A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making

• Published in: Journal of neuroscience methods 2024-08, Vol.408, p.110161, Article 110161
• Main Authors: Ajrawi, Shams Al, Rao, Ramesh, Sarkar, Mahasweta
• Format: Article
• Language: English
• Subjects: Algorithms; Brain - physiology; Brain computer interface (BCI); Brain-Computer Interfaces; Decision Making - physiology; Electrocorticography; Electrocorticography - methods; Electroencephalography - methods; Feature, extraction; Humans; Machine learning (ML); Multiple classifiers; Signal Processing, Computer-Assisted
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2024.110161

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. 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. Using ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection. 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. •A BCI classifier's architecture is quite difficult since numerous elements should be combined.•In this research, extraction and classification of the brain activity is done.•HRFE is put to the test on two BCI signal data sets—specifically, dataset I and BCI contests III.•HRFE has a significant computer vision enhancement when compared to comparable feature selection methods.•For ECoG signal, 93% reliability is achieved.