  People undergoing neuromuscular dysfunctions and amputated limbs require
automatic prosthetic appliances. In developing such prostheses, the precise
detection of brain motor actions is imperative for the Grasp-and-Lift (GAL)
tasks. Because of the low-cost and non-invasive essence of
Electroencephalography (EEG), it is widely preferred for detecting motor
actions during the controls of prosthetic tools. This article has automated the
hand movement activity viz GAL detection method from the 32-channel EEG
signals. The proposed pipeline essentially combines preprocessing and
end-to-end detection steps, eliminating the requirement of hand-crafted feature
engineering. Preprocessing action consists of raw signal denoising, using
either Discrete Wavelet Transform (DWT) or highpass or bandpass filtering and
data standardization. The detection step consists of Convolutional Neural
Network (CNN)- or Long Short Term Memory (LSTM)-based model. All the
investigations utilize the publicly available WAY-EEG-GAL dataset, having six
different GAL events. The best experiment reveals that the proposed framework
achieves an average area under the ROC curve of 0.944, employing the DWT-based
denoising filter, data standardization, and CNN-based detection model. The
obtained outcome designates an excellent achievement of the introduced method
in detecting GAL events from the EEG signals, turning it applicable to
prosthetic appliances, brain-computer interfaces, robotic arms, etc.
