A BCI painting system using a hybrid control approach based on SSVEP and P300

Tang, Zhichuan; Wang, Xinyang; Wu, Jian; Ping, Yaqin; Guo, Xiaogang; Cui, Zhixuan

doi:10.1016/j.compbiomed.2022.106118

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…The integration of art and BCIs has advanced in recent years, spanning domains such as music (44,(58)(59)(60)(61) and painting (56,57,62). In brain painting, previous research has successfully used characteristic responses in brain activity to directly control a palette for drawing paintings (27)(28)(29)57,63,64). Our approach differs from other work on image generation in that it eliminates the need for an intermediate step in decoding intentions.…”

Section: Resultsmentioning

confidence: 99%

“…The subject's intentions are then used to control a palette on a screen. In previous research, two types of neural components have been considered, the P300 wave (26)(27)(28) and the steady state visual evoked potential (SSVEP) (29).…”

Section: Painting Using Neural Signalsmentioning

confidence: 99%

“…Recently, artists have used BCI to create art that is dynamically influenced by their own brain activity or that of the audience. Some of this art includes exhibitions that are influenced by the audience's brain activity and music that is created according to the measured activity (26)(27)(28)(29). The process usually involves an intermediate step that decodes the user's intentions and then adjusts the palette based on the decoded actions.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion model-based image generation from rat brain activity

Yamashiro,

Matsumoto,

Ikegaya

2024

Preprint

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Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens up new possibilities for creative expression and experimentation.

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Section: Resultsmentioning

confidence: 99%

Section: Painting Using Neural Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diffusion model-based image generation from rat brain activity

Yamashiro,

Matsumoto,

Ikegaya

2024

Preprint

View full text Add to dashboard Cite

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“…The first paper [ 24 ] explored the possible creative applications of a BCI by using steady-state visual-evoked potentials (SSVEPs) in combination with different interface matrices. The second paper [ 25 ] used an SSVEP in combination with P300. Both studies applied the interface to help the participants create different drawings, either by copying or through free expression.…”

Section: Resultsmentioning

confidence: 99%

BCI Applications to Creativity: Review and Future Directions, from little-c to C2

2023

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BCI devices are increasingly being used to create interactive interfaces between users and their own psychophysiological signals. Over the years, these systems have seen strong development as they can enable people with limited mobility to make certain decisions to alter their environment. Additionally, their portability and ease of use have allowed a field of research to flourish for the study of cognitive and emotional processes in natural settings. The study of creativity, especially little creativity (little-c), is one example, although the results of this cutting-edge research are often poorly systematized. The purpose of the present paper, therefore, was to conduct a scoping review to describe and systematize the various studies that have been conducted on the application potential of BCI to the field of creativity. Twenty-two papers were selected that collect information on different aspects of creativity, including clinical applications; art experience in settings with high ecological validity; BCI for creative content creation, and participants’ engagement. Critical issues and potentialities of this promising area of study are also presented. Implications for future developments towards multi-brain creativity settings and C2 are discussed.

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“…The specific visual or auditory stimuli used to evoke the electrophysiological activity consists of the paradigm. Examples include the P300 speller using flashing rows and columns and motor imagery consisting of cued imagined physical gestures ( Tang et al, 2022 ). The P300 speller and SSVEP have been used as virtual keyboards to enable the letter-by-letter composition of a message ( Capati et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography

LaRocco,

Tahmina,

Lecian

et al. 2023

Front. Neuroinform.

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IntroductionParalyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain–computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls.MethodsAlthough virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects.ResultsUsing open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7.DiscussionHowever, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.

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A BCI painting system using a hybrid control approach based on SSVEP and P300

Cited by 10 publications

References 17 publications

Diffusion model-based image generation from rat brain activity

Diffusion model-based image generation from rat brain activity

BCI Applications to Creativity: Review and Future Directions, from little-c to C2

Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography

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