing Method Using Low Jitter Code Modulated VEP

2019 ◽  
Author(s):  
Ibrahim Kaya ◽  
Jorge Bohorquez ◽  
Ozcan Ozdamar
Keyword(s):  
Steady State ◽  
Template Matching ◽  
Information Transfer ◽  
Roc Curves ◽  
Simultaneous Generation ◽  
Transfer Rates ◽  
Low Jitter ◽  
Roc Area ◽  
Generation Mechanisms ◽  
Better Than

Visual Evoked Potentials (VEPs) are used in clinical applications in ophthalmology, neurology and extensively in brain computer interface (BCI) research. BCI literature covers steady state VEP (SSVEP) and code modulated VEP (c-VEP) BCIs along with sophisticated methods to improve information transfer rates (ITR). There is a gap of knowledge regarding the VEP adaptation dynamics, physiological generation mechanisms and relation with BCI performance. A simple dual display VEP switch was developed to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50 and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits Quasi-Steady-State VEPs (QSS-VEPs) that are utilized for simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs and it is assumed that it shares the similar generators of the SSVEPs. Eight subjects were recorded and the performance of the overall system was analyzed by means of Receiver Operating Characteristic (ROC) curves, accuracy plots and ITRs. In summary QSS-VEPs performed better than transient VEPs. It was found that in general 32rps stimulation had the highest ROC area, accuracy and ITRs in general. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated. Moreover, QSS-VEPs were found to lead to higher accuracy by the template matching compared to SSVEPs at 10rps and 32rps.

scholarly journals Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5309
Author(s):  
Akira Ikeda ◽  
Yoshikazu Washizawa
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Extraction Methods ◽  
Stimulation Frequency ◽  
Event Related Potential ◽  
Spectrum Estimation ◽  
Complex Valued ◽  
Visual Evoked

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

Receiver operating characteristic (ROC) analysis of neurons in the cat's lateral geniculate nucleus during tonic and burst response mode

1995 ◽  
Vol 12 (4) ◽  
pp. 723-741 ◽  
Author(s):  
W. Guido ◽  
S.-M. Lu ◽  
J.W. Vaughan ◽  
Dwayne W. Godwin ◽  
S. Murray Sherman
Keyword(s):  
Visual Stimulus ◽  
Action Potentials ◽  
Operating Characteristic ◽  
Visual Stimuli ◽  
Roc Curves ◽  
Response Mode ◽  
Burst Mode ◽  
Low Threshold Spike ◽  
Low Threshold ◽  
Roc Area

AbstractRelay cells of the lateral geniculate nucleus respond to visual stimuli in one of two modes: burst and tonic. The burst mode depends on the activation of a voltage-dependent, Ca2+ conductance underlying the low threshold spike. This conductance is inactivated at depolarized membrane potentials, but when activated from hyperpolarized levels, it leads to a large, triangular, nearly all-or-none depolarization. Typically, riding its crest is a high-frequency barrage of action potentials. Low threshold spikes thus provide a nonlinear amplification allowing hyperpolarized relay neurons to respond to depolarizing inputs, including retinal EPSPs. In contrast, the tonic mode is characterized by a steady stream of unitary action potentials that more linearly reflects the visual stimulus. In this study, we tested possible differences in detection between response modes of 103 geniculate neurons by constructing receiver operating characteristic (ROC) curves for responses to visual stimuli (drifting sine-wave gratings and flashing spots). Detectability was determined from the ROC curves by computing the area under each curve, known as the ROC area. Most cells switched between modes during recording, evidently due to small shifts in membrane potential that affected the activation state of the low threshold spike. We found that the more often a cell responded in burst mode, the larger its ROC area. This was true for responses to optimal and nonoptimal visual stimuli, the latter including nonoptimal spatial frequencies and low stimulus contrasts. The larger ROC areas associated with burst mode were due to a reduced spontaneous activity and roughly equivalent level of visually evoked response when compared to tonic mode. We performed a within-cell analysis on a subset of 22 cells that switched modes during recording. Every cell, whether tested with a low contrast or high contrast visual stimulus exhibited a larger ROC area during its burst response mode than during its tonic mode. We conclude that burst responses better support signal detection than do tonic responses. Thus, burst responses, while less linear and perhaps less useful in providing a detailed analysis of visual stimuli, improve target detection. The tonic mode, with its more linear response, seems better suited for signal analysis rather than signal detection.

