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Journal ArticleDOI

Correction to “Designing a Sum of Squared Correlations Framework for Enhancing SSVEP Based BCIs”

17 Feb 2020-Vol. 28, Iss: 4, pp 1044-1045
TL;DR: The study used the SSVEP benchmark dataset containing 40 target data collected from 35 subjects to evaluate the target detection performance of the SSCOR method and the results were reported.
Abstract: In the above paper [1] , we proposed a novel framework that uses a constrained formulation of sum of squared correlation (SSCOR) approach as an alternative method for designing a spatial filter for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). To evaluate the target detection performance of the SSCOR method, the task-related component analyses (TRCA) were used as a benchmark [2] . The study used the SSVEP benchmark dataset containing 40 target data collected from 35 subjects [3] . During the evaluation of the proposed method, the SSCOR provided very high detection performance and outperformed the TRCA method and the results were reported.
Citations
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Journal ArticleDOI
TL;DR: In this paper, a new perspective of spatial filter design is proposed and a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors.
Abstract: In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted.

1 citations

References
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Journal ArticleDOI
TL;DR: A comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects validated the efficiency of the proposal.
Abstract: Objective: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. Methods: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. Results: The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. Conclusion: This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. Significance: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

455 citations


"Correction to “Designing a Sum of S..." refers methods in this paper

  • ...To evaluate the target detection performance of the SSCOR method, the task-related component analyses (TRCA) were used as a benchmark [2]....

    [...]

Journal ArticleDOI
TL;DR: It is shown that it is possible to avoid leakage with a simple specific approach to data management followed by what is called a learn-predict separation, and several ways of detecting leakage when the modeler has no control over how the data have been collected are presented.
Abstract: Deemed “one of the top ten data mining mistakes”, leakage is the introduction of information about the data mining target that should not be legitimately available to mine from. In addition to our own industry experience with real-life projects, controversies around several major public data mining competitions held recently such as the INFORMS 2010 Data Mining Challenge and the IJCNN 2011 Social Network Challenge are evidence that this issue is as relevant today as it has ever been. While acknowledging the importance and prevalence of leakage in both synthetic competitions and real-life data mining projects, existing literature has largely left this idea unexplored. What little has been said turns out not to be broad enough to cover more complex cases of leakage, such as those where the classical independently and identically distributed (i.i.d.) assumption is violated, that have been recently documented. In our new approach, these cases and others are explained by explicitly defining modeling goals and analyzing the broader framework of the data mining problem. The resulting definition enables us to derive general methodology for dealing with the issue. We show that it is possible to avoid leakage with a simple specific approach to data management followed by what we call a learn-predict separation, and present several ways of detecting leakage when the modeler has no control over how the data have been collected. We also offer an alternative point of view on leakage that is based on causal graph modeling concepts.

259 citations

Journal ArticleDOI
01 Oct 2017
TL;DR: This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain– computer interface (BCI) speller that provides high-quality data for computational modeling of SSVEPs.
Abstract: This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain– computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naive) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was $0.5\pi $ . For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds. The dataset can be used as a benchmark dataset to compare the methods for stimulus coding and target identification in SSVEP-based BCIs. Through offline simulation, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data. The dataset also provides high-quality data for computational modeling of SSVEPs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html .

244 citations

Journal ArticleDOI
13 Sep 2019
TL;DR: A novel subject-specific target detection framework, sum of squared correlations (SSCOR), for improving the performance in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs).
Abstract: This study illustrates and evaluates a novel subject-specific target detection framework, sum of squared correlations (SSCOR), for improving the performance of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). The SSCOR spatial filter learns a common SSVEP representation space through the optimization of the individual SSVEP templates. The projection onto this SSVEP response subspace improves the signal to noise ratio (SNR) of the SSVEP components embedded in the recorded electroencephalographic (EEG) data. To demonstrate the effectiveness of the proposed framework, the target detection performance of the SSCOR method is compared with the state of the art task-related component analysis (TRCA). The evaluation is conducted on a 40 target SSVEP benchmark data collected from 35 subjects. The results of the extensive comparisons of the performance metrics show that the proposed SSCOR method outperforms the TRCA method. The ensemble version of the SSCOR framework provides an offline simulated information transfer rate (ITR) of 387 ± 9 bits/min which is much higher than that of the ensemble TRCA approach (max. ITR 216 ± 27 bits/min). The significant improvement in the detection accuracy and simulated ITR demonstrates the efficacy of the proposed framework for target detection in SSVEP based BCI applications.

24 citations


"Correction to “Designing a Sum of S..." refers background or methods in this paper

  • ...By a thorough examination, it was found that it was indeed data leakage during evaluation that lead to the very high detection accuracies presented in paper [1] and not inherently due to adapted mathematical framework alone....

    [...]

  • ...In the above paper [1], we proposed a novel framework that uses a constrained formulation of sum of squared correlation (SSCOR) approach as an alternative method for designing a spatial filter for steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs)....

    [...]

Journal ArticleDOI
17 Feb 2020
TL;DR: 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. shows significantly lower classification accuracy, and questions the validity of evaluation and conclusions drawn.
Abstract: 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 remarkably similar to those reported by Kumar et al. . We, therefore, question the validity of evaluation and conclusions drawn in Kumar et al. ’s study.

3 citations


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