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 …
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
Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …
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
Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic
applications. A desirable BCI system should be portable, minimally invasive, and feature …
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
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have
potentially complementary characteristics that reflect the electrical and hemodynamic …
potentially complementary characteristics that reflect the electrical and hemodynamic …
A Computationally Efficient Method for Hybrid EEG‐fNIRS BCI Based on the Pearson Correlation
A hybrid brain computer interface (BCI) system considered here is a combination of
electroencephalography (EEG) and functional near‐infrared spectroscopy (fNIRS). EEG …
electroencephalography (EEG) and functional near‐infrared spectroscopy (fNIRS). EEG …
Multi-modal integration of EEG-fNIRS for brain-computer interfaces–current limitations and future directions
Multi-modal integration, which combines multiple neurophysiological signals, is gaining
more attention for its potential to supplement single modality's drawbacks and yield reliable …
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
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 …
(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
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 …
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
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 …
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
In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …
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