Short-Term Fourier Transform as Preprocessing for Common Spatial Pattern

Brain-Computer Interface (BCI) allows direct communication between the human brain and a computer system. Motor Imagery (MI), a prominent BCI paradigm, decodes a mental activity of motor planning in a message to a device. This process can incorporate signal acquisition, preprocessing, temporal and spatial filtering, feature extraction, and classification. Common Spatial Pattern is a well-known method for spatial filtering in BCI. Combining CSP with temporal filtering methods, such as Bandpass and Empirical Mode Decomposition, improves efficiency. This study proposes using CSP in the time-frequency domain with Short-Time Fourier Transform (STFT) as a temporal filter. STFT detects fluctuations in brain activity across time and frequency. It can recognize MI-related elements at various frequencies. We compared STFT-CSP with CSP, Filter Bank CSP, Median-SEE, and Sigmoid-SEE concerning the task of distinguishing between left and right hand movements. The proposed STFT-CSP achieved the best overall result and was more robust than the other approaches. Moreover, by analysing the performance profiles, we can conclude that in the worst-case scenario, the accuracy was improved in more than 30%. Therefore, the method increased result stability across the subjects. The proposed STFT-CSP proved to be more consistent than other approaches and more suitable for real-world applications.