Multi-command SSVEP-based BCI system via single flickering frequency half-field stimulation pattern

Punsawad, Yunyong; Wongsawat, Yodchanan

doi:10.1109/iembs.2011.6090257

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…Several recent studies have proposed a half-field stimulation pattern based on the brain mechanism of visual selective attention [48–51]. The user is expected to concentrate their eyes on a fixation point in the middle of two flickers modulated to specific frequencies.…”

Section: Interface Paradigm Designmentioning

confidence: 99%

Enhancing the Usability of Brain-Computer Interface Systems

Baek

Chang

Heo³

et al. 2019

Computational Intelligence and Neuroscience

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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.

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Section: Interface Paradigm Designmentioning

confidence: 99%

Enhancing the Usability of Brain-Computer Interface Systems

Baek

Chang

Heo³

et al. 2019

Computational Intelligence and Neuroscience

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“…The wheelchair control developed by S. M. T. Muller et al [14] provided an average classifier accuracy of 73% for four volunteers with hit rate of 60-100% during online experiment with visual feedback. A multi command SSVEP based BCI system using half-field stimulation method and thresholding algorithm for classification developed by Y. Punsawad, Y. Wongsawat [15] resulted with an average accuracy of 77% over four subjects. SSVEP based control with OAA-SVM developed in our study had got an average classification accuracy of 88.55% over 10 subjects.…”

Section: Resultsmentioning

confidence: 99%

Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications

Singla¹

2014

IJIEE

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In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attentions. This study tries to develop a classifier, which can provide higher classification accuracy for multiclass SSVEP data. Four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 second window and features were extracted using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass Support Vector Machines (SVM) is compared with Artificial Neural Network (ANN) models on the basis of SSVEP classifier accuracies. OAA SVM classifier had got an average accuracy of 88.55% for SSVEP classification over 10 subjects. Based on this study, it is found that for SSVEP classification OAA-SVM classifier can provide better results

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“…Many studies have shown that the best locations to detect and acquire SSVEPs are the occipital areas of the brain [8]- [10]. The EEG signals were measured from two bipolar channels O1-Pz and O2-Pz placed in 10/20 international system ( Fig.…”

Section: Methodsmentioning

confidence: 99%

A comparison of success and time consumption of the most common methods for detection of the SSVEP

Šmondrk

Vozda

Krohová

et al. 2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

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The steady state visual evoked potential (SSVEP) is one of the most discussed method for the brain computer interface (BCI). In this paper, performance of the online SSVEP detection methods was compared. Two parametric and two nonparametric algorithms were used: the classical power spectrum estimation method (PS), the power spectrum estimation using the AR model, the canonical correlation analysis (CCA) and the continuous wavelet transform (CWT). The success of the SSVEP detection and the time consumption of each method were compared.Keywords-Steady-state visual evoked potential, brain computer interface, electroencephalogram, canonical correlation analysis, power spectrum estimation method, continuous wavelet transform

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Multi-command SSVEP-based BCI system via single flickering frequency half-field stimulation pattern

Cited by 10 publications

References 12 publications

Enhancing the Usability of Brain-Computer Interface Systems

Enhancing the Usability of Brain-Computer Interface Systems

Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications

A comparison of success and time consumption of the most common methods for detection of the SSVEP

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