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An accurate classification of upper limb movements usingelectroencephalography (EEG) signals is gaining significant importance inrecent years due to the prevalence of brain-computer interfaces. The upperlimbs in the human body are crucial since different skeletal segments combineto make a range of motion that helps us in our trivial daily tasks. DecodingEEG-based upper limb movements can be of great help to people with spinal cordinjury (SCI) or other neuro-muscular diseases such as amyotrophic lateralsclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This canmanifest in a loss of sensory and motor function, which could make a personreliant on others to provide care in day-to-day activities. We can detect andclassify upper limb movement activities, whether they be executed or imaginedusing an EEG-based brain-computer interface (BCI). Toward this goal, we focusour attention on decoding movement execution (ME) of the upper limb in thisstudy. For this purpose, we utilize a publicly available EEG dataset thatcontains EEG signal recordings from fifteen subjects acquired using a61-channel EEG device. We propose a method to classify four ME classes fordifferent subjects using spectrograms of the EEG data through pre-trained deeplearning (DL) models. Our proposed method of using EEG spectrograms for theclassification of ME has shown significant results, where the highest averageclassification accuracy (for four ME classes) obtained is 87.36%, with onesubject achieving the best classification accuracy of 97.03%.

Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface