Neurofeedback training system with audiovisual stimuli for the attention state induction during cognitive processes

Bruno, Balbuena-Vera; Diego, F. Reyes-Ramirez; Blanca, Tovar-Corona; Anzueto-Ríos, Álvaro; Gonzalo, Solis-Villela J.

doi:10.1109/iceee.2017.8108883

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Moreover, their works pointed out that the stimulation which combining attention and SSVEP was itself a kind of neurofeedback and was expected to be a way to train attention deficits. As for the application of neurofeedback in SSVEP field, Bruno's research claims to improve the visual and auditory cognitive by binaural audio and SSVEP neurofeedback (Bruno et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Neurofeedback training of control network improves SSVEP-based BCI performance in children

Sun

Gao

2020

Preprint

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Background: In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, the study of improving the performance of the brain-computer interface (BCI) using the neuromodulation method rarely exists. The present study designs a neurofeedback training method to improve the performance of steady-state visual evoked potential (SSVEP) BCI and further explores its underlying mechanisms. Methods: As parietal lobe is the sole hub of information transmission, up-regulating alpha-band power of the parietal lobe by neurofeedback training was present as a new neural modulating method to improve SSVEP-based BCI in this study. Results: After this neurofeedback training (NFT), the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6% respectively. However, no improvement has been observed in the control group in which the subjects do not participate in NFT. What’s more, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was also observed. Evidence from the network analysis and attention test further indicate that NFT improves attention via developing the control ability of the parietal lobe and then enhances the above SSVEP indicators. Conclusion: Up-regulating parietal alpha-amplitude using neurofeedback training significantly improves the SSVEP-baesd BCI performance through modulating the control network. The study validates an effective neuromodulation method, and possibly also contributes to explaining the function of the parietal lobe in the control network.

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Section: Introductionmentioning

confidence: 99%

Neurofeedback training of control network improves SSVEP-based BCI performance in children

Sun

Gao

2020

Preprint

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Training with a Neurofeedback System for the Control of a Drone Using Electroencephalographic Signals

Gonzalez-Munoz

Orozco-Magdaleno

Ortiz-Yescas

et al. 2022

2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

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Neurofeedback training of control network improves SSVEP-based BCI performance in children

Sun

Gao

2021

Preprint

View full text Add to dashboard Cite

Background: In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, the study of improving the performance of the brain-computer interface (BCI) using the neuromodulation method rarely exists. The present study designs a neurofeedback training method to improve the performance of steady-state visual evoked potential (SSVEP) BCI and further explores its underlying mechanisms. Methods: As the parietal lobe is the sole hub of information transmission, up-regulating alpha-band power of the parietal lobe by neurofeedback training was presented in this study as a new neural modulation method to improve SSVEP-based BCI in this study. Results: After this neurofeedback training (NFT), the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6% respectively. However, no improvement has been observed in the control group in which the subjects do not participate in NFT. Evidence from the network analysis and attention test further indicate that NFT improves attention via developing the control ability of the parietal lobe and then enhances the above SSVEP indicators.Conclusion: Up-regulating parietal alpha-amplitude using neurofeedback training significantly improves the SSVEP-baesd BCI performance through modulating the control network. The study validates an effective neuromodulation method, and possibly also contributes to explaining the function of the parietal lobe in the control network.

show abstract

Neurofeedback training system with audiovisual stimuli for the attention state induction during cognitive processes

Cited by 4 publications

References 2 publications

Neurofeedback training of control network improves SSVEP-based BCI performance in children

Neurofeedback training of control network improves SSVEP-based BCI performance in children

Training with a Neurofeedback System for the Control of a Drone Using Electroencephalographic Signals

Neurofeedback training of control network improves SSVEP-based BCI performance in children

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