Brain-Computer Interface for Lower-Limb Neurorehabilitation: From Signal Analysis to Practical Applications
Name: CRISTIAN FELIPE BLANCO DIAZ
Type: MSc dissertation
Publication date: 30/08/2023
Advisor:
Name |
Role |
|---|---|
| TEODIANO FREIRE BASTOS FILHO | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| ALBERTO FERREIRA DE SOUZA | Co advisor * |
| DENIS DELISLE RODRIGUEZ | External Examiner * |
| RAFHAEL MILANEZI DE ANDRADE | Internal Examiner * |
| TEODIANO FREIRE BASTOS FILHO | Advisor * |
Summary: In recent years, the development of Brain-Computer Interfaces (BCI) with Electroencephalography (EEG) has gained recognition in the scientific community for implementing robotic systems for rehabilitation. For instance, Motorized Mini Exercise Bikes (MMEBs) have been used for passive assistance with control driven by Motor Imagery (MI). However, these BCIs face challenges, such as long calibrations and low customization in applications, and
intentionality detection with EEG signals during pedaling tasks has not been fully explored. This dissertation aims to use different algorithmic strategies on EEG signals for the detection of pedaling tasks using several algorithmic approaches to implement real-time neurorehabilitation ICCs. For this, protocols with active pedaling, passive pedaling, and MI tasks were executed, and different signal processing methodologies were addressed. Machine and deep
learning techniques were used to classify EEG signals with accuracies close to 0.95 for MI and 0.80 for active pedaling. Riemannian geometry-based methods were also used to identify MI tasks after passive pedaling at three different speeds (30, 45, and 60 rpm) with accuracies close to 0.78. Additionally, a BCI was designed with visual neurofeedback, passive pedaling assistance, and MI, which was evaluated in the online phase, achieving an accuracy of approximately 0.80, indicating that the subject aims to encourage modulations. Subsequently, it was possible to observe the cortical response in the parieto-central cortex of the brain during the session. The results allow concluding that the implemented methodologies are feasible and accurate for the design of robotic lower limb BCIs that allow more personalized physical and neural neurorehabilitation and better human-machine interaction, which could help in the restoration of skills of people with neuromotor disabilities. The results presented here open the door to continue exploring brain information during the development of lower-limb tasks that may allow technological innovation in ICC systems for rehabilitation. Additionally, the proposed system can be used in therapeutic interventions for people with neuromotor impairments, such as post-stroke or spinal cord injury populations.
