  An accurate classification of upper limb movements using
electroencephalography (EEG) signals is gaining significant importance in
recent years due to the prevalence of brain-computer interfaces. The upper
limbs in the human body are crucial since different skeletal segments combine
to make a range of motion that helps us in our trivial daily tasks. Decoding
EEG-based upper limb movements can be of great help to people with spinal cord
injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral
sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can
manifest in a loss of sensory and motor function, which could make a person
reliant on others to provide care in day-to-day activities. We can detect and
classify upper limb movement activities, whether they be executed or imagined
using an EEG-based brain-computer interface (BCI). Toward this goal, we focus
our attention on decoding movement execution (ME) of the upper limb in this
study. For this purpose, we utilize a publicly available EEG dataset that
contains EEG signal recordings from fifteen subjects acquired using a
61-channel EEG device. We propose a method to classify four ME classes for
different subjects using spectrograms of the EEG data through pre-trained deep
learning (DL) models. Our proposed method of using EEG spectrograms for the
classification of ME has shown significant results, where the highest average
classification accuracy (for four ME classes) obtained is 87.36%, with one
subject achieving the best classification accuracy of 97.03%.
