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.

scholarly journals Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance

2021 ◽  
Vol 11 (4) ◽  
pp. 450
Author(s):  
Minglun Li ◽  
Dianning He ◽  
Chen Li ◽  
Shouliang Qi
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Visual Stimuli ◽  
Computer Interface ◽  
Visual Fatigue ◽  
Factors Influencing ◽  
Visual Evoked

The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain–computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.

scholarly journals Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report

2020 ◽  
Vol 08 (01) ◽  
pp. 40-52
Author(s):  
Nanlin Shi
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Information Transfer Rate ◽  
Lateral Sclerosis ◽  
Visual Evoked

This study applied a steady-state visual evoked potential (SSVEP) based brain–computer interface (BCI) to a patient in lock-in state with amyotrophic lateral sclerosis (ALS) and validated its feasibility for communication. The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient, achieving a maximum free-spelling accuracy above 90% and an information transfer rate of over 22.203 bits/min. A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design. The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.

scholarly journals Information Bottleneck as Optimisation Method for SSVEP-Based BCI

2021 ◽  
Vol 15 ◽  
Author(s):  
Anti Ingel ◽  
Raul Vicente
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Performance Measure ◽  
Classification Rule ◽  
Information Bottleneck ◽  
Visual Evoked

In this study, the information bottleneck method is proposed as an optimisation method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The information bottleneck is an information-theoretic optimisation method for solving problems with a trade-off between preserving meaningful information and compression. Its main practical application in machine learning is in representation learning or feature extraction. In this study, we use the information bottleneck to find optimal classification rule for a BCI. This is a novel application for the information bottleneck. This approach is particularly suitable for BCIs since the information bottleneck optimises the amount of information transferred by the BCI. Steady-state visual evoked potential-based BCIs often classify targets using very simple rules like choosing the class corresponding to the largest feature value. We call this classifier the arg max classifier. It is unlikely that this approach is optimal, and in this study, we propose a classification method specifically designed to optimise the performance measure of BCIs. This approach gives an advantage over standard machine learning methods, which aim to optimise different measures. The performance of the proposed algorithm is tested on two publicly available datasets in offline experiments. We use the standard power spectral density analysis (PSDA) and canonical correlation analysis (CCA) feature extraction methods on one dataset and show that the current approach outperforms most of the related studies on this dataset. On the second dataset, we use the task-related component analysis (TRCA) method and demonstrate that the proposed method outperforms the standard argmax classification rule in terms of information transfer rate when using a small number of classes. To our knowledge, this is the first time the information bottleneck is used in the context of SSVEP-based BCIs. The approach is unique in the sense that optimisation is done over the space of classification functions. It potentially improves the performance of BCIs and makes it easier to calibrate the system for different subjects.

Olfactory Modulation of Steady- State Visual Evoked Potential Topography in Comparison with Differences in Odor Sensitivity

2002 ◽  
Vol 16 (2) ◽  
pp. 71-81 ◽  
Author(s):  
Caroline M. Owen ◽  
John Patterson ◽  
Richard B. Silberstein
Keyword(s):  
Steady State ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Behavioral Responses ◽  
Simultaneous Recording ◽  
Odor Concentration ◽  
Odor Detection ◽  
Detection Response ◽  
Response Groups ◽  
Visual Evoked

Summary Research was undertaken to determine whether olfactory stimulation can alter steady-state visual evoked potential (SSVEP) topography. Odor-air and air-only stimuli were used to determine whether the SSVEP would be altered when odor was present. Comparisons were also made of the topographic activation associated with air and odor stimulation, with the view toward determining whether the revealed topographic activity would differentiate levels of olfactory sensitivity by clearly identifying supra- and subthreshold odor responses. Using a continuous respiration olfactometer (CRO) to precisely deliver an odor or air stimulus synchronously with the natural respiration, air or odor (n-butanol) was randomly delivered into the inspiratory airstream during the simultaneous recording of SSVEPs and subjective behavioral responses. Subjects were placed in groups based on subjective odor detection response: “yes” and “no” detection groups. In comparison to air, SSVEP topography revealed cortical changes in response to odor stimulation for both response groups, with topographic changes evident for those unable to perceive the odor, showing the presence of a subconscious physiological odor detection response. Differences in regional SSVEP topography were shown for those who reported smelling the odor compared with those who remained unaware of the odor. These changes revealed olfactory modulation of SSVEP topography related to odor awareness and sensitivity and therefore odor concentration relative to thresholds.

Forced-Choice Preferential Looking and Visual Evoked Potential Acuities of Visually Impaired Children

1992 ◽  
Vol 86 (1) ◽  
pp. 21-24 ◽  
Author(s):  
M.C. Bane ◽  
E.E. Birch
Keyword(s):  
Steady State ◽  
Success Rate ◽  
Visually Impaired ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Forced Choice ◽  
Preferential Looking ◽  
Impaired Children ◽  
Visually Impaired Children ◽  
Visual Evoked

In the authors’ previous study, the success rate for forced-choice preferential looking (FPL) with preverbal visually impaired children was higher than that with pattern visual evoked potential (VEP). The current study sought to increase the VEP success rate and to improve agreement between the FPL and the VEP acuity estimates using horizontal-bar stimuli for children with nystagmus and steady-state presentation for those without nystagmus.

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