Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

Friman, Ola; Volosyak, Ivan; Gräser, Axel

doi:10.1109/tbme.2006.889160

Cited by 489 publications

(397 citation statements)

References 30 publications

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“…The main idea of these methods is to exploit the information from multichannel EEG signals to generate more robust features which could improve the performance of the methods (Lin et al 2006;Friman et al 2007;Wang et al 2016;Zhang et al 2014a, b). But, they completely ignores the temporal information of the EEG signal.…”

Section: Discussionmentioning

confidence: 99%

“…This kind of methods could efficiently exploit multichannel EEG signals to extract more robust features, then achieve higher detection accuracy. The minimum energy combination (MEC) (Friman et al 2007), canonical correlation analysis (CCA) (Lin et al 2006) and multivariate synchronization index (MSI) (Zhang et al 2014a) are the mainly methods adopted in recent SSVEP-based BCI systems. The aim of MEC is to find optimized spatial filters to minimize the nuisance signals and noise as much as possible (Friman et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Zhang

Guo

et al. 2016

Cogn Neurodyn

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Multivariate synchronization index (MSI) has been proved to be an efficient method for frequency recognition in SSVEP-BCI systems. It measures the correlation according to the entropy of the normalized eigenvalues of the covariance matrix of multichannel signals. In the MSI method, the estimation of covariance matrix omits the temporally local structure of samples. In this study, a new spatio-temporal method, termed temporally local MSI (TMSI), was presented. This new method explicitly exploits temporally local information in modelling the covariance matrix. In order to evaluate the performance of the TMSI, we performs a comparison between the two methods on the real SSVEP datasets from eleven subjects. The results show that the TMSI outperforms the standard MSI. TMSI benefits from exploiting the temporally local structure of EEG signals, and could be a potential method for robust performance of SSVEP-based BCI.Keywords Steady-state visual evoked potential (SSVEP) Á Brain-computer interface (BCI) Á Multivariate synchronization index (MSI) Á Temporally local information

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Zhang

Guo

et al. 2016

Cogn Neurodyn

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“…Friman et al [15] proposed to apply the spatial filter W MEC ∈ R Ny×Ns , which minimizes the noise energy in S = YW MEC ∈ R N ×Ns , where N s ≤ N y . The noise here is defined as the difference between the original EEG signal and its best LS approximation in the subspace spanned by the SSVEP sinusoids.…”

Section: Minimum Energy Combination (Mec)mentioning

confidence: 99%

“…A test statistic can be derived from the filtered signals, to which we will refer as s mec (Y, X) and is obtained with [15] …”

Section: Minimum Energy Combination (Mec)mentioning

confidence: 99%

“…This was shown to provide reliable test statistics for SNR that can be used for SSVEP detection [15,19]. Based on the theoretical analysis of these state-of-the-art methods, we propose a new method, namely the canonical variates with autoregressive spectral analysis (CVARS) that estimates the signal and noise power levels from the canonical variates which leads to slight improvement in correct detection rates.…”

Section: Introductionmentioning

confidence: 99%

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Advancing the detection of steady-state visual evoked potentials in brain–computer interfaces

Abu-Alqumsan

Peer

2016

J. Neural Eng.

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Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain–computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

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Multilayer Feedforward Neural Network with Multi‐Valued Neurons for Brain–Computer Interfacing

Manyakov

Aizenberg

Chumerin

et al. 2013

Complex‐Valued Neural Networks

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In this chapter, we describe a multilayer feedforward neural network equipped with multi-valued neurons and its application to the domain of brain-computer interfacing (BCI). A new methodology for electroencephalogram (EEG)-based BCI is developed with which subjects can issue commands by looking at the corresponding targets that are flickering at the same frequency but with different initial phase. Two filter-based feature selection procedures are discussed for extracting relevant information from the phases estimated from the recorded EEGs. The proposed multichannel methodology is compared with existing single channel approaches and the results show that the former performs better in terms of accuracy and length of EEG interval considered for phase estimation. BRAIN-COMPUTER INTERFACE (BCI)Ever since it was acknowledged that the brain controls our intentions, motivations, decisions, muscles, and so on, theories were developed and experiments set up to better understand its neural origin. These efforts also led to the challenge to mimic Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing.

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Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

Cited by 489 publications

References 30 publications

Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Advancing the detection of steady-state visual evoked potentials in brain–computer interfaces

Multilayer Feedforward Neural Network with Multi‐Valued Neurons for Brain–Computer Interfacing

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