Introduction: Motor BCI, leveraging AI and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to a role of the insula in motor control, specifically directional hand-movements. In this study, we applied AI and machine learning techniques to decode directional hand-movements from high-gamma band (70-150Hz) activity in the insular cortex.
Methods: 7 participants with medication-resistant epilepsy underwent stereo electroencephalographic (SEEG) implantation of depth electrodes for seizure monitoring in the insula. SEEG data were sampled throughout a cued motor task involving three conditions: left-hand movement, right-hand movement, or no movement. Neural signal processing focused on high-gamma band activity. Demixed Principal Component Analysis (dPCA) was used for dimension reduction (d=10) and feature extraction from the spectrogram. For movement classification, we implemented a bidirectional Long Short-Term Memory (LSTM) architecture with a single layer, leveraging the capacity to process temporal sequences in forward and back directions for optimal decoding of movement intentions.
Results: Our findings revealed robust movement-specific high-gamma modulation within the insular cortex during motor execution. Temporal decomposition through dPCA demonstrated distinct spatiotemporal patterns of high-gamma across movement conditions. Subsequently, LSTM networks successfully decoded these condition-specific neural signatures, achieving a classification accuracy of 75.0% ± 13.0% (mean ± SD), which significantly exceeded chance-level performance of 33.3% (p < 0.0001, n = 16). Furthermore, we identified a strong negative correlation between temporal distance of training-testing sessions and decoding performance (r = -0.87, p < 0.0001), indicating temporal degradation of the neural representations.
Conclusion : Our study highlights the potential role of deep brain structures, such as the insula, in conditional movement discrimination. We demonstrate that LSTM networks and high-gamma band analysis can advance the understanding of neural mechanisms underlying movement. These insights may pave the way for improvements in SEEG-based BCI.
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