Stationary Transfer Component Analysis for Brain Computer Interfacing

2012 ◽  
Author(s):  
Sidath R. Liyanage ◽  
Jialin S. Pan ◽  
Haihong Zhang ◽  
Kai Keng Ang ◽  
Cuntai Guan ◽  
...  
Keyword(s):  
Component Analysis ◽  
Brain Computer Interfacing ◽  
Computer Interfacing

scholarly journals Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Suogang Wang ◽  
Christopher J. James
Keyword(s):  
Independent Component Analysis ◽  
Computational Cost ◽  
Rhythmic Activity ◽  
Component Analysis ◽  
Independent Component ◽  
Classification Rate ◽  
Eeg Data ◽  
Brain Computer Interfacing ◽  
Bci System ◽  
Computer Interfacing

We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

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