Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems

• Autores: Deepa Beeta Thiyam
• Directores de la Tesis: Sergio A. Cruces Álvarez (dir. tes.)
• Lectura: En la Universidad de Sevilla ( España ) en 2018
• Idioma: español
• Número de páginas: 160
• Tribunal Calificador de la Tesis: Elizabeth Rufus (presid.), S. Sivananthan (secret.), P.S. Pandian (voc.), Elagiri Ramalingam Rajkumar (voc.), Sergio A. Cruces Álvarez (voc.)
• Programa de doctorado: Programa de Doctorado en Ingeniería Automática, Electrónica y de Telecomunicación por la Universidad de Sevilla
• Enlaces
  • Tesis en acceso abierto en: Idus
• Resumen

  • Brain-Computer Interface (BCI) system provides a channel for the brain to control external devices using electrical activities of the brain without using the peripheral nervous system. BCIs operate external devices by acquiring brain signals and converting them to control commands to operate external devices. Motor-imagery (MI) based BCI systems, in particular, are based on the sensory motor rhythms which are generated by the imagination of body limbs. These signals can be decoded as control commands in BCI application. Electroencephalogram (EEG) is commonly used for BCI applications because it is non-invasive. The main challenges of decoding the EEG signal are because it is non-stationary and has low spatial resolution. The common spatial pattern algorithm is considered to be the most effective technique for discrimination of spatial filter but is easily affected by the presence of outliers. Therefore, a robust algorithm is required for extraction of discriminative features from the motor imagery EEG signals.

    This thesis mainly aims in developing robust spatial filtering criteria which are effective for classification of MI movements. We have proposed two approaches for the robust classification of MI movements. The first approach is for the classification of multiclass MI movements based on the thinICA (Independent Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method. The observed results indicate that these approaches can be a step towards the development of a robust feature extraction for MI based BCI system.

    The main contribution of the thesis is the second criterion, which is based on Alpha-Beta logarithmic-determinant divergence for classification of two class MI movements. A detailed study has been done by obtaining a link between the AB log det divergence and CSP criterion. We propose a scaling parameter "kappa" to enable similar way for selecting the respective filters like the CSP algorithm. Additionally, the optimization of the gradient of AB log-det divergence for this application was also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence) algorithm is proposed for the discrimination of two class MI movements. The robustness of this algorithm is tested with both the simulated and real data from BCI competition dataset. Finally, the resulting performances of the proposed algorithms have been favourably compared with other existing algorithms.