How many channels are suitable for independent component analysis in motor imagery brain-computer interface

2019 ◽  
Vol 50 ◽  
pp. 103-120 ◽  
Author(s):  
Bangyan Zhou ◽  
Xiaopei Wu ◽  
Jing Ruan ◽  
Zhao LV ◽  
Lei Zhang
Keyword(s):  
Independent Component Analysis ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Component Analysis ◽  
Independent Component ◽  
Computer Interface

scholarly journals A New Approach to Separate Haemodynamic Signals for Brain-Computer Interface Using Independent Component Analysis and Least Squares

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Zhang ◽  
Xin Liu ◽  
Chunling Yang ◽  
Kuanquan Wang ◽  
Jinwei Sun ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Least Squares ◽  
Short Distance ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Component Analysis ◽  
Independent Component ◽  
Cardiac Contraction ◽  
Computer Interface ◽  
Long Distance

Brain-computer interface (BCI) is one technology that allows a user to communicate with external devices through detecting brain activity. As a promising noninvasive technique, functional near-infrared spectroscopy (fNIRS) has recently earned increasing attention in BCI studies. However, in practice fNIRS measurements can suffer from significant physiological interference, for example, arising from cardiac contraction, breathing, and blood pressure fluctuations, thereby severely limiting the utility of the method. Here, we apply the multidistance fNIRS method, with short-distance and long-distance optode pairs, and we propose the combination of independent component analysis (ICA) and least squares (LS) with the fNIRS recordings to reduce the interference. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Least squares is used to optimize the reconstruction value for brain activity signal. Monte Carlo simulations of photon propagation through a five-layered slab model of a human adult head were implemented to evaluate our methodology. The results demonstrate that the ICA method can separate the brain signal and interference; the further application of least squares can significantly recover haemodynamic signals contaminated by physiological interference from the fNIRS-evoked brain activity data.

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