Using Environmental Objects as Visual Stimuli in BCI-based Interaction System: Theoretical Approach

Petrova, Aliona; Chepin, Eugene V.; Voznenko, Timofei I.

doi:10.1016/j.procs.2021.06.109

Cited by 3 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, this process appears within a few milliseconds ( Barbosaa, Pires & Nunes, 2016 ). While external visual stimuli can be reflected by P300 within a latency of 300 ms, the color visual interaction latency period remains unclear ( Petrova, Chepin & Voznenko, 2021 ). In this work, epoch refers to each human-computer interaction of a single subject in one team, one trial of experiments.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we investigate a new approach to solve the RGB color neural mechanisms, by analyzing the task-irrelevant random stimuli EEG dataset and at the same time using machine learning combined with visual evoked potential ( Petrova, Chepin & Voznenko, 2021 ). The obtained classification results are combined with an EEG channel to locate the cerebral cortex, and find the position where the RGB color information is processed in the primary visual cortex.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decoding of the neural representation of the visual RGB color model

Mao

Feng

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

RGB color is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB color information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB color stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB color is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB color in classification accuracy and channel location. Finally, space decoding of RGB color depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding of the neural representation of the visual RGB color model

Mao

Feng

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…This method can better restrain the subjects' self-consciousness during the EEG recording process. Fast stimuli can well evoke cells of visual rod neurons firing within a few milliseconds after stimulation [ 19 ]. Within a latency of 300 ms from external visual stimuli can be reflected by P300, but the color visual interaction latency is still not clear [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding

Zhang²,

Mao

et al. 2022

Heliyon

View full text Add to dashboard Cite

“…We used fast stimuli because visual rod nerve cells usually discharge in a short time after stimulation. This process usually occurs within a few milliseconds 21 . Therefore, it is di cult to document.…”

Section: Interaction Designmentioning

confidence: 99%

Decoding human visual colour EEG information using machine learning and visual evoked potentials

Jia¹

Science Data Bank Datasets

View full text Add to dashboard Cite

With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we rst demonstrate the possibility of building a new kind of task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding. We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classi cation accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.

show abstract

Using Environmental Objects as Visual Stimuli in BCI-based Interaction System: Theoretical Approach

Cited by 3 publications

References 11 publications

Decoding of the neural representation of the visual RGB color model

Decoding of the neural representation of the visual RGB color model

Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding

Decoding human visual colour EEG information using machine learning and visual evoked potentials

Contact Info

Product

Resources

About