Brain-Computer Interface Based on Compressive Sensing and Steady-State Visual Evoked Potentials Applied to Command a Robotic Wheelchair
Nombre: HAMILTON RIVERA FLOR
Fecha de publicación: 21/09/2023
Supervisor:
Nombre |
Papel |
|---|---|
| RICARDO CARMINATI DE MELLO | Co-advisor * |
| TEODIANO FREIRE BASTOS FILHO | Advisor * |
Junta de examinadores:
| Nombre |
Papel |
|---|---|
| ALAN SILVA DA PAZ FLORIANO | External Examiner * |
| ANTONIO MAURICIO FERREIRA LEITE MIRANDA DE SÁ | External Examiner * |
| DENIS DELISLE RODRIGUEZ | Co advisor * |
| EDUARDO LÁZARO MARTINS NAVES | External Examiner * |
| EMANUEL SLAWINSKI | External Examiner * |
Páginas
Sumario: Peoplewithseverephysicaldisabilitiesareableofusingroboticwheelchairs,whichgenerallydemandsomemotorskills,andthereforetotalusageofassociatemuscles.Robotic wheelchairscommandedbyBrain-ComputerInterfaces(BCIs)basedonElectroencephalography(EEG)havedemonstratedtobeanalternativefortheseend-users,assuchsystems translatebrainpatternsongoingEEGsignalsintocontrolcommands.However,BCIs relyingonlocalprocessingencounterlimitationsinpower,scalability,andreal-time.In general,existingroboticwheelchairscommandedbyBCIsrequirepowerfulhardwarefor highspeedsignalprocessing.Ontheotherhand,end-usersneedalongtrainingprocess forsafelydrivingthesedevices.Asasolution,cloud-basedBCIsandcloudroboticshave emerged,leveragingcloudcomputingforhigh-performancedataprocessing,storage,and analysis.Thisintegrationempowersadvancedandadaptiveroboticassistance,transformingtele-rehabilitationande-healthapplicationsforpeoplewithdisabilities.However, integratingcloudcomputingwithBCIsintroducesitsownsetofchallenges.Theseinclude anefficientandreliabletransmissionoflargevolumesofdataandstablecommunication betweenthebrainsignalsensor,cloudinfrastructure,androboticwheelchair.Toaddress thesechallenges,thisthesisintroducesanovelCloud-BCISystemforwheelchaircommand throughtheuseofSteady-StateVisualEvokedPotential(SSVEP),CompressiveSensing (CS)techniques,andacommunicationframework.ThesystemenhancesInformationTransferRate,ensuringstablecommunicationamongtheBCI,cloudinfrastructure,androbotic wheelchair.LeveragingcloudService-OrientedarchitectureandRoboticOperatingSystem (ROS),thesystemallowsforeasyintegrationofdiverseroboticplatformsandprovides flexibilitytointegratevariousprotocols,classifiers,metrics,andcommandtechniques. Inconclusion,thecloud-BCIsystemdemonstratestobeanefficientandflexiblesolutionfor commandingaroboticwheelchair,makingitavaluabletoolforresearchersanddevelopers inthefieldofassistivetechnologies,tele-rehabilitationandtrainingscenarios.
