Exploiting the temporal structure of EEG data for SSVEP detection
01 Jan 2018-pp 1-4
TL;DR: In this paper, the authors applied periodic component analysis (nCA) for the extraction of SSVEP components from background electroencephalogram (EEG) data and compared it to standard canonical correlation analysis (CCA).
Abstract: Traditional multichannel detection algorithms use reference signals that are a generalisation of the steady-state visual evoked potential (SSVEP) components. This leads to the suboptimal performance of the algorithms. For the first time, periodic component analysis (nCA) has been applied for the extraction of SSVEP components from background electroencephalogram (EEG). Data from six test subjects were used to evaluate the proposed method and compare it to standard canonical correlation analysis (CCA). The results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction, and significantly outperforms traditional CCA even in low SNR conditions. The mean detection accuracy of nCA was higher than CCA across subjects, various window lengths and harmonics. The detection scores obtained from nCA provide reliable discrimination between control and idle states compared to CCA.
Citations
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TL;DR: periodic component analysis ( π CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise and provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost.
6 citations
TL;DR: In SSVEP applications with harmonic stimulation frequencies, the adaptive CCA has significantly improved the frequency recognition accuracy in comparison with the popularly standard CCA method.
Abstract: Steady-state visual evoked potential (SSVEP) is the brain’s response to quickly repetitive visual stimulus with a certain frequency. To increase the information transfer rate (ITR) in SSVEP-based systems, due to the frequency resolution restriction, we are forced to broaden the frequency range, which causes harmonic frequencies to come into the stimulation frequency range. Conventional canonical correlation analysis (CCA) may be associated with error for SSVEP frequency recognition at stimulation frequencies with harmonic relations. The number of harmonics considered to construct reference signals are determined adaptively; for frequencies whose second harmonic exists in the frequency range, two harmonics are used, and for other frequencies, just one harmonic is used. After constructing reference signals and recognizing the frequency corresponding to the maximum value of correlation by CCA, the target frequency is determined after a postprocessing step. Results show that for the 8-s time window length, the average classification accuracy for the adaptive CCA was 84%, while the corresponding values for the CCA with one harmonic (N = 1) and two harmonics (N = 2) were 78% and 74%, respectively. For 4-s length, this accuracy for the adaptive CCA was 86%, while it was 78% for both harmonic selection modes of the standard CCA, N = 1 and N = 2 . In SSVEP applications with harmonic stimulation frequencies, the adaptive CCA has significantly improved the frequency recognition accuracy in comparison with the popularly standard CCA method. The proposed method can be useful for SSVEP-based BCI systems that use broad ranges of stimulation frequencies with harmonic relation.
3 citations
Cites background from "Exploiting the temporal structure o..."
...presented periodic component analysis, which outperforms traditional CCA by providing discrimination between control and idle states [21]....
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References
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TL;DR: It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.
Abstract: Brain-computer interfaces (BCIs) are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. They are used in a broad field of applications nowadays. One important issue is the control of neuroprosthetic devices for the restoration of the grasp function in spinal-cord-injured people. In this communication, an asynchronous (self-paced) four-class BCI based on steady-state visual evoked potentials (SSVEPs) was used to control a two-axes electrical hand prosthesis. During training, four healthy participants reached an online classification accuracy between 44% and 88%. Controlling the prosthetic hand asynchronously, the participants reached a performance of 75.5 to 217.5 s to copy a series of movements, whereas the fastest possible duration determined by the setup was 64 s. The number of false negative (FN) decisions varied from 0 to 10 (the maximal possible decisions were 34). It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.
555 citations
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TL;DR: The current status and future prospects of BCI technology and its clinical applications are discussed, BCI is defined, the BCI-relevant signals from the human brain are reviewed, and the functional components of BCIs are described.
Abstract: Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.
439 citations
"Exploiting the temporal structure o..." refers background in this paper
...BCIs have the potential to drastically improve the quality of life of users with the loss of neuromuscular control due to motor neuron disorders (MNDs) and spinal injuries [1]....
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08 Oct 2013
TL;DR: An L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further and improves the recognition accuracy which is significantly higher than that of the MCCA.
Abstract: Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.
217 citations
"Exploiting the temporal structure o..." refers background in this paper
...SSVEP components are quasiperiodic in nature and exhibit inter-subject variability [5]....
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TL;DR: It is shown that the application of the generalized eigenvalue decomposition is an improved extension of conventional source separation techniques, specifically customized for ECG signals.
Abstract: In this letter, we propose the application of the generalized eigenvalue decomposition for the decomposition of multichannel electrocardiogram (ECG) recordings. The proposed method uses a modified version of a previously presented measure of periodicity and a phase-wrapping of the RR-interval, for extracting the ldquomost periodicrdquo linear mixtures of a recorded dataset. It is shown that the method is an improved extension of conventional source separation techniques, specifically customized for ECG signals. The method is therefore of special interest for the decomposition and compression of multichannel ECG, and for the removal of maternal ECG artifacts from fetal ECG recordings.
208 citations
"Exploiting the temporal structure o..." refers methods in this paper
...Eigenvectors corresponding to ′l′ minimum eigenvalues are used as the transformation vectors (Wπca) that maximizes the period corresponding to the frequency of interest in Y [9]....
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Proceedings Article•
01 Jan 2000
TL;DR: An eigenvalue method is developed for analyzing periodic structure in speech that avoids the inefficiencies of autocorrelation at the pitch period and correlates signals at a clock rate on the order of the actual pitch, as opposed to the original sampling rate.
Abstract: An eigenvalue method is developed for analyzing periodic structure in speech. Signals are analyzed by a matrix diagonalization reminiscent of methods for principal component analysis (PCA) and independent component analysis (ICA). Our method--called periodic component analysis (πCA)--uses constructive interference to enhance periodic components of the frequency spectrum and destructive interference to cancel noise. The front end emulates important aspects of auditory processing, such as cochlear filtering, nonlinear compression, and insensitivity to phase, with the aim of approaching the robustness of human listeners. The method avoids the inefficiencies of autocorrelation at the pitch period: it does not require long delay lines, and it correlates signals at a clock rate on the order of the actual pitch, as opposed to the original sampling rate. We derive its cost function and present some experimental results.
54 citations
"Exploiting the temporal structure o..." refers background or methods in this paper
...The phase gathered per time sample is given by, Δfm = 2π(fm/fs) [8]....
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...Periodic component analysis (πCA) uses the time structure of the data to isolate the components containing the period of interest [8]....
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