Recent advances and open challenges in hybrid brain-computer interfacing: a technological review of non-invasive human research

被引:39
作者
Banville, H. [1 ]
Falk, T. H. [1 ]
机构
[1] Univ Quebec, Inst Natl Rech Sci Energy Mat & Telecommun, Montreal, PQ H5A 1K6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
brain-computer interfaces; hybrid; multimodal; EEG; NIRS; body-machine interface; physiological computing;
D O I
10.1080/2326263X.2015.1134958
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Context. In recent years, hybrid brain-computer interfaces (hBCIs) have proven to be a promising path towards practical brain-computer interfacing. These hybrid interfaces capitalize on the concurrent recording of various physiological signals, or of the elicitation of more than one mental process, to increase the number of possible input commands and achieve more flexible and robust systems. Although hBCIs have previously been reviewed in some articles, a more recent, and complete survey of the literature is missing to lay the foundations for further research. Objective. This work aims at systematically reviewing recent articles on the topic of non-invasive hBCIs, to comprehensively identify the current trends, limitations and challenges that these studies report. Methods. Three major databases covering the fields of science and engineering were queried. From these and others sources, 55 journal articles from 2008 to November 2014 were selected and analyzed. Results. Twenty-two items were investigated to offer a complete perspective on the current state of non-invasive hBCI research in humans, covering the study rationale, experimental protocol, signal-processing methodology, and system evaluation. Based on this analysis, recommendations were formulated to direct further work in the field. Significance. We hope this review will constitute the groundwork for future hBCI studies.
引用
收藏
页码:9 / 46
页数:38
相关论文
共 176 条
[61]  
Jerritta S., 2011, 2011 Proceedings of IEEE 7th International Colloquium on Signal Processing & its Applications (CSPA 2011), P410, DOI 10.1109/CSPA.2011.5759912
[62]   A Novel Semi-supervised Deep Learning Framework for Affective State Recognition on EEG Signals [J].
Jia, Xiaowei ;
Li, Kang ;
Li, Xiaoyi ;
Zhang, Aidong .
2014 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE), 2014, :30-37
[63]   Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals [J].
Jiang, Jun ;
Zhou, Zongtan ;
Yin, Erwei ;
Yu, Yang ;
Hu, Dewen .
BIO-MEDICAL MATERIALS AND ENGINEERING, 2014, 24 (06) :2919-2925
[64]   Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG [J].
Kaiser, Vera ;
Bauernfeind, Guenther ;
Kreilinger, Alex ;
Kaufmann, Tobias ;
Kuebler, Andrea ;
Neuper, Christa ;
Mueller-Putz, Gernot R. .
NEUROIMAGE, 2014, 85 :432-444
[65]  
Kaiser V, 2011, FRONT NEUROSCI-SWITZ, V5, DOI [10.3389/fnins.2011.00086, 10.3389/fninf.2011.00030]
[66]   Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface [J].
Khan, M. Jawad ;
Hong, Melissa Jiyoun ;
Hong, Keum-Shik .
FRONTIERS IN HUMAN NEUROSCIENCE, 2014, 8
[67]   Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking [J].
Kim, Byung Hyung ;
Kim, Minho ;
Jo, Sungho .
COMPUTERS IN BIOLOGY AND MEDICINE, 2014, 51 :82-92
[68]   Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia [J].
Kim, Sung-Phil ;
Simeral, John D. ;
Hochberg, Leigh R. ;
Donoghue, John P. ;
Friehs, Gerhard M. ;
Black, Michael J. .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2011, 19 (02) :193-203
[69]  
Kleber B, 2005, COGNIT PROCESS, V6, P65
[70]   DEAP: A Database for Emotion Analysis Using Physiological Signals [J].
Koelstra, Sander ;
Muhl, Christian ;
Soleymani, Mohammad ;
Lee, Jong-Seok ;
Yazdani, Ashkan ;
Ebrahimi, Touradj ;
Pun, Thierry ;
Nijholt, Anton ;
Patras, Ioannis .
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2012, 3 (01) :18-31