optimizing performance of non-expert users in brain-computer interaction by means of an adaptive...
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Title of the presentation
Optimizing Performance of Non-Expert Users in Brain-Computer Interaction by Means of an Adaptive Performance EngineAndr Ferreira, Athanasios Vourvopoulos, Sergi Bermudez i BadiaMadeira-ITI, University of Madeira, Portugal{[email protected]}
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Motor-Imagery (MI) BCI training is considered the most important type of BCI paradigm for motor function restoration
MI-BCI have shown beneficial effects of motor imagery practice during stroke recovery [9]
Combination of BCIs and virtual reality has been proven useful to train functional upper limb pointing movements [13], [14]How can BCIs help in Motor Restoration?
Athanasios Vourvopoulos - BIH152VR
The use in clinical environment is limited [15] and hardly used outside laboratory environments [16]
Current BCI systems lack reliability and good performance in comparison with other types of interfaces [17]
Users undergo long, tiresome and complex periods of training so that EEG classification algorithms can reach acceptable performance ratesCurrent Limitations
Athanasios Vourvopoulos - BIH153
Create a MI-BCI interaction adaptive to the user guarantee a satisfactory performance by softening decisions makingWhat is Our Aproach?
Athanasios Vourvopoulos - BIH154
20 EEG Datasets 12 Users (4 female)g.MOBIlab biosignal amplifier (gtec, Graz, Austria)Sampling Freq.: 256 Hz8 Active Electrodes (FC3, FC4, C3, C4, C5, C6, CP3, CP4)Motor-Imagery Training Common Spatial Patterns filter (CSP)Linear Discriminant Analysis (LDA)Acquired Data
Athanasios Vourvopoulos - BIH155
How we try to solve itAthanasios Vourvopoulos - BIH156
ClassifierBayesian Inference Layer (BIL) Adaptive Performance Engine (APE) (| ) BCIL or R
End EffectorVR, Prosthetics, etc
Finite State Machine (FSM)
Transition StatesAthanasios Vourvopoulos - BIH157
ClassifierBayesian Inference Layer (BIL) Adaptive Performance Engine (APE) (| ) BCIL or R
End EffectorVR, Prosthetics, etc
Finite State Machine (FSM)
S0S1S2S3S-1S-2S-3w3w2w1w1w2w3w-3w-2w-1w-1w-2w-3
Can performance be improved by means of the BCI-APE?Athanasios Vourvopoulos - BIH158
ClassifierBayesian Inference Layer (BIL) Adaptive Performance Engine (APE) (| ) BCI
Finite State Machine (FSM)
End EffectorVR, Prosthetics, etcL or R
20% increase
What are the tradeoffs?Athanasios Vourvopoulos - BIH159
ClassifierBayesian Inference Layer (BIL) Adaptive Performance Engine (APE) (| ) BCI
Finite State Machine (FSM)
End EffectorVR, Prosthetics, etcL or R
20% performance and 80% indecisions
Can APE adjust performance in real time?Athanasios Vourvopoulos - BIH1510
ClassifierBayesian Inference Layer (BIL) Adaptive Performance Engine (APE) (| ) BCIL or R
End EffectorVR, Prosthetics, etc
Finite State Machine (FSM)
S0S1S2S3S-1S-2S-3w3w2w1w1w2w3w-3w-2w-1w-1w-2w-3
We used a 3rd degree polynomial function to model how Ws change depending on performance
Can APE adjust performance in real time?Athanasios Vourvopoulos - BIH1511
ClassifierBayesian Inference Layer (BIL) Adaptive Performance Engine (APE) (| ) BCIL or R
End EffectorVR, Prosthetics, etc
Finite State Machine (FSM)
S0S1S2S3S-1S-2S-3w3w2w1w1w2w3w-3w-2w-1w-1w-2w-3Decisions taken at each state of the FSM have an associated performance level
Demo - APE in VR
S0S1S-1S2S3S-2S-3Athanasios Vourvopoulos - BIH1512Decision is taken on state: S-3/3
Existing MI-BCI classification approaches are very dissimilar in setup, algorithms, user experience, datasets, etc.
Its very difficult to assess if differences in performance arise from training, users, algorithms or setup
When comparing BCI-APE to previous approaches we observe a comparable performance with the best BCI classification algorithmsComparison with other StudiesAthanasios Vourvopoulos - BIH1513
BCI-APE was created from the need of ensuring satisfactory performances for non-expert and low-performing BCI usersBCI-APE provides a way to adapt performance accuracy on demand depending on the specific needs of usersBetter performing users will have less indecisions and response times will be faster than those low-performing BCI usersEnhance usability and improve the experience of BCI usersConclusionsAthanasios Vourvopoulos - BIH1514
New interaction paradigm need to be developed to embrace BCI-APE
A study carried out to assess its impact in users perceived performanceFuture StepsAthanasios Vourvopoulos - BIH1515
Thank You!http://neurorehabilitation.m-iti.org/
Athanasios Vourvopoulos - BIH1516