Advancements in Temporal Fusion: A New Horizon for EEG-Based Motor Imagery Classification

BCIs facilitate seamless engagement between individuals with motor disabilities and their surrounding environment by translating electroencephalography (EEG) signals generated from Motor Imagery (MI). Crucial to this process is the accurate classification of different types of MI tasks - a challenge that calls for the consistent evolution and refinement of reliable methodologies for EEG signal classification. This paper introduces three innovative approaches: M1, employing a temporal block technique combined with Filter Bank Common Spatial Pattern (FBCSP) and mutual information-based feature selection with a Random Forest classifier; and M2 and M3, extending M1 using Temporal Probability Fusion (TPF) and Probability Difference-based Temporal Fusion (PDTF) respectively. These methods aim to enhance MI EEG signal classification. The effectiveness of M1, M2, and M3 was scrutinized under differing scenarios including changing overlap sizes and channel choices. The analysis highlights that our methods exhibit enhanced performance under particular conditions, underlining the crucial role of temporal information and channel selection. Comparison with established methodologies verifies the superior efficiency of our proposed strategies. This study foregrounds the considerable potential of TPF and PDTF in MI EEG classification tasks, with significant implications for the future development of BCI systems.