How to build a fast and accurate code-modulated brain-computer interface

Torres, Juan Antonio Ramírez; Daly, Ian

doi:10.1088/1741-2552/abfaac

Cited by 6 publications

(13 citation statements)

References 36 publications

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“…Golay sequences [23,90,94,95] are pairs of complementary codes with low aperiodic correlations without length restrictions, hence they are used in digital applications for which Barker sequences are not available [21]. De Bruijn sequences also exist as a special class of nonlinear shift register codes with maximal length, whose cardinality behaves as a double exponential growth (there are 2 (2 m−1 )−m de Bruijn sequences of length 2 m ) [88,100]. However, autoand cross-correlation properties vary among different codes [100].…”

Section: Bit-sequencesmentioning

confidence: 99%

“…However, autoand cross-correlation properties vary among different codes [100]. APA sequences, defined as complex periodic sequences such that all out-of-phase correlation coefficients are zero except one, are also popular [23,88,94,95].…”

Section: Bit-sequencesmentioning

confidence: 99%

“…Some studies claim that the use of different sequence types does not appear to significantly affect overall performances as long as low values of autoand/or cross-correlation are guaranteed [43][44][45]. By contrast, Torres and Daly [88] recently reported significantly higher performances for de Bruijn, APA and Golay sequences in comparison with msequences, Gold and Kasami codes using simulated data. Further analysis with real EEG data would be recommended to validate these findings.…”

Section: Bit-sequencesmentioning

confidence: 99%

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Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

et al. 2021

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Objective. Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain–computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines. Approach. The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc. Main results. The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development. Significance. Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines of research are suggested to contribute to the development of reliable plug-and-play c-VEP-based BCIs.

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Section: Bit-sequencesmentioning

confidence: 99%

Section: Bit-sequencesmentioning

confidence: 99%

Section: Bit-sequencesmentioning

confidence: 99%

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Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

et al. 2021

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“…Importantly, closed-loop BCI simulators are distinguished from the relatively abundant offline simulation methods of neural activity data (Lotte, 2011 ; Aine et al, 2012 ; Krol et al, 2018 ; Dinarès-Ferran et al, 2018 ; Lindgren et al, 2018 ; Zhang and Liu, 2018 ; Aznan et al, 2019 ; Barzegaran et al, 2019 ; Fahimi et al, 2021 ; Kunanbayev et al, 2021 ; Ko et al, 2022 ) which serve important but different purposes [mainly for neural activity data augmentation to improve BCI decoder training and/or testing (Marturano et al, 2020 ; Ramírez Torres and Daly, 2021 ), or, more rarely, to supplement the development and evaluation of source modeling methods (Aine et al, 2012 ; Gramfort et al, 2013 )].…”

Section: Introductionmentioning

confidence: 99%

Closed-loop motor imagery EEG simulation for brain-computer interfaces

Shin

Suma

2022

Front. Hum. Neurosci.

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In a brain-computer interface (BCI) system, the testing of decoding algorithms, tasks, and their parameters is critical for optimizing performance. However, conducting human experiments can be costly and time-consuming, especially when investigating broad sets of parameters. Attempts to utilize previously collected data in offline analysis lack a co-adaptive feedback loop between the system and the user present online, limiting the applicability of the conclusions obtained to real-world uses of BCI. As such, a number of studies have attempted to address this cost-wise middle ground between offline and live experimentation with real-time neural activity simulators. We present one such system which generates motor imagery electroencephalography (EEG) via forward modeling and novel motor intention encoding models for conducting sensorimotor rhythm (SMR)-based continuous cursor control experiments in a closed-loop setting. We use the proposed simulator with 10 healthy human subjects to test the effect of three decoder and task parameters across 10 different values. Our simulated approach produces similar statistical conclusions to those produced during parallel, paired, online experimentation, but in 55% of the time. Notably, both online and simulated experimentation expressed a positive effect of cursor velocity limit on performance regardless of subject average performance, supporting the idea of relaxing constraints on cursor gain in online continuous cursor control. We demonstrate the merits of our closed-loop motor imagery EEG simulation, and provide an open-source framework to the community for closed-loop SMR-based BCI studies in the future. All code including the simulator have been made available on GitHub.

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“…Although the EEG based non-invasive BCI has a broad application prospect in many fields, the lower recognition accuracy and information transmission rate (ITR) of brain-controlled patterns are still the main reasons for restricting its applications (Yang et al , 2021). Among the above three brain-controlled patterns, the SSVEP-BCI has the advantages of high recognition accuracy, high ITR and convenient to configure and utilize, so as to become a hot-spot for the research of BCI applications in recent years (Ming et al , 2021; Yan et al , 2021; Torres and Daly, 2021).…”

Section: Introductionmentioning

confidence: 99%

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Luo

2022

IJICC

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PurposeSteady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approachTo survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.FindingsFor the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/valueBased on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

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How to build a fast and accurate code-modulated brain-computer interface

Cited by 6 publications

References 36 publications

Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

Closed-loop motor imagery EEG simulation for brain-computer interfaces

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

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