Abstract
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.
Highlights
The electroencephalogram (EEG) is a recording of the brain’s electrical activity and is one of the most important measurements used to evaluate neurological disorders in the clinic and to investigate brain function in the laboratory
The results show that following this pre-processing, even a simple linear classifier can achieve superior classification accuracy
In order to demonstrate the performance of the proposed constrained ICA (cICA), we only applied the power feature in the μ-rhythm frequency band as the major classification pattern
Summary
The electroencephalogram (EEG) is a recording of the brain’s electrical activity and is one of the most important measurements used to evaluate neurological disorders in the clinic and to investigate brain function in the laboratory. The primary aim is to provide people with a new channel for communication with the outside environment Many different disorders, such as amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, and numerous other diseases can disrupt the neuromuscular channels through which the brain communicates with its environment and exerts control. In the absence of methods for repairing the damage caused by these diseases, a BCI system provides an option that conveys messages and commands to use some devices such as assistive applications and computers This type of direct brain interface would increase an individual’s independence and improve quality of life and reduce the costs on society
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
