Title |
Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces – Current Limitations and Future Directions
|
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Published in |
Frontiers in Human Neuroscience, October 2017
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DOI | 10.3389/fnhum.2017.00503 |
Pubmed ID | |
Authors |
Sangtae Ahn, Sung C. Jun |
Abstract |
Multi-modal integration, which combines multiple neurophysiological signals, is gaining more attention for its potential to supplement single modality's drawbacks and yield reliable results by extracting complementary features. In particular, integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is cost-effective and portable, and therefore is a fascinating approach to brain-computer interface (BCI). However, outcomes from the integration of these two modalities have yielded only modest improvement in BCI performance because of the lack of approaches to integrate the two different features. In addition, mismatch of recording locations may hinder further improvement. In this literature review, we surveyed studies of the integration of EEG/fNIRS in BCI thoroughly and discussed its current limitations. We also suggested future directions for efficient and successful multi-modal integration of EEG/fNIRS in BCI systems. |
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Geographical breakdown
Country | Count | As % |
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United Kingdom | 5 | 25% |
United States | 2 | 10% |
Japan | 2 | 10% |
Malaysia | 1 | 5% |
France | 1 | 5% |
Brazil | 1 | 5% |
China | 1 | 5% |
Croatia | 1 | 5% |
Switzerland | 1 | 5% |
Other | 0 | 0% |
Unknown | 5 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 65% |
Scientists | 6 | 30% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 167 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 38 | 23% |
Student > Master | 27 | 16% |
Researcher | 18 | 11% |
Student > Bachelor | 12 | 7% |
Student > Doctoral Student | 8 | 5% |
Other | 21 | 13% |
Unknown | 43 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 47 | 28% |
Neuroscience | 25 | 15% |
Computer Science | 8 | 5% |
Medicine and Dentistry | 6 | 4% |
Psychology | 6 | 4% |
Other | 20 | 12% |
Unknown | 55 | 33% |