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Recent progress of non-invasive optical modality to brain computer interface: A review study | IEEE Conference Publication | IEEE Xplore

Recent progress of non-invasive optical modality to brain computer interface: A review study


Abstract:

Brain activity is usually measured by non-invasive modalities. Inter alia, the electroencephalogram (EEG) is used most commonly. However, EEG is very sensitive to other b...Show More

Abstract:

Brain activity is usually measured by non-invasive modalities. Inter alia, the electroencephalogram (EEG) is used most commonly. However, EEG is very sensitive to other biosignals, so other bio-signal detection modalities must be used as supplementary systems. Functional near-infrared spectroscopy (fNIRS) has good characteristics for use as such a supplementary modality, because brain activities can be measured by fNIRS through hemodynamic responses. Therefore, many scientists have adopted fNIRS for brain machine interface (BCI). Recently, fNIRS has become more compact and is robust to noise, so it could bring us to the development of an effective wearable BCI.
Date of Conference: 12-14 January 2015
Date Added to IEEE Xplore: 02 April 2015
ISBN Information:
Conference Location: Gangwon, Korea (South)
Citations are not available for this document.

I. Outline

Brain machine interfaces use brain activities as commands, for which brain activities are usually measured by electroencephalogram (EEG). However, EEG is very sensitive to the other bio-signals: heart rate, eye movement, motion, and so on. Therefore, the use of BCI employing only EEG has limitations, so other bio-signal detection modalities are usually included as supplementary modalities, including those such as electrocardiogram (ECG), electromyography (EMG), photoplethysmography (PPG), electrooculogram (EOG), and galvanic skin reflex (GSR) [1]–[4]. However, while brain activities cannot be directly measured by such modalities, they can be directly measured by the use of functional near-infrared spectroscopy (fNIRS) through hemodynamic responses.

Cites in Papers - |

Cites in Papers - IEEE (4)

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1.
Jing Wang, Xiaojun Ning, Wangjun Shi, Youfang Lin, "A Bayesian Graph Neural Network for EEG Classification — A Win-Win on Performance and Interpretability", 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp.2126-2139, 2023.
2.
Xueze Zhang, Zuoting Song, Shuang Li, Yuan Zhang, Tao Fang, Shouyan Wang, Hui Li, Yifang Lin, Jie Jia, Lihua Zhang, Xiaoyang Kang, Hongbo Wang, "Brain Computer Interface for the Hand Function Restoration", 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp.1-6, 2021.
3.
Sergei Kharchenko, Roman Meshcheryakov, Yaroslav Turovsky, "Influence of the number of channels when highlighting steady-state visual evoked potentials based on a multivariate synchronization index", 2020 13th International Conference on Developments in eSystems Engineering (DeSE), pp.195-199, 2020.
4.
Sergei Kharchenko, Yaroslav Turovsky, Roman Mescheryakov, Anastasia Iskhakova, "Restrictions of the Measurement System and a Patient When Using Visually Evoked Potentials", 2019 12th International Conference on Developments in eSystems Engineering (DeSE), pp.15-19, 2019.

Cites in Papers - Other Publishers (2)

1.
Sergey Kharchenko, Roman Meshcheryakov, Yaroslav Turovsky, Daniyar Volf, Proceedings of 15th International Conference on Electromechanics and Robotics "Zavalishin's Readings", vol.187, pp.225, 2021.
2.
Sergei Kharchenko, Roman Meshcheryakov, Yaroslav Turovsky, "Influence of Signal Preprocessing When Highlighting Steady-State Visual Evoked Potentials Based on a Multivariate Synchronization Index", Futuristic Trends in Network and Communication Technologies, vol.1396, pp.102, 2021.
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References

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