Collins, Ronan

Leamy, Darren J.

Ward, Tomas E.

English

MURAL - Maynooth University Research Archive Library

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based taskDarren Leamy* - National University of Ireland Maynooth, IrelandRónán Collins - The Adelaide and Meath Hospital, Dublin, IrelandTomás Ward - National University of Ireland Maynooth, IrelandHCI International 2011 Brain-Computer Interfacing Different recording modalities in use Our specific BCI goals Aims of this workCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task Electroencephalograhy (EEG)◦ Records electrical activity◦ Due to firing neurons◦ Good temporal resolution◦ Poor spatial resolution Functional Near-Infrared Spectroscopy (fNIRS)◦ Records haemodynamic activity◦ Due to oxygenated haemoglobin (HbO) and deoxygenated haemoglobin (Hb)◦ Poor temporal resolution◦ Good spatial resolutionCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task Neurovascular coupling◦ The relationship between electrical and hemodynamic activity in the brainCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task Inexpensive Portable Wearable RobustCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based taskEEG:BioSemi Active TwoNIRS:TechEn CW6 7 EEG channels 7 fNIRS channels Centred on C3 or C4Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task Two subjects Imagined Movement task Two alternating on-screen instructions 10 second epochs – 40 epochs per subjectCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task EEG◦ Event Related (de)Synchronisation (ERD/ERS)◦ Isolate reference and response EEG data◦ Find frequency range of Mu and Beta activity◦ Measure spectral power change fNIRS◦ Raw intensity to Optical Density◦ Optical Density to HbO and Hb◦ Measure amplitude changeCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task EEG and fNIRS feature extraction 2 dimensional feature space Classification◦ Linear Discriminate Analysis (LDA)◦ Leave-one-out Cross Validation (LOOCV)2 dimensional EEG features+2 dimensional fNIRS features=4 dimensional EEG/fNIRS features+Subject A Subject BChannel fNIRS EEG Dual fNIRS EEG Dual1 59% 51% 64% 64% 46% 62%2 56% 59% 67% 51% 54% 59%3 56% 54% 64% 61% 41% 56%4 69% 67% 72% 64% 59% 67%5 61% 51% 72% 41% 36% 46%6 56% 77% 64% 74% 59% 69%7 56% 59% 62% 15% 43% 49%Average 59% 60% 66% 53% 48% 58%Classification Accuracy Indications of improved classification accuracy Improved BCI performance Worthy of further multi-modal research Future workCombining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based taskThank you for listeningAny questions?With thanks to Science Foundation Ireland: Research Frontiers ProgramContact:Darren Leamy, NUI Maynoothdleamy@eeng.nuim.ie

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task

http://mural.maynoothuniversity.ie/4182/1/dleamy_hcii11_presentation_01.pdf

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task

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