Multi-Class Motor Imagery Detection using optimum Channels
Keywords:
FBCSP, Mutual Information, Motor Imagery, CNN, Kohen KappaAbstract
The Brain-Computer Interface (BCI) finds application in various fields such as robotics and environmental control, particularly benefiting individuals with disabilities. Electroencephalography (EEG) signals serve as a prevalent choice for a typical BCI system due to the non-invasive, cost-effective, and portable nature of electrodes. EEG data is often collected from a large number of channels across the brain, so effective channel selection techniques play a vital role in identifying the best channels for a given application. Channel selection helps to decrease setup time and computational complexity while analyzing EEG signals. By eliminating noisy channels, channel selection can improve system performance. The Filer Bank Common Spatial Pattern (FBCSP) based Convolutional Network (CNN) is used to distinguish between four motor imagery (MI) tasks. A sliding window technique is utilized to generate time-varying data on EEG signals. The results obtained from the experimentation of the proposed method on BCI competition IV dataset 2a demonstrate a noteworthy average accuracy of 92.66% across 22 channels. This performance surpasses that of numerous existing systems. Additionally, when employing the mutual information technique for channel selection, extended experimental results revealed a commendable classification accuracy of 89.1% with five channels and 90.66% with three channels. Notably, the use of three channels exhibited an average kappa value of 0.86. These outcomes underscore the efficacy of our proposed system for real-time BCI development. The robustness of the model is further validated by its ability to achieve an accuracy of 89.3% on BCI competition IV dataset 2b. Thus, our proposed model demonstrates consistent and commendable results across both datasets, affirming its potential for practical and reliable application in brain-computer interface systems.
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