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A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user
Shams Al Ajrawi1, Ramesh Rao2, Mahasweta Sarkar3
1Department of Electrical and Computer Engineering, University of California, San Diego, Alliant International univercity, San Diego, CA, USA; CSML, Alliant International University, San Diego, San Diego, CA, USA; Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, USA.
Background:
With the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles.
New Method:
Due to this, we extracted and classified the brain activity in this study, and we also presented a novel hierarchical recursive feature elimination method that we refer to as HRFE embracing noisy additions. HRFE makes a variety of categorization suggestions to eliminate bias in classifying BCI systems of different types. We put the HRFE to the test on two BCI signal data sets-specifically, dataset I and BCI contests III-using shallow and deep convolution network classification techniques. Just a grid of assets is used to create electrocorticography (ECoG) signals on the contralateral (right) motor cortex, and these signals are recorded in the BCI contests III database.
Results:
Using ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection.
Comparison With Existing Methods:
The simulation findings show that HRFE has a significant computer vision enhancement when compared to comparable feature selection methods in the literature, particularly for ECoG signal, which achieves about 93% reliability.
