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Brain-computer Interface Based on Motor Imagery:

Brain-computer Interface Based on Motor Imagery: the Most Relevant Sources of Electrical Brain Activity. Alexander A. Frolov 1,2 , Dusan Husek 3 , Vaclav Snasel 1 , Pavel Bobrov 1,2 , Olesya Mokienko 2 , Jaroslav Tintera 4 , and Jan Rydlo 4.

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Brain-computer Interface Based on Motor Imagery:

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  1. Brain-computer Interface Based on Motor Imagery: the Most Relevant Sources of Electrical Brain Activity Alexander A. Frolov1,2, Dusan Husek3, Vaclav Snasel1, Pavel Bobrov1,2, Olesya Mokienko2, Jaroslav Tintera4, and Jan Rydlo4 VŠB Technical University of Ostrava, 17 listopadu 15/2172, 708 33 Ostrava, Czech Republic Institute for Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Butlerova 5a, Moscow, Russian Federation Institute of Computer Science, Academy of Sciences of the Czech Republic,Pod VodárenskouVeži 2, Prague 8, Czech Republic Institute for Clinical and Experimental Medicine, Videnska 1958/9,Praha, Czech Republic

  2. Aim of the work • Localizing the sources of electrical brain activity the most relevant for performance of motor imagery based BCI using individual head model. • Verifying the results of localization with clusters of fMRI activity.

  3. Procedure Eight subjects have been training to control BCI for 10 days (1 session a day). Session Block: 4 tasks are permuted randomly Right hand MI Foot MI Relaxation Left hand MI 3 blocks, no feedback 4 4 4 4 7 blocks, feedback is on 10 10 10 10 On the 10-th day fMRI session was carried out for each subject, with the same instructions presented Correct classifier guess (feedback) Relaxation Foot MI Left hand MI Right hand MI

  4. Extraction of patterns of EEG activity. Procedure Step1 Independent componentsX=Wξ Experimental day data, X ICA decomposition IC selectionusing cross-validation Step2 Step3 Components, relevant to the BCI performance Source locations Source localization using the weights of the optimal components Task-relevant activations Anatomic scans for head model fMRI and anatomic MR scans

  5. Extraction of patterns of EEG activity. Step 1. Independent Component Analysis +…+ ξn ξ1 × × = W1 Wn EEG Column of weights Wi is acontribution of thei-thindependent component into the signal at all channels Source2. Source1. ξare activities of the independent components in time Source3. Source n. Bell-Sejnowski algorithm was used

  6. Extraction of patterns of EEG activity. Step 2. Independent Component Selection Check all triples of independent components using Kohen`s Kappa, κ,obtained by cross-validation (7 blocks testing set, 3 blocks training set, 100 trials) Add a component to the previously obtained set so that κ is maximal Repeat until all components are selected Dependence of κ on the number of IC (Ncmp) Individual maximum (subject & session dependent): artifact elimination κ Optimal triple:the most relevant sources All ICs: equiv. toEEG channels used Ncmp

  7. Extraction of patterns of EEG activity. Step 2. The most relevant components These components appeared in optimal triples almost for each session Left hand MIRight hand MIFoot MIRelaxation mu-rhythm ERD in left hand area mu-rhythm ERD in right hand area Hz Hz mu-rhythm ERD in foot area supplementary motor area activity Hz Hz

  8. Localization of sources the most relevant to the BCI performance. Step 3.Finite element model Scalp, 0.35 Sm/m Bone (skull), 0.0132 Sm/m White matter, 0.14 Sm/m Gray matter, 0.33 Sm/m Cerebrospinal fluid, 1.79 Sm/m

  9. Localization of sources the most relevant to the BCI performance. Step 3.Results Subject 1 (Examples of potential distribution approximation) Left hand MI Right hand MI Experiment Approximation Experiment Approximation Residual variance average over all subjects was less than 1% Distance to the closest focus of fMRI activity averaged 9 mm

  10. Localization of sources the most relevant to the BCI performance. Step 3.Mu-rhythm ERD in hand areas

  11. Localization of sources the most relevant to the BCI performance. Step 3.Mu-rhythm ERD in foot area and SMA activity

  12. Conclusions and future work • Conclusions • The method allows for identification of sources of the brain electrical activity the mostrelevant to motor imagery based BCI performance • The relevant sources were localized at the bottom of the central sulcus, i.e. close to the hand representation areas, close to the foot representation area, and in supplementary motor cortex • Future research plans • Introduce anisotropy into the model • Create fast precise localization instrument for each subject using reciprocal approach. The instrument can be then used as a base for creation of source location-based BCI which idea and implementation has attracted researchers` attention recently.

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