Exactly Periodic Spatial Filter for SSVEP Based BCIs
01 Oct 2018-pp 237-242
TL;DR: A novel, high accuracy, calibration less spatial filter for reliable steady-state visual evoked potential (SSVEP) extraction from noisy electroencephalogram (EEG) data called exactly periodic subspace decomposition (EPSD).
Abstract: This study introduces a novel, high accuracy, calibration less spatial filter for reliable steady-state visual evoked potential (SSVEP) extraction from noisy electroencephalogram (EEG) data. The proposed method, exactly periodic subspace decomposition (EPSD), utilises the periodic properties of the SSVEP components to achieve a robust spatial filter for SSVEP extraction. It tries to extract the SSVEP components by projecting the EEG data onto a subspace where only the target signal components are retained. The performance of the method was tested on an SSVEP dataset obtained from ten subjects and compared with common SSVEP spatial filtering and detection techniques. The results obtained from the study shows that EPSD consistently provides a significant improvement in detection performance than other SSVEP spatial filters used in brain-computer interface (BCI) applications.
References
More filters
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
"Exactly Periodic Spatial Filter for..." refers methods in this paper
...To overcome this, recent SSVEP detection methods such as Multiway CCA, Multiset CCA and IT-CCA [11], [12] rely on calibration data to obtain a reliable estimate of the SSVEP components to be used as robust references....
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TL;DR: A new class of techniques to identify periodicities in data that target the period estimation directly rather than inferring the period from the signal's spectrum, obtaining several advantages over the traditional spectrum estimation techniques such as DFT and MUSIC.
Abstract: In this paper, we propose a new class of techniques to identify periodicities in data. We target the period estimation directly rather than inferring the period from the signal’s spectrum. By doing so, we obtain several advantages over the traditional spectrum estimation techniques such as DFT and MUSIC. Apart from estimating the unknown period of a signal, we search for finer periodic structure within the given signal. For instance, it might be possible that the given periodic signal was actually a sum of signals with much smaller periods. For example, adding signals with periods 3, 7, and 11 can give rise to a period 231 signal. We propose methods to identify these “hidden periods” 3, 7, and 11. We first propose a new family of square matrices called Nested Periodic Matrices (NPMs), having several useful properties in the context of periodicity. These include the DFT, Walsh–Hadamard, and Ramanujan periodicity transform matrices as examples. Based on these matrices, we develop high dimensional dictionary representations for periodic signals. Various optimization problems can be formulated to identify the periods of signals from such representations. We propose an approach based on finding the least $l_{2}$ norm solution to an under-determined linear system. Alternatively, the period identification problem can also be formulated as a sparse vector recovery problem and we show that by a slight modification to the usual $l_{1}$ norm minimization techniques, we can incorporate a number of new and computationally simple dictionaries.
80 citations
"Exactly Periodic Spatial Filter for..." refers background in this paper
...Using such a reference signal for extracting SSVEP components leads to a suboptimal estimate if the length of the data (N ) is not a exact multiple of the period of interest (Tm = 1/fm) [16]....
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TL;DR: The detection and estimation of machine vibration multiperiodic signals of unknown periods in white Gaussian noise is investigated and the concept of exactly periodic signals is introduced.
Abstract: The detection and estimation of machine vibration multiperiodic signals of unknown periods in white Gaussian noise is investigated. New estimates for the subsignals (signals making up the received signal) and their periods are derived using an orthogonal subspace decomposition approach. The concept of exactly periodic signals is introduced. This in turn simplifies and enhances the understanding of periodic signals.
63 citations
"Exactly Periodic Spatial Filter for..." refers methods in this paper
...To obtain exactly periodic basis matrix we obtain basis matrices Ψ(2)1 and Ψ(1) and remove them from Ψ(4) [17]....
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...EPSD was introduced in [17] as a novel technique to generate exactly periodic orthogonal components corresponding to the signal of interest even when N is not a multiple of T ....
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23 Jun 2011
TL;DR: This paper proposes a taxonomy to categorize these methods and extensively evaluate them using 22 stimulation frequencies and suggests improvements to existing methods to increase the SSVEP detection performance.
Abstract: Focusing of attention on a repetitive visual stimulation (RVS) at a constant frequency, elicits the so called steady-state visual evoked potential (SSVEP). This effect can be advantageously utilized in brain-computer interfaces (BCIs). SSVEP based BCIs can offer higher bitrates and require shorter training time as compared to other BCI modalities. Detection of the SSVEP from the EEG can be facilitated through spatial filtering (linear combination of the signals recorded at several electrodes). Literature offers several options to perform this. In this paper we propose a taxonomy to categorize these methods and we extensively evaluate them using 22 stimulation frequencies. We suggest improvements to existing methods to increase the SSVEP detection performance. We also consider practical aspects in the discussion of results.
59 citations
"Exactly Periodic Spatial Filter for..." refers methods in this paper
...Commonly used spatial filtering methods in SSVEP BCIs include best bipolar combination (BCC), principal component analysis (PCA), minimum energy combination (MEC), maximum contrast combination (MCC), and partial least squares (PLS) spatial filter [8]....
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23 Jun 2011
TL;DR: Comparison of the performance of the two approaches for steady-state visual evoked potential detection through simulation data and real SSVEP data shows that CCA has lower deviation, higher accuracy and higher signal to noise ratio than MEC.
Abstract: Minimum energy combination (MEC) and canonical correlation analysis (CCA) are widely used for steady-state visual evoked potential (SSVEP) based brain computer interface (BCI), since both approaches have satisfactory performance. The purpose of this paper is to provide a guideline on choice of detection method, through comparison of the performance of the two approaches from simulation data and real SSVEP data. The experiment results show that CCA has lower deviation, higher accuracy and higher signal to noise ratio than MEC.
56 citations
"Exactly Periodic Spatial Filter for..." refers background or result in this paper
...It should be noted that the noise estimate is not used to normalise the obtained detection scores for all the spatial filters similar to [14]....
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...2 depicts that CCA provides higher accuracy compared to MEC which confirms results obtained by [14]....
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