Chapter

Spatial filtering techniques for improving individual template-based SSVEP detection

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Abstract

In the past decade, the performance of brain-computer interfaces based on steady-state visual evoked potentials (SSVEPs) has been significantly improved due to advances in signal analysis algorithms. For example, efficient target-identification methods based on template matching, in which individual templates are obtained by averaging the training data across trials, have been proposed to improve the performance of SSVEP detection. In template-based methods, spatial filtering plays an important role in improving the performance by enhancing the signal-to-noise ratio of SSVEPs. In conventional studies, several spatial-filtering approaches have been introduced for electroencephalogram analysis. However, the optimal spatial-filtering approach for individual template-based SSVEP detection still remains unknown. This chapter reviews the spatial-filtering approaches for improving the template-based SSVEP detection and evaluates their performance through a direct comparison using a benchmark dataset of SSVEPs.

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... In addition, the classification accuracy of the frequency-coded SSVEPs was estimated by the well-established target identification algorithms. In particular, the canonical correlation analysis (CCA)based method [20] and the individual-template-based method incorporated with five spatial filtering techniques [21] were employed as calibration-free and fully-calibrated A c c e p t e d M a n u s c r i p t approaches, respectively. Importantly, this study only employed the aforementioned popularly-used algorithms and spatial filtering techniques for ease in understanding and comparing the results with previous studies. ...
... In fully-calibrated methods, individualized templates χ n ∈ R Nc×Ns obtained by averaging multiple training trials as (χ n ) jk = 1 Nt Nt h=1 (χ n ) jkh are employed to better characterize SSVEPs than the computer-generated models [33,34,35]. It has also been proven that spatial filtering techniques are effective to enhance the performance of the fully-calibrated methods [21]. Assuming the stationarity in EEG signals, spatial filters derived from training data can be applied in the following validation stage. ...
... The correlation-based feature extraction in the fully-calibrated methods can be formulated as r n = Corr X T W ,χ T n W . Since more complicated spatial filters can be derived from training trials in the fully-calibrated methods than in the calibrationfree method, several kinds of algorithms have been proposed in previous studies [21]. The following four promising algorithms were employed in this study. ...
Article
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Objective: The emergence of mobile electroencephalogram (EEG) platforms have expanded the use cases of brain--computer interfaces (BCIs) from laboratory-oriented experiments to our daily life. In challenging situations where humans' natural behaviors such as head movements are unrestrained, various artifacts could deteriorate the performance of BCI applications. This paper explored the effect of muscular artifacts generated by participants' head movements on the signal characteristics and classification performance of steady-state visual evoked potentials (SSVEPs). Approach: A moving visual flicker was employed to induce not only SSVEPs but also horizontal and vertical head movements at controlled speeds, leading to acquiring EEG signals with intensity-manipulated muscular artifacts. To properly induce neck muscular activities, a laser light was attached to participants' heads to give visual feedback; the laser light indicates the direction of the head independently from eye movements. The visual stimulus was also modulated by four distinct frequencies (10, 11, 12, and 13 Hz). The amplitude and signal-to-noise ratio (SNR) were estimated to quantify the effects of head movements on the signal characteristics of the elicited SSVEPs. The frequency identification accuracy was also estimated by using well-established decoding algorithms including calibration-free and fully-calibrated approaches. Main results: The amplitude and SNR of SSVEPs tended to deteriorate when the participants moved their heads, and this tendency was significantly stronger in the vertical head movements than in the horizontal movements. The frequency identification accuracy also deteriorated in proportion to the speed of head movements. Importantly, the accuracy was significantly higher than its chance-level regardless of the level of artifact contamination and algorithms. Significance: The results suggested the feasibility of decoding SSVEPs in humans freely moving their head directions, facilitating the real-world applications of mobile BCIs. The clinical trial registration number is HF2018-858.
... The spatial filtering technique combining the multielectrode signals into single-or multichannel signals offers a better method for extracting SSVEP features and eliminating nuisance signals in SSVEP studies (Yan et al., 2018). Since scalp EEG is usually regarded to be a linear mixture of multiple time series from various cortical sources (Onton et al., 2006), the weight coefficients can be applied for multielectrode scalp EEG signals to estimate the cortical source activities (Nakanishi et al., 2018b). On the basis of this idea, several methods of extracting optimal spatial filters to reconstruct source activities from scalp EEG signals have been carried out to enhance the SNR of SSVEPs. ...
... , y i (N t ) T ∈ R N t ×1 contains the SSVEP signal of the ith electrode in one segment of N t samples, and e i ∈ R N t ×1 is the noise vector. The SSVEP reference signals model X f ∈ R N t ×2N h is defined by Nakanishi et al. (2018b): ...
