Optimal pseudorandom sequence selection for online c-VEP based BCI control applications

Isaksen, Jonas L.; Mohebbi, Ali; Puthusserypady, Sadasivan

doi:10.1371/journal.pone.0184785

Cited by 8 publications

(17 citation statements)

References 30 publications

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“…Table 1 enumerates the studies that used these variations. In general, some studies suggested that the performance of BCIs based on time-delayed versions of a single bit-sequence does not vary significantly when using different code types [43][44][45].…”

Section: Bit-sequencesmentioning

confidence: 99%

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%

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

et al. 2021

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“…Although to a lesser extent, Gold sequences have also previously been explored by Thielen et al [15] in another VEP-based paradigm consisting of asynchronously evoked broad-band VEPs, due to the optimal cross-correlation properties of such sequences. Similarly, while Kasami sequences have had a highly limited presence in BCI research, these demonstrated inconclusive results in the context of c-VEP modulation when studied in 2017 by Isaksen et al [28].…”

Section: Modulation Sequences (Mss)mentioning

confidence: 99%

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

Torres

Daly

2021

J. Neural Eng.

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Objective. In the last decade, the advent of code-modulated brain-computer interfaces (BCIs) has allowed the implementation of systems with high information transfer rates (ITRs) and increased the possible practicality of such interfaces. In this paper, we evaluate the effect of different numbers of targets in the stimulus display, modulation sequences generators, and signal processing algorithms on the accuracy and ITR of code-modulated BCIs. Approach. We use both real and simulated electroencephalographic (EEG) data, to evaluate these parameters and methods. Then, we compared numerous different setups to assess their performance and identify the best configurations. We also evaluated the dependability of our simulated evaluation approach. Main results. Our results show that Golay, almost perfect, and deBruijn sequence-based visual stimulus modulations provide the best results, significantly outperforming the commonly used m-sequences in all cases. We conclude that artificial neural network processing algorithms offer the best processing pipeline for this type of BCI, achieving a maximum classification accuracy of 94.7% on real EEG data while obtaining a maximum ITR of 127.2 bits min−1 in a simulated 64-target system. Significance. We used a simulated framework that demonstrated previously unattainable flexibility and convenience while staying reasonably realistic. Furthermore, our findings suggest several new considerations which can be used to guide further code-based BCI development.

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“…First, the encoding method of c-VEP can be optimized. There are many kinds of pseudorandom codes with different characteristics (Kettunen 1997), and the code selection can influence the system performance (Isaksen et al 2017). In addition, the other stimulation parameters can also be considered, such as the monitor refresh rate, the stimulus pattern, the stimulus color, etc.…”

Section: Discussionmentioning

confidence: 99%

A high-performance brain switch based on code-modulated visual evoked potentials

Zheng

Gao

Zhang³

et al. 2022

J. Neural Eng.

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Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

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Optimal pseudorandom sequence selection for online c-VEP based BCI control applications

Cited by 8 publications

References 30 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

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

A high-performance brain switch based on code-modulated visual evoked potentials

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