A review of hybrid brain-computer interface systems <s> EEG (MI-Based)-NIRS Hybrid BCI. <s> A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user. Ultimately this can lead to abandonment of the device. The primary reason for this is that relatively indirect measures of cognitive function, as can be gleaned from the electroencephalogram (EEG), are harnessed. A more suitable and usable interface would need to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. In this paper, we propose a novel approach, using non-invasive near-infrared imaging technology to develop a user-friendly optical BCI. As an alternative to the traditional EEG-based devices, we have used practical non-invasive optical techniques to detect characteristic haemodynamic responses due to motor imagery and consequently created an accessible BCI that is simple to attach and requires little user training. <s> BIB001 </s> A review of hybrid brain-computer interface systems <s> EEG (MI-Based)-NIRS Hybrid BCI. <s> In recent years, study of brain computer interface (BCI) is conducted actively and many researches of implementation using electro encephalic gram (EEG) are reported. On the other hand, some realization of BCI based on near- infrared spectroscopy (NIRS) also had been reported. Since a measurement instrument for NIRS is comparatively small- scale and it can perform noninvasive measurements, NIRS is expected as one of useful tool in order to realize versatile BCIs. In this paper, the estimation method is shown the possibility of applications to the ON/OFF control of BCI by NIRS. We measured regional cerebral blood flow during tapping movement of the right hand by NIRS and we propose a method to quantitatively estimate start and end timing of movement by using a neural network. <s> BIB002 </s> A review of hybrid brain-computer interface systems <s> EEG (MI-Based)-NIRS Hybrid BCI. <s> Abstract 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 <s> BIB003
A type of hybrid BCI that uses EEG and NIRS BIB002 was introduced by BIB003 . Coyle et al. BIB001 introduced an approach of utilizing NIRS as an optical BCI. In BIB003 , EEG and NIRS measurements were utilized simultaneously for ERD-based BCIs. In this study, the experiment consisted of 2 blocks of motor execution and 2 blocks of motor imagery. For all blocks, both EEG and NIRS were measured simultaneously. The increase in concentration of oxygenated hemoglobins (HbO) and decrease in concentration of deoxygenated hemoglobins (HbR) were measured using NIRS. The global peak cross-validation accuracy for each subject was considered for evaluation of the hybrid BCI. The mean classification accuracies of HbO, HbR, and EEG for executed movement tasks were 71.1%, 73.3%, and 90.8%. For motor imagery tasks they were 71.7%, 65.0%, and 78.2%. The mean classification accuracies of EEG/HbO, EEG/HbR, and EEG/HbO/HbR for executed movement tasks were 92.6%, 93.2%, and 87.4%, and for motor imagery tasks were 83.2%, 80.6%, and 83.1%, respectively. It was shown that the combination of EEG and NIRS improved the classification accuracy in both MI and executed movement tasks. However, the information transfer rate may decrease. This type of hybrid BCI may enhance the performance of subjects who are not able to use EEG-based BCI properly.