... The CCA-based SSVEP visual acuity achieved a difference of 0.039 logMAR and a limit of agreement of 0.202 logMAR from FrACT visual acuity, and that for MSI-based SSVEP visual acuity were −0.080 logMAR and 0.208 logMAR, which was all lower than them of SSVEP visual acuity for the native combination with a difference and a limit of agreement of −0.095 logMAR and 0.253 logMAR. Since the spatial filtering methods can enhance the SNR of SSVEPs and suppress the non-SSVEP noise (Nakanishi et al., 2018b), this result illustrated that the unrelated noise, e.g., EMG and EOG (Friman et al., 2007;Zhang et al., 2021), was one of the reasons for the difference between SSVEP and behavioral visual acuity (Hamilton et al., 2021b), and the other methods of enhancing the SNR, such as signal preprocessing (Kołodziej et al., 2016), e.g., time-domain filtering and blind source separation (BSS) (Ji et al., 2019), and SSVEP recognition algorithms , e.g., wavelet transform (WT) (Rejer, 2017) and empirical mode decomposition (EMD) (Huang et al., 2013;Tello et al., 2014), may also have the property to improve the agreement between SSVEP and behavioral visual acuity. ...
Article
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The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.
... This study employed a template-matching-based classification method, which uses correlation coefficients between individual templates and ongoing EEG signals as features [6], [14], [15], [24]. The filter-bank analysis was also integrated to decompose SSVEPs into sub-band components so that independent information embedded in the harmonic components can be extracted efficiently [18]. ...
... This study employed the ensemble TRCA-based method to analyze the waveform-coded SSVEPs since it has shown the greatest performance in the previous literature of an SSVEPbased BCI [15], [24], [27]. To precisely capture fine-tuned fundamental and harmonic components in SSVEPs, more suitable algorithms than the TRCA-based methods, which might be model-based ones, need to be developed. ...
Article
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This study presents a novel waveform-coding method for multi-target steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Three periodic waveforms including square, sawtooth, and sinusoidal waves at various frequencies and initial phases were employed to elicit discriminable SSVEPs. A virtual keyboard was first designed using 36 visual stimuli modulated by the combinations of different frequencies, phases, and waveforms. With the virtual keyboard, 13 healthy participants performed offline and online BCI experiments with a cue-guided spelling task. The task-related component analysis (TRCA)-based algorithm was used to identify a target visual stimulus. The offline results showed that the visual stimuli tagged with different properties could accurately be identified by analyzing the elicited SSVEPs. Moreover, the online spelling task achieved promising performance with an averaged information transfer rate (ITR) of 62.6 ± 32.5 bits/min. This study validated the feasibility of implementing a multi-command SSVEP-based BCI using the hybrid waveform-, frequency- and phase-coding method. The proposed waveform-coding method provides a completely new channel for multi-target stimulus coding, expanding the research fields of an SSVEP-based BCI.
... Many studies have validated the effectiveness of incorporating individual calibration data in classification models to reliably detect SSVEPs [2], [3], [4]. It has also been well-validated that spatial filtering techniques can enhance the signal-to-noise ratio (SNR) of SSVEPs by alleviating the interference from spontaneous electroencephalographic (EEG) activities [5]. ...
Article
This commentary presents a replication study to verify the effectiveness of a sum of squared correlations (SSCOR)-based steady-state visual evoked potentials (SSVEPs) decoding method proposed by Kumar et al.. We implemented the SSCOR-based method in accordance with their descriptions and estimated its classification accuracy using a benchmark SSVEP dataset with cross validation. Our results showed significantly lower classification accuracy compared with the ones reported in Kumar et al.’s study. We further investigated the sources of performance discrepancy by simulating data leakage between training and test datasets. The classification performance of the simulation was remarkable similar to those reported by Kumar et al.. We, therefore, question the validity of evaluation and conclusions drawn in Kumar et al.’s study.
Article
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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.
Conference Paper
Task-related component analysis (TRCA) has been the most effective spatial filtering method in implementing high-speed brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). TRCA is a data-driven method, in which spatial filters are optimized to maximize inter-trial covariance of time-locked electroencephalographic (EEG) data, formulated as a generalized eigenvalue problem. Although multiple eigenvectors can be obtained by TRCA, the traditional TRCA-based SSVEP detection considered only one that corresponds to the largest eigenvalue to reduce its computational cost. This study proposes using multiple eigen-vectors to classify SSVEPs. Specifically, this study integrates a task consistency test, which statistically identifies whether the component reconstructed by each eigenvector is task-related or not, with the TRCA-based SSVEP detection method. The proposed method was evaluated by using a 12-class SSVEP dataset recorded from 10 subjects. The study results indicated that the task consistency test usually identified and suggested more than one eigenvectors (i.e., spatial filters). Further, the use of additional spatial filters significantly improved the classification accuracy of the TRCA-based SSVEP detection.
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