A brain-computer interface based on a few-channel EEG-fNIRS bimodal system

S Ge, Q Yang, R Wang, P Lin, J Gao, Y Leng… - IEEE …, 2017 - ieeexplore.ieee.org
With the development of the wearable brain-computer interface (BCI), a few-channel BCI
system is necessary for its application to daily life. In this paper, we proposed a bimodal BCI …

Hybrid EEG-fNIRS brain computer interface based on common spatial pattern by using EEG-informed general linear model

Y Gao, B Jia, M Houston… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …

Enhancing performance of a hybrid EEG-fNIRS system using channel selection and early temporal features

R Li, T Potter, W Huang, Y Zhang - Frontiers in human neuroscience, 2017 - frontiersin.org
Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic
applications. A desirable BCI system should be portable, minimally invasive, and feature …

Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning

L Qiu, Y Zhong, Z He, J Pan - Frontiers in Human Neuroscience, 2022 - frontiersin.org
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have
potentially complementary characteristics that reflect the electrical and hemodynamic …

A Computationally Efficient Method for Hybrid EEG‐fNIRS BCI Based on the Pearson Correlation

MAH Hasan, MU Khan, D Mishra - BioMed Research …, 2020 - Wiley Online Library
A hybrid brain computer interface (BCI) system considered here is a combination of
electroencephalography (EEG) and functional near‐infrared spectroscopy (fNIRS). EEG …

Multi-modal integration of EEG-fNIRS for brain-computer interfaces–current limitations and future directions

S Ahn, SC Jun - Frontiers in human neuroscience, 2017 - frontiersin.org
Multi-modal integration, which combines multiple neurophysiological signals, is gaining
more attention for its potential to supplement single modality's drawbacks and yield reliable …

Toward a compact hybrid brain-computer interface (BCI): Performance evaluation of multi-class hybrid EEG-fNIRS BCIs with limited number of channels

J Kwon, J Shin, CH Im - PloS one, 2020 - journals.plos.org
It has been demonstrated that the performance of typical unimodal brain-computer interfaces
(BCIs) can be noticeably improved by combining two different BCI modalities. This so-called …

[HTML][HTML] Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning

A Maher, SM Qaisar, N Salankar, F Jiang… - biocybernetics and …, 2023 - Elsevier
The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-
BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to …

Multimodal fNIRS-EEG classification using deep learning algorithms for brain-computer interfaces purposes

M Saadati, J Nelson, H Ayaz - … Proceedings of the AHFE 2019 International …, 2020 - Springer
The development of brain-computer interface (BCI) systems has received considerable
attention from neuroscientists in recent years. BCIs can serve as a means of communication …

Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces

KS Hong, MJ Khan, MJ Hong - Frontiers in human neuroscience, 2018 - frontiersin.org
In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …