ArticlePloS one2020
Toward a compact hybrid brain-computer interface (BCI): Performance evaluation of multi-class hybrid EEG-fNIRS BCIs with limited number of channels.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A Miniaturized and Modular Wearable Functional Near-Infrared Spectroscopy (fNIRS) Sensing Module for High-Density Cerebral Hemodynamic Monitoring.Biosensors · 2026Article
- Brain-computer interface technology for motor rehabilitation in severe stroke: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Co-localized optode-electrode design for multimodal functional near infrared spectroscopy and electroencephalography.Neurophotonics · 2025Article
- A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.Scientific reports · 2025Article
- Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.PloS one · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.Sensors (Basel, Switzerland) · 2024Article
- The speech neuroprosthesis.Nature reviews. Neuroscience · 2024Review
- Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.Sensors (Basel, Switzerland) · 2024Article
- On decoding of rapid motor imagery in a diverse population using a high-density NIRS device.Frontiers in neuroergonomics · 2024Article
- Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation.Frontiers in bioengineering and biotechnology · 2024Review
- OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.Bioengineering (Basel, Switzerland) · 2023Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.Frontiers in neurorobotics · 2022Article
- Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning.Frontiers in human neuroscience · 2022Article
- The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance.Sensors (Basel, Switzerland) · 2021Article
- Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation.Brain sciences · 2021Article
- A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction.Frontiers in neuroscience · 2021Article
- Classification of Game Demand and the Presence of Experimental Pain Using Functional Near-Infrared Spectroscopy.Frontiers in neuroergonomics · 2021Article
- Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Based on Convolutional Neural Networks.Frontiers in human neuroscience · 2021Article
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3 authors.
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Abstract
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 "hybrid BCI" technology has been studied for decades; however, hybrid BCIs that particularly combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) (hereafter referred to as hBCIs) have not been widely used in practical settings. One of the main reasons why hBCI systems are so unpopular is that their hardware is generally too bulky and complex. Therefore, to make hBCIs more appealing, it is necessary to implement a lightweight and compact hBCI system with minimal performance degradation. In this study, we investigated the feasibility of implementing a compact hBCI system with significantly less EEG channels and fNIRS source-detector (SD) pairs, but that can achieve a classification accuracy high enough to be used in practical BCI applications. EEG and fNIRS data were acquired while participants performed three different mental tasks consisting of mental arithmetic, right-hand motor imagery, and an idle state. Our analysis results showed that the three mental states could be classified with a fairly high classification accuracy of 77.6 ± 12.1% using an hBCI system with only two EEG channels and two fNIRS SD pairs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.