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Signal processing techniques for fNIRS and application to Brain Computer Interfaces

French community for functional NIRS. Signal processing techniques for fNIRS and application to Brain Computer Interfaces Gautier Durantin , ISAE/CERCO. ISAE, Toulouse, 15.04.2014. Introduction.

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Signal processing techniques for fNIRS and application to Brain Computer Interfaces

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  1. French community for functional NIRS Signal processing techniques for fNIRS and application to Brain Computer Interfaces Gautier Durantin, ISAE/CERCO ISAE, Toulouse, 15.04.2014

  2. Introduction • EEG and fNIRStodayencompass the most active areas of Brain Computer Interfaces research(Min 2010) • fNIRSiscurrentlymainlyused as a complement of EEG (Takeuchi 2009, Fazli 2011) • Noise reduction techniques and signal improvement techniques are the nextstep to improve BCI performance (Mitsukura 2013, Izzetoglu 2010) (Bashashati 2007)

  3. Filtering for fNIRS – What do weneed to filter ? • Lowfrequency components :linear trends, measurementbias(Jang 2008) • High frequency components : physiological noise (cardiacfrequency), measurement noise (Huppert 2006) Rawsignal (x) FILTERING MODULE Filtered signal (y) • Use of filters to removethese components (e.g. linearfilters) Delay, stability, performance !

  4. MovingAverage Convergence Divergence Filter • We propose a specificlinearfilterused by economists(Utsugi 2007, Cui 2010) • The MACD (movingaverage convergence divergence), based on Exponentialmovingaverage (EMA) filters. Rawsignal (x) Exponentialmovingaverage (EMA) Filtered signal (y) • MACD isobtainedfromtwo EMA : one short (k small), and one long (k big) MACD Short-term EMA + - Rawsignal (x) Filtered signal (y) Long-term EMA

  5. MovingAverage Convergence Divergence Filter MACD (short = 6s ; long = 13s) • Economists use a signal line, obtainedfrom a short EMA (5s) of MACD data, to predict stable increases on the curve. (Appel 1999) • A MACD crossover of the signal line predicts a stable increase in the signal measured A MACD crossover of the signal line predicts a stable increase in the signal measured

  6. Real-time hemodynamicresponseonsetdetection • Controlledexperiment of digit sequencememorizationtask 4 8 5 9 1 x-x-x X 1 9 subjects 24 trials REST (6-9sec) STIMULATION ANSWER (8 sec) Increase in the signal MACD crossover Stimulus

  7. Towards a real-time BCI usingfNIRS • 4 control knobs : • Speed, Heading, Altitude, Vertical speed • Lowload(speed 200, heading 200, Alt. 2000…) • High load(speed 245, heading 315, altitude 8600…)

  8. Towards a real-time BCI usingfNIRS Resp. windows ATC msg mi Pilot in ISAE flight simulator SOA Signal filtering and synchronization (MACD) fNIRSoutput REST Real-time information on pilot’s mental state (Rest VS Task) Classification Process or TASK Data knowledge from phase L Air Traffic Control (simulated) Overallaccuracy : 98 % (std. Dev : 2,6%) Load detection

  9. Towards a real-time BCI usingfNIRS Resp. windows ATC msg mi Pilot in ISAE flight simulator SOA Signal filtering and synchronization (MACD) fNIRSoutput REST Real-time information on perceivedworkload Classification Process or TASK Data knowledge from phase L Air Traffic Control (simulated) Load detection

  10. Classification process • Use of differentfeatures(Tai & Chau 2009) • [HbO2], [Hhb], peakresponse, kurtosis, skewness on different time windows Signal processing (MACD) and feature extraction fNIRS output of 20 training trials Classifier design (LDA, SVM) TRAINING TESTING CLASSIFIER Real-time information on workload fNIRS output

  11. Towards a real-time BCI usingfNIRS : results 28 sessions • Overallaccuracyobtainedduringtesting phase : 79 % (std. dev : 12,8%) • 19 subjects out of 28 have more than 75% accuracy 20 training trials 20 testing trials

  12. Furtherimprovements in signal processing To improve signal processing and BCI accuracy, a solution wouldbe to adda priori information in processingmodels • Use of hemodynamicresponsemodelsfor temporal dynamic estimation of fNIRS. (Boynton 1996, Buxton 1997) • Use of Kalmanfilteringto includeestimation of temporal dynamics in signal processing(Abdelnour 2009, Gagnon 2011)

  13. Kalmanfiltering Participant NIRS Physiologicalprocessing model (HRF) Measurement model Stimulus NIRS signal Dynamical model of hemodynamicresponse and fNIRSmeasurement KALMAN FILTER fNIRSfiltered signal fNIRSraw data Confidence in the measures Confidence in the model Kalmanfiltering

  14. Kalmanfiltering : results • Tested offline on digit spanmemorizationtask data (9 subjects) withthreelevels of difficulty KALMAN FILTERING (effect size eta²=0,34) LINEAR FILTERING (effect size eta²=0,2) Kalmanfilteringis a promisingtool to improve signal useability Challenges remainconcerningKalmantuning and real-time implementation

  15. Conclusion • Signal processingis a key steptowards efficient Brain Computer Interface usingfNIRS. • Linearfilteringbrings good results, but improvementscanbe made to improve the accuracy of BCI designedwiththis type of filters. • Kalmanfiltering or adaptive filtering are the best opportunities to improve signal useability.

  16. Thankyou for your attention

  17. Digit spantask • 6 levels of difficulty • 4 trials for eachlevel of difficulty

  18. Kalmanmodeling Physiologicalresponse model Measurement model Kalmanfilter

  19. Possible improvement of Kalmanmodeling Physiologicalresponse model Measurement model Kalmanfilter MACD filter for onsetprediction

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