Brain-Computer Interfaces (BCIs) hold great potential to transform lives of those with motor impairments, but their efficacy is constrained by individual neural variability. Neural data is highly complex, so we chose a linear and non-linear filtering method to compare their adaptability. Robust and adaptive decoding methods are crucial for preparing and optimizing neural data, ensuring its interpretability and accessibility for BCIs. This study compares two approaches for electrocorticography (ECoG) data processing: Linear Spatial Filtering and Consistent Embeddings of High-Dimensional Behavioral and Recordings (CEBRA). Spatial Filtering significantly improved the average signal-to-noise ratio (SNR) for one subject from 3.1 dB to 8.3 dB; however, the extracted features exhibited poor class separability, resulting in weak classification performance. On the other hand, CEBRA successfully identified distinct latent patterns and preserved relevant data structures but lacked the generalizability needed for reliable stimulus classification on new subjects. These findings suggest that neither method alone is sufficient, revealing the need for additional processing techniques or hybrid approaches to improve BCI adaptability.