Enhanced performance by a hybrid NIRS–EEG brain computer interface

• Published in: NeuroImage (Orlando, Fla.) Vol. 59; no. 1; pp. 519 - 529
• Main Authors: Fazli, Siamac, Mehnert, Jan, Steinbrink, Jens, Curio, Gabriel, Villringer, Arno, Müller, Klaus-Robert, Blankertz, Benjamin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 02.01.2012; Elsevier Limited
• Subjects: Adult; Brain; Brain - physiology; Combined NIRS-EEG; Electroencephalography; Electroencephalography - methods; Experiments; Humans; Hybrid BCI; Image Interpretation, Computer-Assisted; Imagination - physiology; Infrared imaging systems; Meta-classifier; NMR; Nuclear magnetic resonance; Signal Processing, Computer-Assisted; Spectroscopy, Near-Infrared - methods; Studies; User-Computer Interface; Young Adult; Hybrid BCI; Meta-classifier; Combined NIRS-EEG
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.07.084

Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI. ► We use multi-modal imaging (whole-head NIRS and EEG) for sensory motor rhythm based BCI. ► By the use of meta-classifiers we enhance performance by 5% on average. ► Some subjects, previously not able to operate a BCI, now become able to do so. ► NIRS and EEG have different information content and complement each other.