Direct information transfer rate optimisation for SSVEP-based BCI

Ingel, Anti; Kuzovkin, Ilya; Vicente, Raúl

doi:10.1088/1741-2552/aae8c7

Cited by 5 publications

(27 citation statements)

References 25 publications

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“…Therefore, under the assumption that the differences in ITR are normally distributed, the better performance of Classifier 2 is statistically significant. The same comparison between Classifier 2 and the approach of Ingel et al (2018) did not show statistical significance (p = 0.097). We also evaluated the classifier of Ingel et al (2018) with the added pre-processing step of subtracting Cz channel from O1 and O2 channels, and we replaced their original gradient descent algorithm with a more stable basinhopping algorithm (Wales and Doye, 1997).…”

Section: Results On Datasetmentioning

confidence: 82%

“…Tables 4-6 show that in some cases, the proposed algorithm is outperformed by the arg max classifier and by the algorithm described by Ingel et al (2018). We assume this is because the choice of bins was not optimal.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, we subtract the Cz channel signal from the O1 and O2 channels as the first step. The rest of the feature extraction is identical to Ingel et al (2018). In particular, we use channels O1 and O2 from the dataset, and we use PSDA (Cheng et al, 2002) and CCA (Lin et al, 2007) feature extraction methods.…”

Section: Feature Extraction Methods For Datasetmentioning

confidence: 99%

“…To calculate mean detection time (MDT), we follow the approach of Ingel et al (2018). In particular, we estimate MDT as follows:…”

Section: Information Transfer Ratementioning

confidence: 99%

“…For example, this dataset has been used by Zhang et al (2018b), Zhang et al (2018a), Jiang et al (2018), , Wong et al (2020), and they report average ITRs up to 230 bits/min. Ingel et al (2018) proposed an algorithm for finding optimal rule from a fixed set of threshold-based classification rules. In particular, they use a threshold-based classification rule which has a threshold for each feature.…”

Section: Introductionmentioning

confidence: 99%

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Information Bottleneck as Optimisation Method for SSVEP-Based BCI

Ingel

Vicente

2021

Front. Hum. Neurosci.

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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.

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Section: Results On Datasetmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Feature Extraction Methods For Datasetmentioning

confidence: 99%

“…To calculate mean detection time (MDT), we follow the approach of Ingel et al (2018). In particular, we estimate MDT as follows:…”

Section: Information Transfer Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information Bottleneck as Optimisation Method for SSVEP-Based BCI

Ingel

Vicente

2021

Front. Hum. Neurosci.

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Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Floriano

Delisle-Rodríguez

Díez

et al. 2020

Computer Methods and Programs in Biomedicine

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Channel capacity in brain–computer interfaces

et al. 2020

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Objective. Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain–computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression—also based on that channel model, but with less restrictive assumptions—and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. Approach. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs. Main results. Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 1.68 to 4.79 0.70 bits per symbol, with p -value <0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol. Significance. Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy.

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Direct information transfer rate optimisation for SSVEP-based BCI

Cited by 5 publications

References 25 publications

Information Bottleneck as Optimisation Method for SSVEP-Based BCI

Information Bottleneck as Optimisation Method for SSVEP-Based BCI

Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Channel capacity in brain–computer interfaces

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