Channel capacity in brain–computer interfaces

Costa, Tássia R.; Uribe, Luisa Fernanda Suárez; Carvalho, Sarah N.; Soriano, Diogo C.; Castellano, Gabriela; Suyama, Ricardo; Attux, Romis; Panazio, Cristiano

doi:10.1088/1741-2552/ab6cb7

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…Therefore, although the term 'BCI illiteracy' is generally misleading, because it suggests the inability to control the BCI lies with the person, which is very often not the case, it seems to be still broadly used by the BCI community, as e.g. [37][38][39]. The other related term 'BCI inefficiency' suggested for this phenomenon later [40] was not properly adopted by the scientific community, probably due to the fact that it was published in proceedings within the scope of a conference which are still not broadly indexed by scientific databases.…”

Section: Introductionmentioning

confidence: 99%

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces

Volosyak

Rezeika

Benda

et al. 2020

Biomed. Phys. Eng. Express

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Brain-Computer Interface (BCI) systems use brain activity as an input signal and enable communication without requiring bodily movement. This novel technology may help impaired patients and users with disabilities to communicate with their environment. Over the years, researchers investigated the performance of subjects in different BCI paradigms, stating that 15%–30% of BCI users are unable to reach proficiency in using a BCI system and therefore were labelled as BCI illiterates. Recent progress in the BCIs based on the visually evoked potentials (VEPs) necessitates re-considering of this term, as very often all subjects are able to use VEP-based BCI systems. This study examines correlations among BCI performance, personal preferences, and further demographic factors for three different modern visually evoked BCI paradigms: (1) the conventional Steady-State Visual Evoked Potentials (SSVEPs) based on visual stimuli flickering at specific constant frequencies (fVEP), (2) Steady-State motion Visual Evoked Potentials (SSmVEP), and (3) code-modulated Visual Evoked Potentials (cVEP). Demographic parameters, as well as handedness, vision correction, BCI experience, etc., have no significant effect on the performance of VEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only 20 out of a total of 86 participants indicated a change in fatigue during the experiment. 83 subjects were able to successfully finish all spelling tasks with the fVEP speller, with a mean (SD) information transfer rate of 31.87 bit/min (9.83) and an accuracy of 95.28% (5.18), respectively. Compared to that, 80 subjects were able to successfully finish all spelling tasks using SSmVEP, with a mean information transfer rate of 26.44 bit/min (8.04) and an accuracy of 91.10% (6.01), respectively. Finally, all 86 subjects were able to successfully finish all spelling tasks with the cVEP speller, with a mean information transfer rate of 40.23 bit/min (7.63) and an accuracy of 97.83% (3.37).

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Section: Introductionmentioning

confidence: 99%

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces

Volosyak

Rezeika

Benda

et al. 2020

Biomed. Phys. Eng. Express

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“…The number of targets is denoted by N and the accuracy of the BCI is denoted by a . ITR for a single prediction is defined as given in Equation (4):

This formula is a special case of mutual information between the true target and the predicted target, obtained by assuming that the channel is doubly symmetric (Wolpaw et al, 1998 ; da Silva Costa et al, 2020 ). Since the proposed approach does not take into account this assumption and often this assumption is not met in practice (Yuan et al, 2013 ; Thompson et al, 2014 ), we use mutual information between the predicted class and correct class as the performance measure as suggested by Thompson et al ( 2014 ).…”

Section: Methodsmentioning

confidence: 99%

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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“…Classification refers to the process of identifying the user's intended target based on the features extracted from the EEG signals in response to different frequencies of visual stimuli [21]. The classification operates in two modes.…”

Section: Classificationmentioning

confidence: 99%

Optimizing SSVEP-based BCI training through Adversarial Generative Neural Networks

Figueiredo,

Negreiros Carvalho,

Vargas

et al. 2023

IJECER

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Brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) use brain activity to control external devices, with applications ranging from assistive technologies to gaming. Typically, BCI systems are developed using supervised learning techniques that require labelled brain signals. However, acquiring these labelled signals can be tiring and time-consuming, especially for subjects with disabilities. In this study, we evaluated the performance impact of using synthetic brain signals to train and calibrate an SSVEP-based BCI system. Specifically, we used generative adversarial networks (GANs) to synthesize brain signals with SSVEP information, considering cases with two and four visual stimuli. Four scenarios with different proportions of real vs. synthetic brain signals were evaluated: Scenario 1 (baseline) using only real data and Scenarios 2-4 with 10%, 20% and 30% of real data replaced by synthetic data, respectively. Our results reveal that synthetic data can be used to train the BCI without a performance loss across the tested scenarios when two visual stimuli are used and with an average performance reduction compared to baseline of 7% (Scenario 2), 10,3% (Scenario 3) and 9,3% (Scenario 4) for four stimuli. Furthermore, considering each recording has duration of 2 seconds, by replacing 30% of real data with synthetic data, there is an immediate time-saving of 48 s and 96 s in the cases with two and four visual stimuli, respectively. This trade-off between accuracy and efficiency has significant implications for improving the usability and accessibility of SSVEP-based BCI, especially for assistive applications.

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Channel capacity in brain–computer interfaces

Cited by 9 publications

References 42 publications

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces

Information Bottleneck as Optimisation Method for SSVEP-Based BCI

Optimizing SSVEP-based BCI training through Adversarial Generative Neural Networks

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