سامانه همایش‌ها TSTA | ثبت‌نام و ارسال مقاله کنفرانس‌ها و کنگره‌های ملی

صفحه اصلی / سی و دومین کنفرانس ملی و دهمین کنفرانس بین المللی مهندسی زیست پزشکی ایران

A Combined Time-Frequency and Common Spatial-Spectral Pattern Approach for EEG-Based Motor Imagery Classification

نویسندگان :

Reza Nejati¹ Hamed Danandeh Hesar²

1- Sahand University of Technology 2- Sahand University of Technology

کلمات کلیدی :

Motor Imagery Tasks،Tunable-Q Wavelet Transform،Common Spatial-Spectral Patterns

چکیده :

Brain-Computer Interfaces (BCIs) are revolutionizing neurorehabilitation, providing crucial communication and control for individuals with severe motor impairments from conditions like ALS, spinal cord injuries, or stroke. By creating direct links between brain activity and external devices, BCIs bypass damaged neural pathways, thus restoring motor function and significantly enhancing quality of life. Electroencephalography (EEG) is a favored BCI modality due to its accessibility and cost-effectiveness. However, a major challenge lies in the substantial impact of cognitive and individual differences on motor imagery (MI) task performance and overall BCI accuracy. This research introduces a novel method to overcome these challenges, focusing on enhanced MI classification. Our approach synergistically integrates Common Spatial-Spectral Pattern (CSSP) filters with the Tunable-Q Wavelet Transform (TQWT). This powerful combination was applied to the extensive CHO-2017 database (52 participants), which uniquely captures significant inter-individual cognitive variations, specifically to distinguish between left and right-hand MI tasks. A critical aspect of our method is the utilization of only the top 10 most discriminative features extracted through this hybrid technique. This deliberate streamlining maximizes classification efficacy while maintaining computational efficiency. This tailored feature set demonstrated remarkable effectiveness, performing across 99% of participants. When integrated with a K-Nearest Neighbors (KNN) classifier, this approach achieved an outstanding accuracy of 98.84%, notably surpassing existing state-of-the-art methods in the field. These findings hold significant promise for developing more accurate and robust BCI systems capable of extracting optimal commands for diverse MI applications, ultimately advancing neurorehabilitation outcomes.