Current applications of Brain-Machine Interfaces (BMIs) primarily focus on decoding user intentions directed toward the final target, often neglecting the sequential actions required to reach that target. This limitation is largely due to the susceptibility of electroencephalogram (EEG) sensors commonly used in BMI systems to noise. As a result, recent research has shifted its focus toward motor imagery tasks, often discarding motor execution-based BMI systems. In this work, we address the challenge of decoding detailed motion trajectories from brain signals. Rather than relying solely on noisy EEG signals, we integrate current movement information along with temporal features to incorporate time constraints into the learning process. Experimental results demonstrate that the proposed model can accurately predict the next target location from brain signals, producing trajectories that closely resemble the ground truth.