Recognition Of Digits From EEG Signals: An Innovative Approach

Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent of deep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. This study proposes a methodological combination of EEG signal processing techniques and SVM models for the classification of digit. Also, it explores DCT and LDA techniques for feature extraction and selection. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.