ArticleHealth information science and systems2019
Modeling and classification of voluntary and imagery movements for brain-computer interface from fNIR and EEG signals through convolutional neural network.
Article in Health information science and systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement.Health information science and systems · 2023Article
- Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals.Bioengineering (Basel, Switzerland) · 2023Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting.Health information science and systems · 2021Article
- The Potential Role of fNIRS in Evaluating Levels of Consciousness.Frontiers in human neuroscience · 2021Article
- A space-frequency localized approach of spatial filtering for motor imagery classification.Health information science and systems · 2020Article
- Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation.Brain informatics · 2020Article
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Authors and funding
3 authors.
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Abstract
Practical brain-computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating the neuronal electric potential regarding the psychophysiological activity. Since the fNIR modality has an issue of temporal resolution, fNIR alone is not enough to achieve satisfactory classification accuracy as multiple neural stimuli are produced by voluntary and imagery movements. This leads us to make a combination of fNIR and EEG with a view to developing a BCI model for the classification of the brain signals of the voluntary and imagery movements. This work proposes a novel approach to prepare functional neuroimages from the fNIR and EEG using eight different movement-related stimuli. The neuroimages are used to train a convolutional neural network (CNN) to formulate a predictive model for classifying the combined fNIR-EEG data. The results reveal that the combined fNIR-EEG modality approach along with a CNN provides improved classification accuracy compared to a single modality and conventional classifiers. So, the outcomes of the proposed research work will be very helpful in the implementation of the finer BCI system.
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