Brain-Computer Interface Based on Unsupervised Methods to Recognize Motor Intention for Command of a Robotic Exoskeleton
Nombre: DENIS DELISLE RODRIGUEZ
Fecha de publicación: 01/12/2017
Supervisor:
Nombre |
Papel |
|---|---|
| ANSELMO FRIZERA NETO | Co-advisor * |
| TEODIANO FREIRE BASTOS FILHO | Advisor * |
Junta de examinadores:
| Nombre |
Papel |
|---|---|
| ANSELMO FRIZERA NETO | Co advisor * |
| ANTÔNIO PADILHA LANARI BÓ | External Examiner * |
| EDUARDO ROCON DE LIMA | External Examiner * |
| ELIETE MARIA DE OLIVEIRA CALDEIRA | Internal Examiner * |
| PATRICK MARQUES CIARELLI | Internal Examiner * |
Páginas
Sumario: Stroke and road traffic injuries may severely affect movements of lower-limbs in humans, and
consequently the locomotion, which plays an important role in daily activities, and the quality
of life. Robotic exoskeleton is an emerging alternative, which may be used on patients with
motor deficits in the lower extremities to provide motor rehabilitation and gait assistance. However,
the effectiveness of robotic exoskeletons may be reduced by the autonomous ability of the
robot to complete the movement without the patient involvement. Then, electroencephalography
signals (EEG) have been addressed to design brain-computer interfaces (BCIs), in order to
provide a communication pathway for patients perform a direct control on the exoskeleton using
the motor intention, and thus increase their participation during the rehabilitation. Specially,
activations related to motor planning may help to improve the close loop between user and
exoskeleton, enhancing the cortical neuroplasticity. Motor planning begins before movement
onset, thus, the training stage of BCIs may be affected by the intuitive labeling process, as it is
not possible to use reference signals, such as goniometer or footswitch, to select those time periods
really related to motor planning. Therefore, the gait planning recognition is a challenge,
due to the high uncertainty of selected patterns, However, few BCIs based on unsupervised
methods to recognize gait planning/stopping have been explored.
This Doctoral Thesis presents unsupervised methods to improve the performance of BCIs during
gait planning/ stopping recognition. At this context, an adaptive spatial filter for on-line
processing based on the Concordance Correlation Coefficient (CCC) was addressed to preserve
the useful information on EEG signals, while rejecting neighbor electrodes around the electrode
of interest. Here, two methods for electrode selection were proposed. First, both standard deviation
and CCC between target electrodes and their correspondent neighbor electrodes are
analyzed on sliding windows to select those neighbors that are highly correlated. Second, Zscore
analysis is performed to reject those neighbor electrodes whose amplitude values presented
significant difference in relation to other neighbors.
Furthermore, another method that uses the representation entropy and the maximal information
compression index (MICI) was proposed for feature selection, which may be robust to
select patterns, as only it depends on cluster distribution. In addition, a statistical analysis
was introduced here to adjust, in the training stage of BCIs, regularized classifiers, such as
support vector machine (SVM) and regularized discriminant analysis (RDA).
Six subjects were adopted to evaluate the performance of different BCIs based on the proposed
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methods, during gait planning/stopping recognition.
The unsupervised approach for feature selection showed similar performance to other methods
based on linear discriminant analysis (LDA), when it was applied in a BCI based on the traditional
Weighted Average to recognize gait planning. Additionally, the proposed adaptive filter
improved the performance of BCIs based on traditional spatial filters, such as Local Average
Reference (LAR) and WAR, as well as others BCIs based on powerful methods, such as Common
Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP) and Riemannian
kernel (RK). RK presented the best performance in comparison to CSP and FBCSP, which
agrees with the hypothesis that unsupervised methods may be more appropriate to analyze
clusters of high uncertainty, as those formed by motor planning.
BCIs using adaptive filter based on Zscore analysis, with an unsupervised approach for feature
selection and RDA showed promising results to recognize both gait planning and gait stopping,
achieving for three subjects, good values of true positive rate (>70%) and false positive (<16%).
Thus, the proposed methods may be used to obtain an optimized BCI that preserves the useful
information, enhancing the gait planning/stopping recognition. In addition, the method
for feature selection has low computational cost, which may be suitable for applications that
demand short time of training, such as clinical application time.
