Classification Of Complicated Upper Limb Movements From Pre-movement EEG Signals Using STFT And Spectral Characteristics

A promising technology for facilitating communication and control for people with disabilities is the brain-computer interface (BCI). Electroencephalogram (EEG) signals are frequently used in BCI systems; however, accurate classification of these signals remains challenging. This study introduces a brand-new technique for classifying EEG signals using spectral characteristics. Using pre-movement EEG recordings, the short-time Fourier transform (STFT) and spectral feature extraction are employed to produce an accurate classification method for upper limb dynamic movements. A bigger dataset of healthy people was used to test the suggested method, and other classification algorithms, such as Convolutional Neural Networks (CNN) and Residual Networks (Resnet), were used to assess its performance. The results show that the suggested method consistently produces high accuracy rates for all subjects and movements, with an overall accuracy of 88.7% and the highest accuracy of 100 % achieved on subject 5 during movement 3 using Resnet on a privately available dataset that was compiled from 12 healthy subjects and consisted of 5 types of upper-limb complex premovements that were done in 50 trails. Our study extends the previous work by using a different feature extraction method and classification algorithms on a larger dataset of healthy subjects, outperforming previous methods. Utilizing spectral features, our method could improve the accuracy of BCI systems in various applications, including medical diagnosis, control of assistive devices, and gaming software. Furthermore, this approach could also be extended to other types of signals beyond EEG, enabling accurate classification in a broader range of applications.