Abstract
A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.
Highlights
Intelligent methods of data processing have begun to play a crucial role in the personalization of various areas of human activity
The availability and miniaturization of computer technology has created the prerequisites for the widespread use of new-generation human–machine interfaces, such as brain–computer interfaces (BCIs) or neural interfaces [1,2]
To perform a comparative analysis, baseline accuracy was obtained with the traditional combination of common spatial pattern (CSP) and a linear classifier [20]
Summary
Intelligent methods of data processing have begun to play a crucial role in the personalization of various areas of human activity. The availability and miniaturization of computer technology has created the prerequisites for the widespread use of new-generation human–machine interfaces, such as brain–computer interfaces (BCIs) or neural interfaces [1,2]. The development of BCIs is carried out in a multidisciplinary approach at the intersection of information technology and neuroscience and is, perhaps, one of the most intensively growing and promising areas of applied research. The primary goal, as is widely known, is to create a new reliable communication channel for the rehabilitation of people with speech and motor activity disorders [3,4]. BCI implements its communication function by decoding various types of mental commands of an individual, including evoked brain activity (ERP) and voluntary EEG-induced patterns (motor imagery and inner speech) that are formed in the brain electrical activity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
