Motor-Related EEG Analysis Using a Pole Tracking Approach

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 3837 - 3847
• Main Authors: Kostoglou, Kyriaki, Muller-Putz, Gernot R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024
• Subjects: Adult; Algorithms; Analytical models; autoregressive poles; brain activity; Brain modeling; brain-computer interface; Brain-Computer Interfaces; Cascade filters; Covariance matrices; Electroencephalography; Electroencephalography - methods; Evoked Potentials, Motor - physiology; Feature extraction; Female; Healthy Volunteers; Humans; Imagination - physiology; Male; Middle Aged; Motor Cortex - physiology; Movement - physiology; movement classification; Reproducibility of Results; Sensitivity and Specificity; Spinal Cord Injuries - physiopathology; Stability analysis; Time-frequency analysis; Tracking; Transfer functions; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3483294

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.