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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
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Transfer Learning in Trajectory Decoding: Sensor or Source Space?

Nitikorn Srisrisawang1, Gernot R Müller-Putz1,2

  • 1Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

Transfer learning for brain-computer interfaces (BCI) showed limited success in decoding hand trajectories across participants and sessions. Individual sensor-space models performed best, highlighting challenges in generalizable BCI calibration.

Keywords:
brain–computer interfaceelectroencephalographypartial least-squares regressionsource localizationtrajectory decodingtransfer learningunscented Kalman filter

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interface (BCI) systems require extensive calibration, limiting their practical application.
  • Transfer learning offers a potential solution to reduce BCI calibration time by leveraging existing data.
  • Continuous hand trajectory decoding is a key BCI application area with significant potential.

Purpose of the Study:

  • To investigate the effectiveness of across-participant and across-session transfer learning for minimizing BCI calibration time.
  • To evaluate different transfer learning strategies for continuous hand trajectory decoding.
  • To compare the performance of sensor-space versus source-space features in transfer learning scenarios.

Main Methods:

  • Reanalysis of existing BCI data from 10 participants across three sessions.
  • Utilized a leave-one-participant-out (LOPO) model as a baseline.
  • Employed Recursive Exponentially Weighted Partial Least Squares Regression (REW-PLS) for efficient model training.
  • Compared four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), individual with cumulative update (IndC), and individual non-cumulative update (Ind).

Main Results:

  • Generalized models (Gen, GenC) performed below chance level, indicating poor cross-participant and cross-session generalization.
  • Individual models (IndC) did not significantly improve performance over non-cumulative models (Ind).
  • The best decoding performance was achieved using individual models trained with sensor-space features, outperforming source-space features.

Conclusions:

  • Current transfer learning approaches demonstrate an inability to generalize effectively across participants and sessions for this hand trajectory decoding task.
  • Sensor-space features provided superior performance compared to source-space features in individual BCI models.
  • The decoding patterns in individual models were localized around the precuneus, suggesting task-specific neural representations.