A Novel Approach to Motor Imagery EEG Signal Transformation and Classification Using Stockwell Transform and Deep Learning Models

Motor imagery (MI) classification using EEG signals has gained popularity, playing an essential role in developing technologies such as brain-computer interfaces (BCIs). This paper proposes novel approaches using the Stockwell transform (S-transform) to encode signals into images in time-frequency space and classify them by feeding them to pre-trained Inception-ResNet-V2, AlexNet, and SqueezeNet CNNs. High subject-to-subject and session-to-session signal variability hinder the recognition of MI tasks. Most literature has studied within-subject performance. This study conducted experiments using a leave-one-subject-out cross-validation strategy, investigated inter-subject variation's effect and contributed by evaluating the model's performance and generalization ability. At the same time, different sessions and the presence or absence of feedback were assessed, and the results were analyzed. The results are encouraging, considering the difficulty of classifying MI and inter-subject differences. For a cue-based paradigm and non-feedback signals, the results are between 62.1-80.8%; for signals with smiley feedback, the results are between 57.1-96.3%; and for signals with and without feedback are between 56.8-91.4%. These findings highlight the potential of combining the S-transform with CNNs, offering valuable insights into inter-subject variability in EEG-based BCI applications.