scholarly journals A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 891 ◽  
Author(s):  
Malik M. Naeem Mannan ◽  
M. Ahmad Kamran ◽  
Shinil Kang ◽  
Hak Soo Choi ◽  
Myung Yung Jeong
Keyword(s):  
Eye Tracking ◽  
Classification Accuracy ◽  
Information Transfer ◽  
High Speed ◽  
Communication Channel ◽  
Computer Interface ◽  
Stimuli Responsive ◽  
Transfer Rates ◽  
Computer Interfaces ◽  
Large Numbers

Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

scholarly journals Enhancing the Usability of Brain-Computer Interface Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hyun Jae Baek ◽  
Min Hye Chang ◽  
Jeong Heo ◽  
Kwang Suk Park
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Signal Acquisition ◽  
Information Transfer Rate ◽  
Wide Range ◽  
First Results ◽  
And Performance ◽  
Improving Accuracy ◽  
User Friendly ◽  
Interface Designs

Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.

Optimizing information‐transfer rates for human communication

2006 ◽  
Vol 119 (5) ◽  
pp. 3237-3237
Author(s):  
Charlotte M. Reed ◽  
Nathaniel I. Durlach ◽  
Hong Z. Tan
Keyword(s):  
Information Transfer ◽  
Human Communication ◽  
Transfer Rates

scholarly journals Brain–Computer Interface Spellers for Communication: Why We Need to Address Their Security and Authenticity

2020 ◽  
Vol 10 (3) ◽  
pp. 139
Author(s):  
Anirban Dutta
Keyword(s):  
Research And Development ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Computer Interfaces ◽  
Transfer Rates ◽  
Computer Interfaces ◽  
Significant Research

Brain–Computer Interfaces (BCI) have witnessed significant research and development in the last 20 years where the main aim was to improve their accuracy and increase their information transfer rates (ITRs), while still making them portable and easy to use by a broad range of users [...]

scholarly journals The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli

2020 ◽  
Vol 10 (9) ◽  
pp. 616 ◽  
Author(s):  
Lu Wang ◽  
Dan Han ◽  
Binbin Qian ◽  
Zhenhao Zhang ◽  
Zhijun Zhang ◽  
...  
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Recognition Rate ◽  
Frequency Allocation ◽  
Cognitive Studies ◽  
Computer Interfaces ◽  
Information Transfer Rate ◽  
Input Signals ◽  
Specific Frequency ◽  
Visual Evoked

Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain–computer interfaces (BCIs). However, whether SSVEP can be used as a reliable indicator has been a controversial issue. We focused on the independence of SSVEP from frequency allocation and number of stimuli. First, a cue–target paradigm was adopted to examine the interaction between SSVEPs evoked by two stimuli with different frequency allocations under different attention conditions. Second, we explored whether signal strength and the performance of SSVEP-based BCIs were affected by the number of stimuli. The results revealed that no significant interaction of SSVEP responses appeared between attended and unattended stimuli under various frequency allocations, regardless of their appearance in the fundamental or second-order harmonic. The amplitude of SSVEP suffered no significant gain or loss under different numbers of stimuli, but the performance of SSVEP-based BCIs varied along with duration of stimuli; that is, the recognition rate was not affected by the number of stimuli when the duration of stimuli was long enough, while the information transfer rate (ITR) presented the opposite trend. It can be concluded that SSVEP is a reliable tool for marking and monitoring multiple stimuli simultaneously in cognitive studies, but much caution should be taken when choosing a suitable duration and the number of stimuli, in order to achieve optimal utility of BCIs in the future.

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