About this Research Topic
The ability of code-modulated (visual) evoked potentials (c-VEPs) to achieve reliable, high-speed brain-computer interfaces (BCIs) for communication and control has been demonstrated over the past few years. Their exogenous nature, the short calibration times, the high-speed and high-accuracy command decoding, and the robustness against noise are some of the reasons why these control signals promise to play a key role in the field of BCIs. Despite their promising future, the application of c-VEPs in BCI has only just begun, thus many open questions and challenges remain to be answered. Moreover, the utility of these potentials has been shown to verify the integrity of perceptual pathways without behavioral response. To advance the field, more efforts should be devoted to providing real-world applications with target users, optimizing paradigms to reduce user fatigue, proposing novel signal processing algorithms, and understanding the underlying brain mechanisms involved in the generation of these potentials.
This Research Topic aims at advancing the field of BCIs based on c-VEPs, encouraging novel studies focused on providing solutions to the current challenges in the field. For instance, further work is needed to determine whether c-VEP-based BCIs are suitable to be controlled by motor-disabled users outside the laboratory. Efforts are also needed to reduce user-fatigue, e.g. via high-frequency codes, non-binary codes, or even studying the long-term influence of the stimulation. Another line of research may be focused on proposing Deep Learning architectures, still scarce in c-VEP-based BCIs, despite their popularity in other areas. It is also important to encourage the development of asynchronous and early stopping algorithms for real-world applications; or even zero-calibration paradigms (e.g., by transfer-learning) toward plug-and-play BCIs. In addition, some fundamental scientific questions remain to be answered: what is the basic element (i.e., event) to which the brain responds? Is it influenced by the brightness or tone of the flash? Is it possible to model the c-VEP response by convolving basic elements, or should stimulus duration be considered? Apart from the visual modality, other contributions based on code-modulated evoked potentials (c-EP) are welcome, such as auditory or tactile, useful in late-stage diseases that impair gaze control.
We are looking for manuscripts (excluding reviews) that cover, but are not limited to, the following topics related to c-VEP-based BCIs:
* Applications of non-invasive BCIs based on c-VEP, especially if they have been tested with target users
* User friendliness in c-VEP based BCIs
* Reduced (or even zero) calibration approaches, e.g., transfer-learning
* Analysis and proposals of improved stimulus protocols or paradigms, e.g., pseudo-random noise-codes such as non-binary sequences, handcrafted codes, stimuli variations such as colors, presentation rate, etc
* Modeling brain responses, e.g. regression approaches, deep neural networks
* Signal processing advances
* Novel asynchronous and/or early stopping algorithms
* Other c-EP modalities: auditory, tactile, covert vs. overt attention detection
* Fundamental neuroscientific analysis of c-EP brain responses
* Embedded c-VEP-based BCIs, portability
* Long-term use analysis
* Applications of c-EP for medical monitoring and diagnosis
* The role of c-EP in learning and training
* Continuous noisetags
Topic Editor Dr. Desain is the founder of MindAffect, a company that elaborates on the use of cEP for medical applications. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
This Research Topic aims at advancing the field of BCIs based on c-VEPs, encouraging novel studies focused on providing solutions to the current challenges in the field. For instance, further work is needed to determine whether c-VEP-based BCIs are suitable to be controlled by motor-disabled users outside the laboratory. Efforts are also needed to reduce user-fatigue, e.g. via high-frequency codes, non-binary codes, or even studying the long-term influence of the stimulation. Another line of research may be focused on proposing Deep Learning architectures, still scarce in c-VEP-based BCIs, despite their popularity in other areas. It is also important to encourage the development of asynchronous and early stopping algorithms for real-world applications; or even zero-calibration paradigms (e.g., by transfer-learning) toward plug-and-play BCIs. In addition, some fundamental scientific questions remain to be answered: what is the basic element (i.e., event) to which the brain responds? Is it influenced by the brightness or tone of the flash? Is it possible to model the c-VEP response by convolving basic elements, or should stimulus duration be considered? Apart from the visual modality, other contributions based on code-modulated evoked potentials (c-EP) are welcome, such as auditory or tactile, useful in late-stage diseases that impair gaze control.
We are looking for manuscripts (excluding reviews) that cover, but are not limited to, the following topics related to c-VEP-based BCIs:
* Applications of non-invasive BCIs based on c-VEP, especially if they have been tested with target users
* User friendliness in c-VEP based BCIs
* Reduced (or even zero) calibration approaches, e.g., transfer-learning
* Analysis and proposals of improved stimulus protocols or paradigms, e.g., pseudo-random noise-codes such as non-binary sequences, handcrafted codes, stimuli variations such as colors, presentation rate, etc
* Modeling brain responses, e.g. regression approaches, deep neural networks
* Signal processing advances
* Novel asynchronous and/or early stopping algorithms
* Other c-EP modalities: auditory, tactile, covert vs. overt attention detection
* Fundamental neuroscientific analysis of c-EP brain responses
* Embedded c-VEP-based BCIs, portability
* Long-term use analysis
* Applications of c-EP for medical monitoring and diagnosis
* The role of c-EP in learning and training
* Continuous noisetags
Topic Editor Dr. Desain is the founder of MindAffect, a company that elaborates on the use of cEP for medical applications. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: brain-computer interfacing, closed loop, code-modulated visual evoked potentials, event-related potentials, electroencephalogram, machine learning, pseudo-random noise-codes, signal processing
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